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中文摘要：
对基本粒子群优化算法的速度方程进行了改进,减少了控制参数,引入随机调节因子,使得粒子的自我认知能力和社会认知能力在一定范围内随机产生,同时对个体最优粒子进行自适应随机变异,由此构造出一种改进的粒子群优化算法。数值结果表明新算法能够克服早熟收敛,具有更好的性能和全局搜索能力。
英文摘要：
An Improved Particle Swarm Optimization（IPSO） algorithm is proposed by improving the standard PSO＇s velocity equation.The new algorithm reduces the control parameters,introduces random adjustment factor,and generates the cognitive ability and social cognitive ability of the particle randomly in a certain range.By judging the local convergence,when PSO gets into the local convergence,IPSO can carry out stochastic mutation on individual optimal particle.The experimental results demonstrate that the new algorithm can overcome premature convergence,and has better global searching and performance.
同期刊论文项目
同项目期刊论文
Modified Multi-Objective Particle Swarm Optimization Algorithm for Multi-objective Optimization Prob
Modified differential evolution algorithm of constrained nonlinear mixed integer programming problem
PSO algorithm with chaos and gene density mutation for solving nonlinear zero-one integer programmin
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