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Evolutionary constrained optimization with hybrid constraint-handling technique
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DOI：10.1016/j.eswa.2022.118660.png)
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In constrained optimization evolutionary algorithms (COEAs), constraint-handling technique is used to balance the objective function and constraints, but how to achieve this balance is a very important problem. We found that the information of the population during the evolution process can reflect the current situation of the population, whether the population are inside the feasible region (feasible situation), or are near the boundary of the feasible region (semi-feasible situation), or far away from the feasible region (infeasible situation). Therefore, corresponding constraint-handling method are designed according to the information of each situation-Hybrid Constraint-handling Technique (HCT). The information of the population evolution process is used by HCT to maintain the objective function and constraints balance, and combines the evolutionary algorithm and HCT to propose ECO-HCT to solve COPs. Meanwhile, in infeasible situation, an elite replacement strategy is proposed to help the population accumulate experience. In addition, a criterion for judging that the population falls into the local optimum in the infeasible region and a simple restart mechanism are designed. They can help the population jump out of the local optimum in the infeasible region and effectively improve the algorithm's ability to solve complex COPs. The 24 constraint test functions from IEEE CEC2006, the 28 constraint test functions from IEEE CEC2017, and three constrained engineering design problems are used to verify the effectiveness and efficiency of the proposed ECO-HCT. Experimental results show that ECO-HCT has very competitive performance compared with other advanced methods.
Keyword:
Evolutionary constrained optimization
Hybrid constraint-handling technique
Differential evolution
Restart mechanism
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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