五种粒子群优化模型效率的研究

Research of effectiveness of five particle swarm optimization models

粒子群优化算法按照认知部分和社会部分被区分为5种模型(完全模型、自认知模型、社交模型、非自身社交模型和非自身完全模型)。为了明确5种粒子群优化模型的效率,选用进化计算领域中常用的5种基准函数,分别对5种粒子群优化算法模型设置不同的参数,分析了它们在求解5种基准函数时的成功率、平均函数求值数、最佳适应度等。结果表明:PSO完全模型和非自身完全模型使用收缩系数K在某些参数设置下求解高维问题时即搜索问题的解时效率较高,社交模型和非自身社交模型在一些参数设置下求解Schaffer函数等二维问题的效率最好。

The basic particle swarm optimization algorithm is identified as five types of PSO according to its cognition component and social component value,such as PSO Full-Model,PSO Cognitive-Only Model,PSO Social-Only Model,PSO Selfless Model and PSO Selfless Full-model.Compare five PSO models’effectiveness and efficiency according to their success rate,average function evaluation and their best fitness by applying parameter set and using five benchmark functions.The result is that Full-Model and Selfless Full Model with K are effective in solving the functions with high dimension,Social Model and Selfless Model without K are also effective in solving the functions with less dimension such as Schaffer function.