| 基于差分演化与猫群算法融合的群体智能算法 |
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| 引用本文：吴伟林，周永华.基于差分演化与猫群算法融合的群体智能算法[J].计算技术与自动化,2014,(4):78-83 |
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| 中文摘要:提出一种基于差分演化与猫群算法融合的群体智能算法。该算法基于猫群算法的两种行为模式，引进差分演化的思想，根据分组率随机把群体分成两个种群，一个种群执行猫群算法搜寻模式，另一种群执行差分变异模式，算法采用一种信息共享机制，使两个种群在搜索最优解时可以实现协同进化，信息交流。既实现了不同进化模式间的优势互补, 又可以增加种群的多样性。对5个基准函数进行仿真实验并分别与DE和CSO 进行比较，表明混合算法同时具有全局搜索和局部搜索最优解性能，收敛速度快，计算精度高，更适合用于求解高维复杂函数。 |
| 中文关键词:差分演化算法 猫群算法 混合优化算法 协同进化 |
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| The Swarm Intelligent Algorithm Based on the Combination of Differential Evolution and Cat Swarm Optimization Algorithms |
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| Abstract:The author proposes a swarm algorithm based on the combination of differential evolution and cat swarm intelligent algorithms. The algorithm is based on two kinds of behavior pattern of cat swarm optimization and introduces the thought of differential evolution. According to the group rate ,the algorithm divides the swarm into two groups randomly, one group performs cat swarm optimization (cso) algorithm searching mode, the other group performs differential variation model.The two groups realize co-evolution and exchange of information in searching the optimal solution by introducing an information sharing mechanism,thus both realizing the complementary advantages of different evolutionary patterns and increasing the diversity of groups.Carrying out simulation experiments to the five benchmark functions and comparing the results with DE and CSO respectively,it can be seen that hybrid algorithm has optimal properties both in global search and local search, fast convergence speed and high calculation accuracy,making it more suitable in solving high-dimensional complicated function. |
| keywords:differential evolution cat swarm optimization hybrid optimization algorithm cooperate evolution |
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