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.

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.

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.

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.