A novel two-stage constraints handling framework for real-world multi-constrained multi-objective optimization problem based on evolutionary algorithm

Multi-constrained multi-objective optimization is a challenging topic, which is very common in dealing with real-world problems. This paper proposes a novel two-stage ρ g / μ g framework based on multi-objective evolutionary algorithm (MOEA) to solve the multi-constrained multi-objective optimizatio...

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Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 51; no. 11; pp. 8212 - 8229
Main Authors: Li, Xin, An, Qing, Zhang, Jun, Xu, Fan, Tang, Ruoli, Dong, Zhengcheng, Zhang, Xiaodi, Lai, Jingang, Mao, Xiaobing
Format: Journal Article
Language:English
Published: New York Springer US 01.11.2021
Springer Nature B.V
Subjects:
Artificial Intelligence
Computer Science
Constraints
Convergence
Evolutionary algorithms
Genetic algorithms
Handling
Machines
Manufacturing
Mechanical Engineering
Multiple objective analysis
Optimization
Pareto optimization
Processes
Relaxation method (mathematics)
Sorting algorithms
Multi-constrained multi-objective optimization
Two-stage constraints handling
Dynamic stochastic ranking
Constraint relaxation
ISSN:0924-669X, 1573-7497
Online Access:Get full text
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Summary:Multi-constrained multi-objective optimization is a challenging topic, which is very common in dealing with real-world problems. This paper proposes a novel two-stage ρ g / μ g framework based on multi-objective evolutionary algorithm (MOEA) to solve the multi-constrained multi-objective optimization problems (MCMOPs), which dynamically balances the diversity and convergence of solutions. During the multi-constraints handling process, ρ g / μ g -MOEA makes the reduction of violated constraints as its primary goal, and converges to feasible regions by a proposed ρ g -criterion based constraints relaxation method. Moreover, in the late stage of evolution, by introducing the improved dynamic stochastic ranking (DSR) strategy, the “potential” infeasible individuals are utilized to find more feasible regions, which would guarantee a good distribution of the obtained Pareto frontiers. Thereafter, the proposed framework combined with non-dominated sorting genetic algorithm II (NSGAII) is applied to ten benchmark functions and a series of real-world MCMOPs, and the performances are compared with those obtained by some state-of-the-art constraints handling methods. Experimental results indicate that the proposed ρ g / μ g framework outperforms the current efficient methods in dealing with test CMOPs, and can achieve satisfactory results when solving real-world MCMOPs.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-020-02174-5