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Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

iEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1458-1475页

作者： Jing Liang Hongyu Lin Caitong Yue ponnuthur.i nagaratnam suganthan Yaonan Wang School of Electrical and information Engineering Zhengzhou UniversityZhengzhou 450001 School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of Electrical and information Engineering Zhengzhou UniversityZhengzhou 450001China KiNDi Center for Computing Research Qatar UniversityDohaQatarand also with the Nanyang Technology UniversitySingapore College of Electrical and information Engineering and also with the National Engineering Research Center for Robot Visual Perception and Control TechnologyHunan UniversityChangsha 410082China iEEE

in multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective *** paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision *** to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal *** proposed algorithm adopts a dual-population framework and an improved environmental selection *** utilizes a convergence archive to help the first population improve the quality of *** improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first *** combination of these two strategies helps to effectively balance and enhance conver-gence and diversity *** addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is *** proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

关键词： Benchmark functions diversity measure evolution-ary algorithms multimodal multiobjective optimization.

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Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

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iEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1819-1835页

作者： Kangjia Qiao Jing Liang Kunjie Yu Xuanxuan Ban Caitong Yue Boyang Qu ponnuthur.i nagaratnam suganthan School of Electrical and information Engineering Zhengzhou UniversityZhengzhou 450001China School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of Electronic and information Zhongyuan University of TechnologyZhengzhou 450007China KiNDi Center for Computing Research College of EngineeringQatar UniversityDoha 999043Qatar

Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for *** all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto ***,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single ***,this paper attempts to identify promising single constraints and utilize them to help solve *** be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint ***,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance ***,an improved tentative method is designed to find and transfer useful knowledge among *** results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

关键词： Constrained multi-objective optimization(CMOPs) evolutionary multitasking knowledge transfer single constraint.

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An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

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Science China(information Sciences) 2024年第2期67卷 5-19页

作者： Jing LiANG Yuyang ZHANG Ke CHEN Boyang QU Kunjie YU Caitong YUE ponnuthur.i nagaratnam suganthan School of Electrical and information Engineering Zhengzhou University State Key Laboratory of intelligent Agricultural Power Equipment School of Electrical Engineering and Automation Henan Institute of Technology School of Electronics and information Zhongyuan University of Technology KiNDi Center for Computing Research College of EngineeringQatar University

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.

关键词： evolutionary algorithms feature selection multiobjective optimization knowledge transfer classification

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Multi-objective differential evolution with diversity enhancement

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Journal of Zhejiang University-Science C(Computers and Electronics) 2010年第7期11卷 538-543页

作者： ponnuthur.i-nagaratnam suganthan School of Electrical and Electronic Engineering Nanyang Technological University

Multi-objective differential evolution (MODE) is a powerful and efficient population-based stochastic search technique for solving multi-objective optimization problems in many scientific and engineering fields. However, premature convergence is the major drawback of MODE, especially when there are numerous local Pareto optimal solutions. To overcome this problem, we propose a MODE with a diversity enhancement (MODE-DE) mechanism to prevent the algorithm becoming trapped in a locally optimal Pareto front. The proposed algorithm combines the current population with a number of randomly generated parameter vectors to increase the diversity of the differential vectors and thereby the diversity of the newly generated offspring. The performance of the MODE-DE algorithm was evaluated on a set of 19 benchmark problem codes available from http://***/home/epnsugan/. With the proposed method, the performances were either better than or equal to those of the MODE without the diversity enhancement.

关键词： Multi-objective evolutionary algorithm (MOEA) Multi-objective differential evolution (MODE) Diversity enhancement

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Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

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自动化学报（英文版） 2024年第6期 1458页

作者： Jing Liang Hongyu Lin Caitong Yue ponnuthur.i nagaratnam suganthan Yaonan Wang

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Dynamic economic emission dispatch based on multi-objective pigeon-inspired optimization with double disturbance

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Science China(information Sciences) 2019年第7期62卷 108-119页

作者： Li YAN Boyang QU Yongsheng ZHU Baihao QiAO ponnuthur.i nagaratnam suganthan School of Electronic and information Engineering Zhongyuan University of Technology School of Electrical and Electronic Engineering Nanyang Technological University

Dear editor,Recently, dynamic economic dispatch (DED) has attracted much attention in power system optimal operation [1, 2], because it considers the interplay between the different dispatching periods based on ramp rate limits of the generator units. To reduce emissions during the process of power generation,an extended model of DED, namely dynamic economic emission dispatch (DEED)[3, 4], has been

关键词： pro Dynamic economic emission dispatch based on multi-objective pigeon-inspired optimization with double disturbance MOPSO

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Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

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自动化学报（英文版） 2024年第8期 1819页

作者： Kangjia Qiao Jing Liang Kunjie Yu Xuanxuan Ban Caitong Yue Boyang Qu ponnuthur.i nagaratnam suganthan

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双局部粒子群算法解决环境经济调度问题

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计算机工程与应用 2014年第11期50卷 1-6页

作者：瞿博阳梁静 ponnuthur.i nagaratnam suganthan 中原工学院电子信息学院郑州450007 南洋理工大学电气电子工程学院新加坡639798 郑州大学电气工程学院郑州450001

提出一种基于双局部最优的多目标粒子群优化算法,与可行解为优的约束处理方法相结合,来求解决非线性带约束的多目标电力系统环境经济调度问题。该算法针对传统多目标粒子群算法多样性低的局限性,通过对搜索空间的分割归类来增加帕累托... 详细信息

提出一种基于双局部最优的多目标粒子群优化算法,与可行解为优的约束处理方法相结合,来求解决非线性带约束的多目标电力系统环境经济调度问题。该算法针对传统多目标粒子群算法多样性低的局限性,通过对搜索空间的分割归类来增加帕累托最优解的多样性;并采用一种新的双局部最优来引导粒子的搜索,从而增强了算法的全局搜索能力。算法加入了可行解为优的约束处理方法对iEEE30节点六发电机电力系统环境经济负荷分配模型分别在几个不同复杂性问题的情况进行仿真测试,并与文献中的其他算法进行了比较。结果表明,改进的算法能够在保持帕累托最优解多样性的同时具有良好的收敛性能,更有效地解决电力系统环境经济调度问题。

关键词：环境经济调度多目标优化粒子群优化约束处理方法

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鸽群智能优化专题简介

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中国科学：信息科学 2019年第7期49卷 939-940页

作者：段海滨梁静 ponnuthur.i nagaratnam suganthan 北京航空航天大学自动化科学与电气工程学院北京100191 郑州大学电气工程学院郑州450001 School of Electrical & Electronic Engineering Nanyang Technological University Singapore 639798 Singapore

2016年8月,美国国防部国防科学委员会发布了Autonomy,指出"未来人工智能战争不可避免".2017年7月,美国情报高级研究计划局发布了Artificial intelligence and National Security,再次指出“人工智能技术是国家安全的颠覆性技术”.2017年... 详细信息

2016年8月,美国国防部国防科学委员会发布了Autonomy,指出"未来人工智能战争不可避免".2017年7月,美国情报高级研究计划局发布了Artificial intelligence and National Security,再次指出“人工智能技术是国家安全的颠覆性技术”.2017年7月21日,中国国务院发布了《新一代人工智能发展规划》,文中共21次提到“群体智能”,并多次提及“群体认知”、“群体感知”、协同与演化”、“群体集成智能”等概念.简单而言,群体智能是受群居性动物集体行为启发,用于设计问题求解算法和分布式系统的理论与方法.

关键词：智能优化人工智能技术美国国防部群体智能鸽群科学委员会分布式系统国家安全

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