A hybrid multi-objective evolutionary algorithm with feedback mechanism

Exploration and exploitation are two cornerstones for multi-objective evolutionary algorithms (MOEAs). To balance exploration and exploitation, we propose an efficient hybrid MOEA (i.e., MOHGD) by integrating multiple techniques and feedback mechanism. Multiple techniques include harmony search, gen...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 48; no. 11; pp. 4149 - 4173
Main Authors: Lu, Chao, Gao, Liang, Li, Xinyu, Zeng, Bing, Zhou, Feng
Format: Journal Article
Language:English
Published: New York Springer US 01.11.2018
Springer Nature B.V
Subjects:
Artificial Intelligence
Computer Science
Convergence
Evolutionary algorithms
Exploitation
Exploration
Feedback
Genetic algorithms
Machines
Manufacturing
Mechanical Engineering
Multiple objective analysis
Pareto optimum
Processes
Searching
Strategy
Differential evolution
Feedback mechanism
Hybrid selection mechanism
Harmony search
Multi-objective evolutionary algorithm
ISSN:0924-669X, 1573-7497
Online Access:Get full text
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Summary:Exploration and exploitation are two cornerstones for multi-objective evolutionary algorithms (MOEAs). To balance exploration and exploitation, we propose an efficient hybrid MOEA (i.e., MOHGD) by integrating multiple techniques and feedback mechanism. Multiple techniques include harmony search, genetic operator and differential evolution, which can improve the search diversity. Whereas hybrid selection mechanism contributes to the search efficiency by integrating the advantages of the static and adaptive selection scheme. Therefore, multiple techniques based on the hybrid selection strategy can effectively enhance the exploration ability of the MOHGD. Besides, we propose a feedback strategy to transfer some non-dominated solutions from the external archive to the parent population. This feedback strategy can strengthen convergence toward Pareto optimal solutions and improve the exploitation ability of the MOHGD. The proposed MOHGD has been evaluated on benchmarks against other state of the art MOEAs in terms of convergence, spread, coverage, and convergence speed. Computational results show that the proposed MOHGD is competitive or superior to other MOEAs considered in this paper.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-018-1211-5