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.

Feature selection is a crucial method for discovering relevant features in high‐dimensional data. However, most studies primarily focus on completely labeled data, ignoring the frequent occurrence of missing labels in real‐world problems. To address high‐dimensional and label‐missing problems in data classification simultaneously, we proposed a semisupervised bacterial heuristic feature selection algorithm. To track the label‐missing problem, a k ‐nearest neighbor semisupervised learning strategy is designed to reconstruct missing labels. In addition, the bacterial heuristic algorithm is improved using hierarchical population initialization, dynamic learning, and elite population evolution strategies to enhance the search capacity for various feature combinations. To verify the effectiveness of the proposed algorithm, three groups of comparison experiments based on eight datasets are employed, including two traditional feature selection methods, four bacterial heuristic feature selection algorithms, and two swarm‐based heuristic feature selection algorithms. Experimental results demonstrate that the proposed algorithm has obvious advantages in terms of classification accuracy and selected feature numbers.

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.

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.

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.

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.

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.