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Evolutionary Intelligence
Published in: Evolutionary Intelligence 3/2022

08-03-2021 | Research Paper

Hybrid neighborhood and global replacement strategies for multi objective evolutionary algorithm based on decomposition

Authors: Xiaoji Chen, Haibin Wang, Jiankun Chu, Bin Hai, Zhichao Wang

Published in: Evolutionary Intelligence | Issue 3/2022

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Abstract

In multi-objective evolutionary algorithm based on decomposition, replacement strategy (RS) plays a key role in balancing population convergence and diversity. However, the existing RSs either focus on the neighborhood or global replacement strategy, which may have difficulties in solving the complex multi-objective optimization problems. To solve this problem, a hybrid neighborhood and global replacement strategies for multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed. In this mechanism, a probability threshold pt is used to determine whether to implement neighborhood or global replacement strategy to balance convergence and diversity. Meanwhile, an offspring generation method is designed to generate high-quality offspring solutions for each sub-problem, which can improve the success of the replacement strategy. Based on MOEA/D, a new MOEA/D-HRS algorithm is designed, and it is compared with some state-of-the-art multi-objective evolutionary algorithms (MOEAs) in a series of bi-objective and three-objective test instances with linear or nonlinear various linkages. The experimental results show that MOEA/D-HRS can achieve the best performance in most test instances.
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Appendix
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Literature
1.
Zhou A, Zhao H, Zhang H. (2019) Pareto optimal set approximation by models: a linear case. Paper presented at international conference on evolutionary multi-criterion optimization, pp. 451–462
2.
Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Comput 2(3):221–248CrossRef
3.
Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multiobjective optimization: NSGA-II. Paper presented at conference on parallel problem solving from nature. Springer, pp. 849–858
4.
M. Laumanns. (2001) SPEA2: Improving the strength Pareto evolutionary algorithm. Institut of Technische Informatik und Kommunikationsnetze (TIK).
5.
Zitzler E, Simon Künzli (2004) Indicator-based selection in multiobjective search. International Conference on Parallel Problem Solving from Nature, Birmingham, UK, Springer, pp. 832–842
6.
Tharwat A, Houssein EH, Ahmed MM, Hassanien AE, Gabel T (2018) MOGOA algorithm for constrained and unconstrained multi-objective optimization problems. Appl Intell 48(8):2268–2283CrossRef
7.
Zhang Q, Li H (2007) MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731CrossRef
8.
Mashwani WK, Salhi A (2012) A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation. Appl Soft Comput 12(9):2765–2780CrossRef
9.
Zhang H, Zhou A, Zhang G, Singh HK (2017) Accelerating MOEA/D by nelder-mead method. IEEE Congress on Evolutionary Computation, San Sebastian, Spain, IEEE, pp. 976–983
10.
Zhou A, Zhang Y, Zhang G, Gong W (2015) On neighborhood exploration and subproblem exploitation in decomposition based multiobjective evolutionary algorithms. IEEE Congress on Evolutionary Computation. Sendai, Japan, IEEE, pp 1704–1711
11.
Zhang J, Zhou A, Zhang G (2015) A multiobjective evolutionary algorithm based on decomposition and preselection. Bio-inspired computing-theories and applications. Springer, Berlin
12.
Lin Q, Tang C, Ma Y, Du Z, Li J, Chen J, Ming Z (2016) A novel adaptive control strategy for decomposition based multiobjective algorithm. Comput Oper Res 78:94–107MathSciNetCrossRef
13.
Lin Q, Liu Z, Yan Q, Du Z, Coello CAC, Liang Z, Wang W, Chen J (2016) Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm. Inf Sci 339:332–352CrossRef
14.
Li K, Zhang Q, Kwong S, Li M, Wang R (2014) Stable matching-based selection in evolutionary multiobjective optimization. IEEE Trans Evol Comput 18(6):909–923CrossRef
15.
Wang Z, Zhang Q, Zhou A, Gong M, Jiao L (2017) Adaptive replacement strategies for MOEA/D. IEEE Transact Cybernetics 46(2):474–486CrossRef
16.
Li J, Tan Y (2019) A comprehensive review of the fireworks algorithm. ACM Comput Surv 52(6):1–28CrossRef
17.
L. Liu, S. Zheng, and Y. Tan. (2015) S-metric based multi-objective fireworks algorithm. IEEE Congress on Evolutionary Computation, pp. 1257–1264.
18.
Naujoks B, Beume N, Emmerich M (2005) Multi-objective optimization using s-metric selection: application to three-dimensional solution spaces. IEEE Congr Evolut Comput 2:1282–1289MATH
19.
Li H, Ong YS, Gong M, Wang Z (2019) Evolutionary multitasking sparse reconstruction: framework and case study. IEEE Trans Evol Comput 23(5):733–747CrossRef
20.
Gong M, Wang Z, Zhu Z, Jiao L (2017) A similarity-based multiobjective evolutionary algorithm for deployment optimization of near space communication system. IEEE Trans Evol Comput 21(6):878–897CrossRef
21.
Liu J, Gong M, Miao Q, Wang X, Li H (2018) Structure learning for deep neural networks based on multiobjective optimization. IEEE Transact Neural Networks Learn Syst 29(6):2450–2463MathSciNetCrossRef
22.
Gong M, Liu J, Li H, Cai Q, Su L (2015) A multiobjective sparse feature learning model for deep neural networks. IEEE Transact Neural Networks Learn Syst 26(12):3263–3277MathSciNetCrossRef
23.
Li J, Sang H, Han Y (2018) Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions. J Clean Prod 181:584–598CrossRef
24.
Malik R, Dhir R, Mittal SK (2019) Remote sensing and landsat image enhancement using multiobjective PSO based local detail enhancement. J Ambient Intell Human Comput 10(9):3563–3571CrossRef
25.
David L, González–ÁlvarezMiguel A. Vega–RodríguezJuan A. Gómez–PulidoJuan M. Sánchez-Pérez. (2010) Using a parallel team of multiobjective evolutionary algorithms to solve the motif discovery problem. Distrib Comput Artif Intell 79:569–576CrossRef
26.
Gong M, Li H, Meng D, Miao Q (2019) Decomposition-based evolutionary multi-objective optimization to self-paced learning. IEEE Trans Evol Comput 23(2):288–302CrossRef
27.
Gong M, Li H, Luo E, Liu J, Liu J (2017) A multi-objective cooperative coevolutionary algorithm for hyperspectral sparse unmixing. IEEE Trans Evol Comput 21(2):234–248CrossRef
28.
Miettinen K (1999) Nonlinearmultiobjectiveoptimization. Kluwer Academic Publishers, Amsterdam, pp 66–74
29.
Jin Y (2005) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput 9(1):3–12CrossRef
30.
Naujoks B, Beume N, Emmerich M (2005) Multi-objective optimisation using s-metric selection: application to three-dimensional solution spaces. IEEE Cong Evolut Comput 2:1282–1289MATH
31.
Li H, Zhang Q (2009) Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II. IEEE Trans Evol Comput 13(2):284–302CrossRef
32.
H. H. Tam, M. F. Leung, Z. Wang, S. C. Ng, C. C. Cheung, and A. K. Lui. (2016) Improved adaptive global replacement scheme for MOEA/D-AGR. IEEE Congress on Evolutionary Computation. Vancouver, BC, Canada, IEEE, pp 2153–2160
33.
Y. Li, A. Zhou, and G. Zhang. (2014) An MOEA/D with multiple differential evolution mutation operators. IEEE Congress on Evolutionary Computation, Beijing, China, IEEE, pp. 397–404
34.
Li K, Kwong S, Zhang Q, Deb K (2015) Interrelationship-based selection for decomposition multiobjective optimization. IEEE Transactions on Cybernetics 45(10):2076–2088CrossRef
Metadata
Title
Hybrid neighborhood and global replacement strategies for multi objective evolutionary algorithm based on decomposition
Authors
Xiaoji Chen
Haibin Wang
Jiankun Chu
Bin Hai
Zhichao Wang
Publication date
08-03-2021
Publisher
Springer Berlin Heidelberg
Published in
Evolutionary Intelligence / Issue 3/2022
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-021-00582-1

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