计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

蚁群和微分进化相融合的自适应优化算法

魏 林a,付 华b,尹玉萍b   

  1. (辽宁工程技术大学 a. 基础教学部;b. 电气与控制工程学院,辽宁 葫芦岛 125105)
  • 收稿日期:2012-08-20 出版日期:2013-09-15 发布日期:2013-09-13
  • 作者简介:魏 林(1979-),男,讲师、博士研究生,主研方向:智能计算,智能控制,信息安全;付 华,教授、博士生导师; 尹玉萍,讲师、硕士
  • 基金项目:
    国家自然科学基金资助项目(51274118, 70971059);辽宁省科技攻关计划基金资助项目(2011229011)

Self-adaption Optimization Algorithm with Fusion of Ant Colony and Differential Evolution

WEI Lin  a, FU Hua b, YIN Yu-ping  b   

  1. (a. Department of Basic Education; b. School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China)
  • Received:2012-08-20 Online:2013-09-15 Published:2013-09-13

摘要: 为解决复杂函数的全局优化问题,提出一种蚁群和微分进化相融合的自适应优化算法。采用微分进化算法的变异和交叉操作避免蚁群算法过早收敛,使用蚁群算法的寻优路径信息素正反馈机制来加速微分进化算法收敛于最优路径,并自动调整搜索范围。实验结果表明,与蚁群算法和微分进化算法相比,该算法全局优化的搜索效率较高。

关键词: 蚁群算法, 微分进化算法, 信息素, 融合算法, 全局优化

Abstract: An self-adaption hybrid optimization algorithm with fusion of Ant Colony(AC) algorithm and Differential Evolution(DE) algorithm is proposed to solve the problem of complicated function global optimization. The new algorithm utilizes DE algorithm with the mutation and crossover operation to avoid AC algorithm premature convergence, and utilizes the pheromone positive feedback effect to speed up evolutionary algorithm search, and automatically adjusts searching range. Experimental results show that compared to the AC algorithm and DE algorithm, this new algorithm greatly improves the global optimization search efficiency.

Key words: Ant Colony(AC) algorithm, Differential Evolution(DE) algorithm, pheromone, fusion algorithm, global optimization

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