文件名称:GTPSO
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- 人工智能/神经网络/遗传算法
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- [PDF]
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- 2012-11-26
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- 218kb
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提出一种改进的粒子群优化算法———基于全局劣汰策略的混合粒子群优化算法(GTPSO) 。GTPSO在
保持PSO算法快速收敛的基础上,以郭涛算法(GuoA)的寻优机制确保种群的多样性和算法的坚韧性。数值计
算结果表明,对于高维(维数≥10)复杂非凸多峰函数的数值优化问题, GTPSO算法的计算结果均优于GuoA算
法和粒子群优化算法。-An improved particle swarm optimization algorithm--- poor overall survival strategy based on a mixture of particle swarm optimization (GTPSO). GTPSO while maintaining fast convergence PSO algorithm based on the algorithm to Guo Tao (GuoA) optimization of the mechanism in place to ensure the diversity of the population of the tenacity and algorithms. The numerical results show that for high-dimensional (dimension ≥ 10) the complexity of multi-peak function non-convex numerical optimization problems, GTPSO algorithm results are superior to GuoA algorithms and particle swarm optimization algorithm.
保持PSO算法快速收敛的基础上,以郭涛算法(GuoA)的寻优机制确保种群的多样性和算法的坚韧性。数值计
算结果表明,对于高维(维数≥10)复杂非凸多峰函数的数值优化问题, GTPSO算法的计算结果均优于GuoA算
法和粒子群优化算法。-An improved particle swarm optimization algorithm--- poor overall survival strategy based on a mixture of particle swarm optimization (GTPSO). GTPSO while maintaining fast convergence PSO algorithm based on the algorithm to Guo Tao (GuoA) optimization of the mechanism in place to ensure the diversity of the population of the tenacity and algorithms. The numerical results show that for high-dimensional (dimension ≥ 10) the complexity of multi-peak function non-convex numerical optimization problems, GTPSO algorithm results are superior to GuoA algorithms and particle swarm optimization algorithm.
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一种基于全局劣汰策略的混合粒子群优化算法.pdf