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| 1 | A self-organizing multimodal multi-objective pigeon-inspired optimization algorithm显示文摘Multi-objective optimization algorithms have recently attracted much attention as they can solve problems involving two or more conflicting objectives effectively and efficiently. However, most existing studies focus on improving the performance of the solutions in the objective spaces. This paper proposes a novel multimodal multi-objective pigeon-inspired optimization(MMOPIO) algorithm where some mechanisms are designed for the distribution of the solutions in the decision spaces. First, MMOPIO employs an improved pigeon-inspired optimization(PIO) based on consolidation parameters for simplifying the structure of the standard PIO. Second, the self-organizing map(SOM) is combined with the improved PIO for better control of the decision spaces, and thus, contributes to building a good neighborhood relation for the improved PIO.Finally, the elite learning strategy and the special crowding distance calculation mechanisms are used to prevent premature convergence and obtain solutions with uniform distribution, respectively. We evaluate the performance of the proposed MMOPIO in comparison to five state-of-the-art multi-objective optimization algorithms on some test instances, and demonstrate the superiority of MMOPIO in solving multimodal multi-objective optimization problems. | Yi HU Jie WANG Jing LIANG Kunjie YU Hui SONG Qianqian GUO Caitong YUE Yanli WANG | 2019 | Science China(Information Sciences)2019，62，7： | 10 |
| 2 | Differential Evolution with Level-Based Learning Mechanism显示文摘To address complex single objective global optimization problems,a new Level-Based Learning Differential Evolution(LBLDE)is developed in this study.In this approach,the whole population is sorted from the best to the worst at the beginning of each generation.Then,the population is partitioned into multiple levels,and different levels are used to exert different functions.In each level,a control parameter is used to select excellent exemplars from upper levels for learning.In this case,the poorer individuals can choose more learning exemplars to improve their exploration ability,and excellent individuals can directly learn from the several best individuals to improve the quality of solutions.To accelerate the convergence speed,a difference vector selection method based on the level is developed.Furthermore,specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process.A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants. | Kangjia Qiao Jing Liang Boyang Qu Kunjie Yu Caitong Yue Hui Song | 2022 | Complex System Modeling and Simulation2022，2，1： | 1 |
| 3 | Locating multiple roots of nonlinear equation systems via multi-strategy optimization algorithm with sequence quadratic program显示文摘Dear editor, Locating all the roots of a nonlinear equation system (NES) is not only of great significance for solving real-world problems but also one of the core problems of mathematics [1]. | Jing LIANG Boyang QU Baolei LI Kunjie YU Caitong YUE | 2022 | Science China(Information Sciences)2022，65，7： | 1 |
| 4 | Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization显示文摘Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA. | Kangjia Qiao Jing Liang Zhongyao Liu Kunjie Yu Caitong Yue Boyang Qu | 2023 | IEEE/CAA Journal of Automatica Sinica2023，10，10： | 0 |
| 5 | An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification显示文摘Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset.Dominance-based and decomposition-based multiobjective evolutionary algorithms(MOEAs)have been extensively used to address the feature selection problem due to their strong global search capability.However,most of them face the problem of not effectively balancing convergence and diversity during the evolutionary process.In addressing the aforementioned issue,this study proposes a unified evolutionary framework that combines two search forms of dominance and decomposition.The advantages of the two search methods assist one another in escaping the local optimum and inclining toward a balance of convergence and diversity.Specifically,an improved environmental selection strategy based on the distributions of individuals in the objective space is presented to avoid duplicate feature subsets.Furthermore,a novel knowledge transfer mechanism that considers evolutionary characteristics is developed,allowing for the effective implementation of positive knowledge transfer between dominance-based and decomposition-based feature selection methods.The experimental results demonstrate that the proposed algorithm can evolve feature subsets with good convergence and diversity in a shorter time compared with 9 state-of-the-art feature selection methods on 20 classification problems. | Jing LIANG Yuyang ZHANG Ke CHEN Boyang QU Kunjie YU Caitong YUE Ponnuthurai Nagaratnam SUGANTHAN | 2024 | Science China(Information Sciences)2024，67，2： | 0 |
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