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求解约束优化问题的改进果蝇优化算法及其工程应用
石建平, 李培生, 刘国平
南昌大学机电工程学院
摘要:
针对基本果蝇优化算法收敛速度慢、求解精度低、易于陷入局部极值以及算法候选解不能取负值等不足,提出了一种用于解决约束优化问题的改进果蝇优化算法。该算法利用果蝇个体历史最佳记忆信息和种群全局历史最佳记忆信息构建了多策略混合协同进化的搜索机制,较好保持了种群的多样性,有效避免了算法的早熟收敛现象;通过引入实时动态更新机制和局部深度搜索策略,进一步提高了算法的收敛速度和收敛质量。用13个标准测试函数和两个工程优化问题来验证所提出算法的可行性与有效性。实验结果表明:与其它智能优化算法相比,该算法具有较强的优化性能。
关键词:  果蝇优化算法  约束优化问题  协同进化  局部搜索  工程优化
DOI:10.13195/j.kzyjc.2019.0557
分类号:TP301.6
基金项目:国家自然科学(51566012);贵州省联合(黔科合LH字[2015]7302号).
Improved Fruit Fly Optimization Algorithm for Solving Constrained Optimization Problems and Engineering Applications
shijianping, Lipeishen, Liuguoping
Nanchang University
Abstract:
In view of the shortcomings of the basic fruit fly optimization algorithm, such as slow convergence speed, low accuracy, easy to fall into local optimum, and the candidate solutions of the algorithm cannot take negative values, an improved fruit fly optimization algorithm for solving constrained optimization problem was proposed. Using the best memory information of individual history and the best memory information of group global history, a multi-strategy hybrid co-evolutionary search mechanism was constructed, which can better maintain the diversity of the population and effectively avoid premature convergence of the algorithm. By introducing a real-time dynamic update mechanism and a local depth search strategy, the convergence speed and convergence quality of the algorithm were further improved. Experiments on 13 benchmark problems and two engineering optimization problems show that compared with other intelligent optimization algorithms, the algorithm has stronger optimization performance.
Key words:  fruit fly optimization algorithm  constrained optimization problem  co-evolutionary  local search  engineering optimization

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