Ming potential patterns from patient data are usually treated as a high‐dimensional data clustering problem. Evolutionary multiobjective clustering algorithms with feature selection (FS) are widely used to handle this problem. Among the existing algorithms, FS can be performed either before or during the clustering process. However, research on performing FS at both stages (hybrid FS), which can yield robust and credible clustering results, is still in its infancy. This paper introduces an improved high‐dimensional patient data clustering algorithm with hybrid FS called variable dimensional multiobjective lifetime constrained quantum PSO with reinforcement learning (VLQPSOR). VLQPSOR consists of two main independent stages. In the first stage, a dimensionality reduction ensemble strategy is developed before clustering to reduce the patient dataset’s dimensionality, resulting in subdatasets of varying dimensions. In the second stage, an improved multiobjective QPSO clustering algorithm is proposed to simultaneously conduct dimensionality reduction and clustering. To accomplish this, several strategies are employed. Firstly, the variable dimensional lifetime constrained particle learning strategy, the continuous‐to‐binary encoding transformation strategy, and multiple external archives elite learning strategy are introduced to further reduce the dimensionality of the subdatasets and mitigate the risk of QPSO getting trapped in local optima. Secondly, an improved reinforcement learning–based clustering method selection strategy is proposed to adaptively select the optimal classical clustering algorithm. Experimental results demonstrate that VLQPSOR outperforms five representative comparative algorithms across four validity indexes and clustering partitions for most patient datasets. Ablation experiments confirm the effectiveness of the proposed strategies in enhancing the performance of QPSO.

With the increase in aircraft maintenance orders and heterogeneity of aircraft irregular shapes, outsourced aircraft maintenance companies urgently need a more efficient and tailored intelligent aircraft parking allocation method. However, existing methods could be improved in lightweight handling of non-overlapping constraints and effective use of problem-specific heuristics. To tackle these challenges and achieve a more rational allocation of parking stands, a bi-objective optimization model involving rotation angles is firstly constructed to maximize hangar utilization and safety margin, and is decomposed into two single-objective optimization problems via a lexicographic method. To efficiently solve this model, problem characteristics of “irregular contours matching” are analyzed. Furthermore, a series of mechanisms that fully utilize the problem characteristics are designed, thus integrating a two-stage metaheuristic framework based on irregular contours matching. These mechanisms include an aircraft parking strategy based on geometric fit for rationally locating aircraft parking positions and mitigating the dimensional explosion problem, a metaheuristic optimizer with similar insertion neighborhood operation for enhancing the hangar utilization, and a fast safety margin optimization algorithm based on binary searching iterator. Experimental studies conducted on 18 real-world instances show that the proposed framework outperforms several state-of-the-art algorithms, as well as the dynamic search algorithm automatically selected by the CPLEX optimizer. Note to Practitioners—This paper investigates an aircraft parking stand allocation problem that originated in outsourced aircraft maintenance companies. The goal of the problem is to maximize the hangar utilization and safety margin. Existing aircraft parking stand allocation methods ignore the lightweight handling of non-overlapping constraints, optimal configuration of rotation angles and safety margins, and effective utilization of problem-specific heuristics. Thus the allocation efficiency could be further improved when coping with large-scale order requirements. This paper constructs a mathematical optimization model with limited rotation angles, and introduces a concise geometric tool to address aircraft collision problem. Furthermore, a two-stage metaheuristic framework based on problem characteristics is proposed to solve the model efficiently. The superiority of the present method was verified on 18 real-instances with various hangar sizes and maintained aircraft. It is believed that this method effectively improves maintenance resource utilization, reducing labor and maintenance costs of outsourced aircraft maintenance companies.

The potential intelligence behind advanced machining systems (AMSs) offers positive contributions toward process improvement. Imitation learning (IL) offers an appealing approach to accessing this intelligence by observing demonstrations from skilled technologists. However, existing IL algorithms that implement single policy strategies have yet to consider realistic scenarios for complex AMS tasks, where the available demonstrations may have come from various experts. Moreover, most IL assumes that the expert's policy is optimal, preventing the learning from fulfilling the previously ignored green missions. This article introduces a novel three-phase policy search algorithm based on IL, enabling the learning of heterogeneous expert policies while balancing energy preferences. The first phase equips the agent with machining basics through upper-level policy learning, generating an imitation policy distribution with various decision-making principles. The second phase enhances energy conservation capabilities by employing Pareto-improvement learning and fine-tuning the agent's policies to a Pareto-policy manifold. The third phase produces outcomes and amplifies the efficacy of human feedback by utilizing ensemble policies. The experimental results indicate that the proposed method outperforms meta-heuristics, exhibiting superior solution quality and faster computation times compared to four diverse baseline methods, each with diverse samples.

Abstract Background Crowdfunding is increasingly favoured by entrepreneurs for online financing. Predicting crowdfunding success can provide valuable guidance for stakeholders. It is a new attempt to evaluate the relative performance of different machine learning algorithms for crowdfunding prediction. Objectives This study aims to identify the key factors of crowdfunding, and find the different performance and usage of machine learning algorithms for crowdfunding prediction. Method We crawled data from MoDian.com, a Chinese crowdfunding platform, and predicted the crowdfunding performance using four machine learning algorithms, which is a new exploration in this area. Most of the existing literature focuses on empirical analysis. This work solves the problem of predicting crowdfunding performance using a dataset with a minimal number of highly contributive features, which has higher accuracy compared to the regression analysis. Results The experiment results show that feature‐selection‐based machine learning models are effective and beneficial in crowdfunding prediction. Conclusion Feature selection can significantly improve the prediction performance of the machine learning models. KNN achieved the best prediction results with five features: number of backers, target amount, number of project likes, number of project comments, and sponsor fans. The prediction accuracy was improved by 16%, the precision was improved by 13.23%, the recall was improved by 22.66%, the F‐score was improved by 18.48%, and the AUC was improved by 14.9%.

To address the challenge of parameter adjustment in complex environments, this paper introduces a transfer learning-based parameter control framework via deep reinforcement learning for multiobjective evolutionary algorithms (MOEAs). To avoid the requirement for accurate Pareto front information, this framework is proposed with comprehensive global-state information, including basic problem features, the relative position of individuals, the distribution of fitness value, and the grid-IGD. Building on this framework, four reinforced multiobjective evolutionary algorithms (r-MOEAs) are proposed and tested on four DTLZ benchmarks and eight WFG benchmarks. The results of the comparative analyses reveal that compared with the original MOEAs, the four r-MOEAs exhibit faster convergence and stronger robustness. It is also confirmed that our proposed parameter control framework has the capability to learn knowledge from different experiences and improve the performance of MOEAs.

Abstract The information transfer mechanism within the population is an essential factor for population‐based multiobjective optimization algorithms. An efficient leader selection strategy can effectively help the population to approach the true Pareto front. However, traditional population‐based multiobjective optimization algorithms are restricted to a single global leader and cannot transfer information efficiently. To overcome those limitations, in this paper, a multiobjective bacterial colony optimization with dynamic multi‐leader co‐evolution (MBCO/DML) is proposed, and a novel information transfer mechanism is developed within the group for adaptive evolution. Specifically, to enhance convergence and diversity, a multi‐leaders learning mechanism is designed based on a dynamically evolving elite archive via direction‐based hierarchical clustering. Finally, adaptive bacterial elimination is proposed to enable bacteria to escape from the local Pareto front according to convergence status. The results of numerical experiments show the superiority of the proposed algorithm in comparison with related population‐based multiobjective optimization algorithms on 24 frequently used benchmarks. This paper demonstrates the effectiveness of our dynamic leader selection in information transfer for improving both convergence and diversity to solve multiobjective optimization problems, which plays a significant role in information transfer of population evolution. Furthermore, we confirm the validity of the co‐evolution framework to the bacterial‐based optimization algorithm, greatly enhancing the searching capability for bacterial colony.

Abstract Efficient management of aircraft and crew recovery system is crucial for cost savings and improving the satisfaction, which are related to the airline's reputation. However, most existing work considers only one objective of minimizing costs or maximizing satisfaction. In this study, we propose a new integrated multi‐objective recovery system that takes both cost and satisfaction into account simultaneously. To better capture crew satisfaction in the event of airport closure, a bidding mechanism for early off‐duty task is designed. To overcome the experience‐dependent and labour‐consuming problems associated with current manual or mathematical recoveries, we develop an intelligent optimizer based on multi‐swarm and MOPSO frameworks, termed adaptive seeking and tracking multi‐objective particle swarm optimization algorithm (ASTMOPSO). Specifically, during the evolutionary process, the sub‐swarm size undergoes adaptive internal transfer while executing more efficient evolutionary strategies to approach the global Pareto front. Additionally, five ad‐hoc repair procedures are designed to ensure feasibility for our aircraft and crew recovery system. The ASTMOPSO is applied to real‐world instances from Shenzhen Airlines with different sizes. Experimental results demonstrate the statistical superiority of our method over other popular peer algorithms. And the infeasible solution repair procedures significantly improve the feasibility rate by at least 40%, particularly for large‐scale instances.

Abstract Outsourcing maintenance service providers are vital to guarantee safe operation in airline industry. To reduce the workload and avoid incompatible arrangement schemes in traditional manual arrangement, this article constructs an intelligent system with a novelly designed model and algorithm for membership‐based aircraft parking stand allocation problem. This problem arises from outsourcing maintenance service providers. They need to first serve membership orders, while guaranteeing punctual delivery of other orders. In particular, mutual collision should be strictly avoided between aircrafts. To solve this problem, first, a mathematical model is constructed to optimize timetable and aircraft parking stand allocation scheme. Second, to quickly obtain feasible scheduling scheme, three kinds of mechanisms, including information guidance mechanism, boundary arrangement mechanisms and local optimal adjustment mechanism, are novelly proposed. Moreover, event‐based time–space separated heuristic algorithm is subtly designed based on time–space separation characteristics. In addition, coding schemes are proposed through problem analysis. Finally, three cases with different scales are utilized and six comparison algorithms are selected to illustrate the superiority of our proposed algorithm.

Abstract Carpooling is a sustainable and ecologically acceptable transportation mode. Individuals commonly engage in coordination and negotiation processes to find matching partners and typically modify their schedules to enable cooperation. Mutual cooperation between carpooling individuals plays an important role in executing trips. Through cooperation, participants can achieve challenging agreements effectively in a repetitive manner. This paper presents a negotiation mechanism that can match individuals for carpooling using organization and agent-based concepts. It describes a matching model and a carpooling social network. It studies several aspects of multi-zonal individual behavior to identify groups of carpooling candidates. The carpooling social network is simulated on an ongoing basis for each of the following carpooling activities: interaction, negotiation, and trip execution. The interaction process enables communication between individuals within carpooling social groups in order to activate the negotiation process. During the negotiation process, participants typically modify their schedules to support cooperation by considering their personal preferences and constraints. Negotiation leads to matching of individuals based on trip start times, driver selection, detour duration, and carpool group pickup and dropoff sequences. Trip start times are established on travel, social, financial, and schedule-related factors. The carpoolers’ pickup and dropoff sequences that are feasible for an optimal carpool group are projected using specific scoring methods. Carpooling community candidates are recognized via outcomes projected using the FEATHERS activity–based model. The framework is implemented through the Janus multi-agent system.

Metaheuristic algorithms have been investigated intensively to address highly complex combinatorial optimization problems. However, most metaheuristic algorithms have been designed manually by researchers of different expertise without a consistent framework. This article proposes a general search framework (GSF) to formulate in a unified way a range of different metaheuristics. With generic algorithmic components, including selection heuristics and evolution operators, the unified GSF aims to serve as the basis of analyzing algorithmic components for automated algorithm design. With the established new GSF, two reinforcement learning (RL)-based methods, deep$Q$-network based and proximal policy optimization-based methods, have been developed to automatically design a new general population-based algorithm. The proposed RL-based methods are able to intelligently select and combine appropriate algorithmic components during different stages of the optimization process. The effectiveness and generalization of the proposed RL-based methods are validated comprehensively across different benchmark instances of the capacitated vehicle routing problem with time windows. This study contributes to making a key step toward automated algorithm design with a general framework supporting fundamental analysis by effective machine learning.

With the growing demands on green short life-cycle products, advanced energy-aware process planning (AEPP) becomes critical. A major limitation of the existing methods is the poor resistance to the perturbations encountered in advanced machining systems. Therefore, a graph convolutional reinforcement learning (GCRL) method is proposed to overcome such limitations. In this method, a graph convolutional policy network is trained to rapidly adapt the learned commonalities to specific tasks. Unlike algorithms that fix decision variables before optimization, this method employs graph generation to represent AEPP while taking into consideration the flexibilities of operations, machines, and cutting tools. The problem is reformulated as a novel Markov decision process (MDP) to describe the dynamic generation procedure of process plans. A graph convolutional network (GCN) is concurrently used to perform graph embedding to compress the topology of input graphs. Additionally, reinforcement learning (RL) is used to achieve robust and intuitive learning for process planning. To improve the adaption performance of the proposed GCRL, a two-phase multitask training strategy is adopted. Learning efficiency is improved because agents can incorporate both intertask similarities and task-specific rules. A comprehensive case study, including energy characteristics and algorithm performance analyses, is also performed to validate the developed method.

Abstract Clustering divides objects into groups based on similarity. However, traditional clustering approaches are plagued by their difficulty in dealing with data with complex structure and high dimensionality, as well as their inability in solving multi‐objective data clustering problems. To address these issues, an evolutionary state‐based novel multi‐objective periodic bacterial foraging optimization algorithm (ES‐NMPBFO) is proposed in this article. The algorithm is designed to alleviate the high‐computing complexity of the standard bacterial foraging optimization (BFO) algorithm by introducing periodic BFO. Moreover, two learning strategies, global best individual (gbest) and personal historical best individual (pbest), are used in the chemotaxis operation to enhance the convergence speed and guide the bacteria to the optimum position. Two elimination‐dispersal operations are also proposed to prevent falling into local optima and improve the diversity of solutions. The proposed algorithm is compared with five other algorithms on six validity indexes in two data clustering cases comprising nine general benchmark datasets and four credit risk assessment datasets. The experimental results suggest that the proposed algorithm significantly outperforms the competing approaches. To further examine the effectiveness of the proposed strategies, two variants of ES‐NMPBFO were designed, and all three forms of ES‐NMPBFO were tested. The experimental results show that all of the proposed strategies are conducive to the improvement of solution quality, diversity and convergence.

Recommender systems assist users to make decisions among a huge volume of options. Accuracy-oriented recommender systems focus on the prediction power of algorithms and neglect that users may appreciate diverse and novel recommendations in real-world scenarios. Thus, this paper proposed a multicriteria recommendation model that can optimize the recommendation accuracy, diversity, novelty, and individual tendency simultaneously. Additionally, a new multiobjective bacterial foraging optimization method is proposed to improve its searching capability and the performance of recommendation model. The proposed optimization-based multicriteria recommendation algorithm is compared with existing methods on both benchmark functions and real-world data sets. The results demonstrate that the proposed algorithm is superior to other recommendation algorithms in most cases. This study provides insights in recommendation system design and draws scholarly attention to the optimization-based recommendation strategy.

This study proposes a general multiple parameter control framework by leveraging the ability of a reinforcement learning system to learn empirical knowledge for evolutionary computation. We design a feedback evaluation mechanism to define the rewards offered to agents, using which they can learn to choose appropriate parameters in formulated action sets. Moreover, a learning strategy is proposed to utilize the parameter selection-related knowledge that is gained during training episodes. Three famous evolutionary computation (EC) methods (i.e., particle swarm optimization, artificial bee colony, and differential evolution) are selected as the baseline algorithms and applied to the proposed framework. The aforementioned redesigned algorithms are tested on 15 common benchmark functions, as well as the CEC2017 benchmarks. In addition, the robustness of the algorithms is demonstrated through parameter sensitivity analysis. The results of the comparative analysis reveal that the three improved algorithms exhibit a faster overall convergence and higher accuracy than their state-of-the-art variants. It is also confirmed that our proposed framework has the capability to improve the performance of EC approach.

This study focuses on the challenges of aviation maintenance technician (AMT) scheduling and constructs a model based on personnel satisfaction and the parallel execution of aircraft maintenance tasks. To obtain the scheduling scheme from the constructed NP‐hard model, an interactive multi swarm bacterial foraging optimization (IMSBFO) algorithm is proposed using multi‐swarm coevolu tion, structural recombination, and three informa tion interactive mechanisms among individuals. Moreover, considering the distributed feature of the AMT scheduling problem, a specific mechanism is designed to convert continuous solution to a binary AMT scheduling scheme. Finally, a series of com parative experiments highlight the efficiency and superiority of our proposed IMSBFO algorithm, and the optimal scheduling scheme owns the delicate balance between the work and rest time.

The circulation and convergence of water in the hydrologic cycle process inspired us to design a new optimization algorithm, the Hydrologic Cycle Optimization (HCO) algorithm. In this study, a comprehensive demonstration of the HCO was presented. First, a simplified model of the hydrological cycle phenomenon was established. Then, the framework of HCO and its operators were discussed and verified in detail. Several experiments were done to test the optimization ability of the HCO algorithm. In the first experiment, the parameter settings were tested, and an adaptive version of the algorithm was proposed. Then the HCO was tested on 20 numeric optimization benchmark functions and eight data clustering sets, respectively, and compared with other algorithms. The experimental results showed that the HCO is superior to the compared algorithms, indicating that it is a competitive approach for numerical and engineering optimization problems.

Integrated berth and quay crane allocation problem (BQCAP) are two essential seaside operational problems in container terminal scheduling. Most existing works consider only one objective on operation and partition of quay into berths of the same lengths. In this study, BQCAP is modeled in a multiobjective setting that aims to minimize total equipment used and overall operational time and the quay is partitioned into berths of different lengths, to make the model practical in the real-world and complex quay layout setting. To solve the new BQCAP efficiently, a multiobjective hydrologic cycle optimization algorithm is devised considering problem characteristics and historical Pareto-optimal solutions. Specifically, the quay crane of the large vessel in all Pareto-optimal solutions is rearranged to increase the chance of finding a good solution. Besides, worse solutions are probabilistic retained to maintain diversity. The proposed algorithm is applied to a real-world terminal scheduling problem with different sizes from a container terminal company. Experimental results show that our algorithm generally outperforms the other well-known peer algorithms and its variants on solving BQCAP, especially in finding the Pareto-optimal solutions range.

This article aims to develop a novel hybrid multi-attribute group decision-making approach under interval-valued intuitionistic fuzzy sets (IVIFS) by integrating variable weight, correlation coefficient, and technique for order performance by similarity to an ideal solution (TOPSIS). First, experts give their evaluation in IVIFS, and then the weighting evaluation matrix is computed based on interval-valued intuitionistic fuzzy weighted averaging operator with the subjective attribute weights given in advance. Second, a simple and useful weighting approach on the basis of correlation coefficient is put forward to obtain the experts weights. Third, we treat the attribute weights as a varying vector, and then propose a variable weighting approach for its acquisition. Fourth, an individual decision can be converted to an alternative decision by considering the experts and attributes weights together. At last, the integrated assessment value of each alternative is computed by TOPSIS, and then the most appropriate alternative is chosen. Two illustrative examples dealt with the problem by the method presented in this article demonstrate the usefulness of this approach, compared with those by the other methods.

Bacterial foraging optimization (BFO) has been exploited for function optimization, owing to its innovative ideas gleaned from the microbiological system. This paper first discusses its three crucial limitations: high computational cost, difficulty in parameter settings, and premature convergence. To alleviate the above problems, simplified BFO with quorum sensing (QS) is proposed. First, a novel computational framework is provided to reduce the computational complexity, leading to a simplified version. Second, the concept of “QS,” bacterial reciprocal behavior, is integrated into the simplified version by utilizing a new position updating equation coupled with a dynamic communication topology. Each bacterium adjusts its search trajectory based on both biased random walk and promising search directions provided by its communicatees. The communicatees are selected via a dynamic communication topology, where a rank-based communication strategy and two information mutation schemes are used for global exploration of the search space. Finally, a parameter automation strategy is introduced to promote the exploitation of promising regions. Further, the effectiveness and efficiency of the proposed algorithm are empirically confirmed on 30 benchmark functions, by comparing it with the four variants of BFO and four other advanced algorithms.

In the customer segmentation problem, a large number of features are manually designed and used to comprehensively describe the customer instances. However, some of these features are irrelevant, redundant, and noisy, which are not necessary and effective for customer segmentation. Feature selection is an important data preprocessing method by selecting important features from the original feature set. Particularly, feature selection in customer segmentation is a multiobjective problem that aims to minimize the feature number and maximize the classification performance. This paper proposes a multiobjective feature-selection method based on a meta-heuristic algorithm—hydrological cycling optimization (HCO)—to solve customer segmentation. The proposed method is able to automatically evolve a set of non-dominated solutions that select small numbers of features and achieve high classification accuracy. To this end, three strategies based on the global flow operator, possibility-based acceptance criteria, and density-based evaporation and precipitation are proposed to improve the global search ability and the solution diversity of the proposed approach. The performance of the proposed approach is examined on three customer-segmentation datasets and compared with original multiobjective HCO and six well-known evolutionary multiobjective algorithms. The results confirm the superiority of the proposed approach in solving multiobjective customer-segmentation problems by achieving higher calculation stability, search diversity, and solution quality compared with the other competing methods.

We propose a reverse chemotaxis strategy to guide bacterial individuals to quickly converge to potential areas by using local fitness information efficiently. When bacterial individuals tumble or swim to worthless areas (i.e., poor fitness), the proposed strategy reverses and expands the current search directions in hopes of finding other effective directions to escape the current dilemma. We then use random noise generated by a Gaussian distribution to perturb these reversed directions to prevent bacterial individuals from oscillating in original directions. Besides, we also propose a structural simplification strategy to greatly simplify the optimization framework of the standard bacterial foraging optimization (BFO) by integrating the elimination-dispersal operation into the reproduction operation. In other words, there are two ways to replace unhealthy bacterial individuals in our proposed strategy, one is copied by healthy bacterial individuals and the other is generated by the elimination-dispersal operation. We also use Gaussian mutations to perturb these offspring individuals that exactly replicate their parents to increase the diversity of the population. Finally, the three-level nested BFO optimization framework can be reduced to a single-level loop. To evaluate the performance of our proposal, we run (the standard BFO + two proposed strategies) and the standard BFO on 28 benchmark functions from CEC 2013 test suite, and each function is run 30 times independently on three different dimensions. The experimental results confirmed that our proposed strategies can speed up the BFO search and jump out of local areas effectively.

Decomposition-based optimizers have shown very promising computational and convergence performance on many large-scale real-parameter optimization problems. Among them, a class of recently proposed cooperative coevolutionary algorithms (CCEAs) and a type of conventional block coordinate descent algorithms (BCDAs) are arguably the two most representative frameworks applied to the minimization of non-differentiable and differentiable objective function, respectively. This paper explores the connections between CCEAs and BCDAs, which can help gain deeper understandings of CCEAs. First, we propose a unified analytical framework for both CCEAs and BCDAs to capture the common game-theoretic nature by combining their respective theoretical advances. Second, many real-world objective functions are non-additively separable, where all decision variables interact with each other in a direct or indirect fashion. However, most of the state-of-the-art decomposition strategies for CCEAs can only capture the simple additive separability and cannot recognize the non-additive separability, but which has been widely studied in the BCDAs context. The performance of CCEAs on such functions is yet to be fully understood since intuitively CCEAs seem to be not suitable for them. We use the proposed framework to confirm and extend CCEAs' applicability to a special class of non-additively separable functions. Finally, based on the proposed framework, we provide two practical suggestions as well as a suite of new test functions to help design practically better CCEAs for large-scale optimization.

Building the work timetables for staff in healthcare institutions is known to be a highly constrained and NP-hard problem. In this research, a mathematical programming model, maximizing nurses' preference for work shifts and rest days while minimizing hospital operating costs, is proposed to solve the nurse scheduling problem (NSP) optimally. Then, we apply a new optimization algorithm-HCOMA, HCO based memetic algorithm, combining entropy-based decision-making mechanism and local search, to heuristically solve the NSP. In the global search, the entropy is calculated to assess population diversity following by every specified iteration. By analyzing the change of diversity, the population can identify the stagnation of search and perform local search at the best time. In summary, the local search includes three core parts: Meta-Lamarckian learning strategy, cooling schedule and Metropolis Criterion. Three neighborhood structures are utilized to exchange or reset the nurse's shifts, expanding the feasible solution area of the search and generating high-quality solutions. The Meta-Lamarckian learning strategy is used to automatically choose the best search structure based on their performance. The performance of HCOMA was tested with sufficient experimentations. The test problems were generated based on the actual situation of a hospital, including an instance and 30 random problems. The results indicate that the proposed algorithm was superior to the standard HCO and three well-known evolutionary algorithms in solution quality and convergence rate.

Capturing specific interrelationship among input arguments has great importance in the process of aggregation as they may change the aggregation result significantly, which can lead viable changes in the overall decision outcome. In this study, we attempt to aggregate a set of inputs with certain heterogeneous interrelationship pattern among them. To do this, we introduce a new aggregation operator, which we call the extended geometric Bonferroni mean. We investigate its properties and develop an algorithm to learn its associated parameters based on decision maker's perceived view toward the aggregation process. Moreover, to learn such heterogeneous relationship among the inputs from the data set, we provide a learning algorithm. Examples are given to illustrate the realization of algorithm and to show certain advantages over the existing aggregation operators.

This paper presents a Coevolutionary Structure-Redesigned-Based Bacteria Foraging Optimization (CSRBFO) based on the natural phenomenon that most living creatures tend to cooperate with each other so as to fulfill tasks more effectively. Aiming at lowering computational complexity while maintaining the critical search capability of standard bacterial foraging optimization (BFO), we employ a general loop to replace the nested loop and eliminate the reproduction step of BFO. Hence, the proposed CSRBFO only consists of two main steps: (1) chemotaxis and (2) elimination & dispersal. A coevolutionary strategy by which all bacteria can learn from each other and search for optima cooperatively is incorporated into the chemotactic step to accelerate convergence and facilitate accurate search. In the elimination & dispersal step, the three-stage evolutionary strategy with different learning methods for maintaining diversity is studied. An evaluation of the convergence status is then added to determine whether bacteria should move on to the next stage or not. The combination of coevolutionary strategy and convergence status evaluation is expected to balance exploration and exploitation. Experimental results comparing seven well-known heuristic algorithms on 24 benchmark functions demonstrate that the proposed CSRBFO outperforms the comparison algorithms significantly in most of the cases.

Inspired by the ideas from the mutual cooperation of symbiosis in natural ecosystem, this paper proposes a new variant of PSO, named Symbiosis-based Alternative Learning Multi-swarm Particle Swarm Optimization (SALMPSO). A learning probability to select one exemplar out of the center positions, the local best position, and the historical best position including the experience of internal and external multiple swarms, is used to keep the diversity of the population. Two different levels of social interaction within and between multiple swarms are proposed. In the search process, particles not only exchange social experience with others that are from their own sub-swarms, but also are influenced by the experience of particles from other fellow sub-swarms. According to the different exemplars and learning strategy, this model is instantiated as four variants of SALMPSO and a set of 15 test functions are conducted to compare with some variants of PSO including 10, 30 and 50 dimensions, respectively. Experimental results demonstrate that the alternative learning strategy in each SALMPSO version can exhibit better performance in terms of the convergence speed and optimal values on most multimodal functions in our simulation.

Feature selection is a desired process when learning from high-dimensional data. However, it is seldom considered in Genetic Programming (GP) for high-dimensional symbolic regression. This work aims to develop a new method, Genetic Programming with Feature Selection (GPWFS), to improve the generalisation ability of GP for symbolic regression. GPWFS is a two-stage method. The main task of the first stage is to select important/informative features from fittest individuals, and the second stage uses a set of selected features, which is a subset of original features, for regression. To investigate the learning/optimisation performance and generalisation capability of GPWFS, a set of experiments using standard GP as a baseline for comparison have been conducted on six real-world high-dimensional symbolic regression datasets. The experimental results show that GPWFS can have better performance both on the training sets and the test sets on most cases. Further analysis on the solution size, the number of distinguished features and total number of used features in the evolved models shows that using GPWFS can induce more compact models with better interpretability and lower computational costs than standard GP.

In order to improve the performance of original artificial bee colony (ABC) algorithm for global optimization problems in terms of solution accuracy and convergence speed, a superior tracking artificial bee colony (STABC) is presented in this paper. In STABC, the updating mechanism for bees is transformed from one-dimension-wise to all-dimension-wise. In addition, this strategy enables bees always to track the superior individuals in population. Experimental comparisons are conducted on twelve benchmark functions with various properties. Compared with the involved algorithms, experiment results demonstrate the remarkable improvement of the proposed algorithm for global optimization problems.

Feature selection is an important preprocessing technique for data analysis and data mining. One of main challenge for feature selection is to overcome the curse of dimensionality. Bacterial algorithms, like Bacterial Foraging Optimization (BFO), have been well-exploited as the metaheuristics for addressing the optimization problems. In this paper, an extended bacterial algorithm named as Bacterial-Inspired Feature Selection Algorithm (BIFS) is proposed. In BIFS, the searching process of bacteria consists of two main mechanisms: interactive swimming (or running) strategy used in Bacterial Colony Optimization (BCO), and random tumbling strategy embedded in Bacterial Foraging Optimization (BFO). The rule controlled foraging mode in BCO has been used in BIFS to overcome the high computational cost problem in most BFOs. Meanwhile, the `roulette wheel weighting' strategy is employed to weight the influence of features on the fitness functions and evaluate the distribution of the features within the large search space. Experiments on six benchmark datasets show that the proposed algorithm (i.e. BIFS) achieves higher classification accuracy rate in comparison to the four bacterial based algorithms and other three evolutionary algorithms. Furthermore, an additional real application of the proposed bacterial-inspired feature selection algorithm for fault diagnosis of complex structures in engineering has been developed. The results show that the proposed bacterial-inspired algorithm is capable of selecting the most sensitive sensors to detect and isolate the fault of complex structures.

Knapsack problem is famous NP-complete problem where one has to maximize the benefit of objects in a knapsack without exceeding its capacity. In this paper, a binary bacterial foraging optimization (BBFO) is proposed to find solutions of 0/1 knapsack problems. The original BFO chemotaxis equation is modified to operate in discrete space by using a mapping function, where some new variables and parameter, i.e., binary matrix y, logistic transformation S, and limiting transformation L is built to transform the bacterial position to a binary matrix. By using this schema, the proposed BBFO model can also be easily applied in other discrete problem solving. To further validate the efficiency of the BFO-based approach, an improved version BFO named BFO with linear decreasing chemotaxis step (BFO-LDC) is used to evaluate on six different instances. Comparisons with particle swarm optimization (PSO) and original BFO are presented and discussed.

The Integrated Yard Truck Scheduling and Storage Allocation Problem (YTS-SAP) is one of the major optimization problems in container port which minimizes the total delay for all containers. To deal with this NP-hard scheduling problem, standard particle swarm optimization (SPSO) and a local version PSO (LPSO) are developed to obtain the optimal solutions. In addition, a simple and effective `problem mapping' mechanism is used to convert particle position vector into scheduling solution. To evaluate the performance of the proposed approaches, experiments are conducted on different scale instances to compare the results obtained by GA. The experimental studies show that PSOs outperform GA in terms of computation time and solution quality.

BACKGROUND: Recent microbiologic studies have shown that quorum sensing mechanisms, which serve as one of the fundamental requirements for bacterial survival, exist widely in bacterial intra- and inter-species cell-cell communication. Many simulation models, inspired by the social behavior of natural organisms, are presented to provide new approaches for solving realistic optimization problems. Most of these simulation models follow population-based modelling approaches, where all the individuals are updated according to the same rules. Therefore, it is difficult to maintain the diversity of the population. RESULTS: In this paper, we present a computational model termed LCM-QS, which simulates the bacterial quorum-sensing (QS) mechanism using an individual-based modelling approach under the framework of Agent-Environment-Rule (AER) scheme, i.e. bacterial lifecycle model (LCM). LCM-QS model can be classified into three main sub-models: chemotaxis with QS sub-model, reproduction and elimination sub-model and migration sub-model. The proposed model is used to not only imitate the bacterial evolution process at the single-cell level, but also concentrate on the study of bacterial macroscopic behaviour. Comparative experiments under four different scenarios have been conducted in an artificial 3-D environment with nutrients and noxious distribution. Detailed study on bacterial chemotatic processes with quorum sensing and without quorum sensing are compared. By using quorum sensing mechanisms, artificial bacteria working together can find the nutrient concentration (or global optimum) quickly in the artificial environment. CONCLUSIONS: Biomimicry of quorum sensing mechanisms using the lifecycle model allows the artificial bacteria endowed with the communication abilities, which are essential to obtain more valuable information to guide their search cooperatively towards the preferred nutrient concentrations. It can also provide an inspiration for designing new swarm intelligence optimization algorithms, which can be used for solving the real-world problems.

In this paper, an improved Differential Evolution (DE) with a self-adaptive strategy to determine the control parameters is proposed to solve constrained real-parameter optimization, combined with the dynamic constraint-handling mechanism. It is implemented by restating the single-objective constrained optimization as a set of single-objective unconstrained problems and dynamically assigning to the individual adaptively as its fitness, and the self-adaptive strategy of control parameters based on the intrinsic structure analysis of differential vectors is use to solve each unconstrained optimization problem individually. This approach is tested on a suit of test problems proposed for CEC2010 competition and special session on single objective constrained real-parameter optimization. The result indicates the combination of dynamic constraint-handling mechanism and self-adaptation of control parameters in DE will outperform using the former solely for constrained optimization.

Differential evolution (DE) is undoubtedly one of the most powerful stochastic searching optimization algorithms. However, solving a specific problem using DE crucially depends on appropriately choosing of trial vector generation strategies and their associated control parameters. At the same time, multimodal optimization refers to locating not only one optimum but a set of optimal solutions. Niching is a useful technique to solve multi-modal optimization problems. Discovering multiple niches is the key capability of niching algorithms. In this paper, we propose a Strategy Adaptive Memetci Crowding DE (SAMCDE), which incorporate Crowding DE (CDE) with strategies and control parameter self-adaptation technique as well as fine search technique to handle multi-modal optimization problems. The algorithm is tested on 10 benchmark multi-modal functions and compared with the original CDE as well as several popular multimodal optimization algorithms in literature. As shown by the experimental results, the proposed algorithm is able to generate superior performance on the tested functions.

An Elite Multi-Group Differential Evolution algorithm for unconstrained single objective optimization is proposed. In the novel algorithm, the population is divided into sub-groups with different parameters setting to balance the global and local search ability. The good information collected in the search process is exchanged among groups. Experiments are conducted on seven commonly used benchmark functions and two new constructed harder test functions which are useful to test the local search ability of the algorithms and the proposed algorithm shows its effectiveness and efficiency.

In this paper, Dynamic Multi-Swarm Particle Swarm Optimizer (DMS-PSO) which was first designed for solving single objective optimizations problems is extended to solve Multi-objective optimization problems with constraints. Through analysis, novel pbest and lbest updating criteria which are more suitable for solving Multi-objective optimization problems are proposed. By combining the external archive and the novel updating criteria, excellent performance is achieved by DMS-MO-PSO on eight benchmark test functions.

In this paper, A self-adaptive strategy to determine the control parameters of Differential Evolution (DE) is proposed based on the elaborate analysis of intrinsic structure. The projection information of fitness function in differential direction is used to get the scale factor, while the difference between the local distance and global search range is applied to determine the crossover rate. This approach named as ITDE is tested by the benchmark functions including unconstrained uni-Modal, multi-Modal and bound-constrained ones, and the test results indicate the ITDE has good performance to balance the explorative and exploitative capability of algorithm than standard DE and easy to be implemented widely with many variants of DE algorithm.

Markowitz mean-variance model is one of the best known models that has been heavily studied in modern world of finance. However, the model is considered to be too basic in practice, as it ignores many of the constraints that real-world investors have to face with. In this paper we focused on a complex constrained portfolio selection model with additional constraining factors including the transaction fee, the minimal transaction unit, the maximal transaction quantity of every assets and the minimum/maximum of the investment. When taken these complex constraints in to account, the process became a high-dimensional constrained optimization problem. In our study, based on the study of symbiosis phenomenon in natural ecosystem, a multiple-swarm approach (SMPSO) was proposed to solve the resulting model. A numerical experimental study of a portfolio selection problem was conducted to illustrate our proposed method. The simulation results demonstrated that our proposed method is more efficient than PSO based method in solving the complex constrained portfolio selection problem.