Differential Evolution Algorithm Based on Ensemble of Constraint Handling Techniques and Multi-Population Framework

• 作者: Quanxi Feng Sainan Yuan Yanting Wei
• 来源期刊: 智能科学国际期刊(英文)
• 学科: 工学
• 关键词: CONSTRAINT Optimization DIFFERENTIAL Evolution Algorithm MULTI-POPULATION ε CONSTRAINT HANDLING Technique
• 年，卷（期）: 2020,（2）
• 页码范围: 22-40
• 页数: 19页
• 分类号: TP1

摘要

Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differential evolution algorithm?based on ensemble of constraint handling techniques and multi-population?framework, called ECMPDE. First, handling three improved variants of differential evolution algorithms are dynamically matched with two constraint handling techniques through the constraint allocation mechanism. Each combination includes three variants with corresponding constraint handling technique?and these combinations are in the set. Second, the population is divided into three smaller subpopulations and one larger reward subpopulation. Then a combination with three constraint algorithms is randomly selected from the set, and the three constraint algorithms are run in three sub-populations respectively. According to the improvement of fitness value, the optimal constraint?algorithm is selected to run on the reward sub-population, which can share?information and close cooperation among populations. In order to verify the effectiveness of the proposed algorithm, 12 standard constraint optimization problems?and 10 engineering constraint optimization problems are tested. The experimental results show that ECMPDE is an effective algorithm for solving constraint optimization problems.