scholarly journals A constraint handling technique using compound distance for solving constrained multi-objective optimization problems

2021 ◽  
Vol 6 (6) ◽  
pp. 6220-6241
Author(s):  
Jiawei Yuan ◽  
Keyword(s):  
Optimization Problems ◽  
Constraint Handling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Handling Technique

Adaptively Allocating Constraint-Handling Techniques for Constrained Multi-objective Optimization Problems

2021 ◽  
pp. 2159032
Author(s):  
Ning Yang ◽  
Hai-Lin Liu
Keyword(s):  
Optimization Problems ◽  
Performance Measure ◽  
Experimental Results ◽  
The Other ◽  
Constraint Handling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Selection Operators ◽  
Handling Technique ◽  
Feasible Solutions

For solving constrained multi-objective optimization problems (CMOPs), an effective constraint-handling technique (CHT) is of great importance. Recently, many CHTs have been proposed for solving CMOPs. However, no single CHT can outperform all kinds of CMOPs. This paper proposes an algorithm, namely, ACHT-M2M, which adaptively allocates the existing CHTs in an M2M framework for solving CMOPs. To be more specific, a CMOP is first decomposed into several constrained multi-objective optimization subproblems by ACHT-M2M. Each subproblem has a subpopulation in a subregion. CHT for each subregion is adaptively allocated according to a proposed composite performance measure. Population for the next generation is selected from subregions by selection operators with different CHTs and the obtained nondominated feasible solutions in each generation are used to update a predefined archive. ACHT-M2M assembles the advantages of different CHTs and makes them cooperate with each other. The proposed ACHT-M2M is finally compared with the other 12 representative algorithms on benchmark CMOPs and the experimental results further confirm the effectiveness of ACHT-M2M for solving CMOPs.

A Constrained Multi-Objective Evolutionary Algorithm Based on Boundary Search and Archive

2015 ◽  
Vol 30 (01) ◽  
pp. 1659002 ◽  
Author(s):  
Hai-Lin Liu ◽  
Chaoda Peng ◽  
Fangqing Gu ◽  
Jiechang Wen
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Search Method ◽  
Constraint Handling ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Genetic Operator ◽  
Multi Objective ◽  
Leading Role

In this paper, we propose a decomposition-based evolutionary algorithm with boundary search and archive for constrained multi-objective optimization problems (CMOPs), named CM2M. It decomposes a CMOP into a number of optimization subproblems and optimizes them simultaneously. Moreover, a novel constraint handling scheme based on the boundary search and archive is proposed. Each subproblem has one archive, including a subpopulation and a temporary register. Those individuals with better objective values and lower constraint violations are recorded in the subpopulation, while the temporary register consists of those individuals ever found before. To improve the efficiency of the algorithm, the boundary search method is designed. This method makes the feasible individuals with a higher probability to perform genetic operator with the infeasible individuals. Especially, when the constraints are active at the Pareto solutions, it can play its leading role. Compared with two algorithms, i.e. CMOEA/D-DE-CDP and Gary’s algorithm, on 18 CMOPs, the results show the effectiveness of the proposed constraint handling scheme.

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