A multi-objective particle swarm optimization algorithm based on two-archive mechanism

被引:95
作者
Cui, Yingying
Meng, Xi
Qiao, Junfei [1 ]
机构
[1] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
基金
中国国家自然科学基金;
关键词
Multi-objective particle swarm; optimization; Two-archive mechanism; Genetic operator; Evolutionary information; EVOLUTIONARY ALGORITHM;
D O I
10.1016/j.asoc.2022.108532
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As a powerful optimization technique, multi-objective particle swarm optimization algorithms have been widely used in various fields. However, performing well in terms of convergence and diversity simultaneously is still a challenging task for most existing algorithms. In this paper, a multi-objective particle swarm optimization algorithm based on two-archive mechanism (MOPSO_TA) is proposed for the above challenge. First, two archives, including convergence archive (CA) and diversity archive (DA) are designed to emphasize convergence and diversity separately. On one hand, particles are updated by indicator-based scheme to provide selection pressure toward the optimal direction in CA. On the other hand, shift-based density estimation and similarity measure are adopted to preserve diverse candidate solutions in DA. Second, the genetic operators are conducted on particles from CA and DA to further enhance the quality of solutions as global leaders. Then the search ability of MOPSO_TA can be improved by performing hybrid operators. Furthermore, to balance global exploration and local exploitation of MOPSO_TA, a flight parameters adjustment mechanism is developed based on the evolutionary information. Finally, the proposed algorithm is compared experimentally with several representative multi-objective optimization algorithms on 21 benchmark functions. The experimental results demonstrate the competitiveness and effectiveness of the proposed method.(c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:17
相关论文
共 56 条
[1]   A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization [J].
Asafuddoula, M. ;
Ray, Tapabrata ;
Sarker, Ruhul .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2015, 19 (03) :445-460
[2]   Indicator-based multi-objective local search [J].
Basseur, M. ;
Burke, E. K. .
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, 2007, :3100-3107
[3]   A Grid-Based Inverted Generational Distance for Multi/Many-Objective Optimization [J].
Cai, Xinye ;
Xiao, Yushun ;
Li, Miqing ;
Hu, Han ;
Ishibuchi, Hisao ;
Li, Xiaoping .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021, 25 (01) :21-34
[4]   An Evolutionary Algorithm with Double-Level Archives for Multiobjective Optimization [J].
Chen, Ni ;
Chen, Wei-Neng ;
Gong, Yue-Jiao ;
Zhan, Zhi-Hui ;
Zhang, Jun ;
Li, Yun ;
Tan, Yu-Song .
IEEE TRANSACTIONS ON CYBERNETICS, 2015, 45 (09) :1851-1863
[5]   A Competitive Swarm Optimizer for Large Scale Optimization [J].
Cheng, Ran ;
Jin, Yaochu .
IEEE TRANSACTIONS ON CYBERNETICS, 2015, 45 (02) :191-204
[6]   Large-scale many-objective particle swarm optimizer with fast convergence based on Alpha-stable mutation and Logistic function [J].
Cheng, Shixin ;
Zhan, Hao ;
Yao, Huiqin ;
Fan, Huayu ;
Liu, Yan .
APPLIED SOFT COMPUTING, 2021, 99
[7]  
Coello CAC, 2002, IEEE C EVOL COMPUTAT, P1051, DOI 10.1109/CEC.2002.1004388
[8]   Multi-stage multi-objective particle swarm optimization algorithm based on the evolutionary information of population [J].
Cui, Yingying ;
Qiao, Junfei ;
Meng, Xi .
2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, :3412-3417
[9]   Two-Archive Evolutionary Algorithm Based on Multi-Search Strategy for Many-Objective Optimization [J].
Dai, Cai .
IEEE ACCESS, 2019, 7 :79277-79286
[10]   Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems [J].
Das, I ;
Dennis, JE .
SIAM JOURNAL ON OPTIMIZATION, 1998, 8 (03) :631-657