In the standard particle swarm optimization(SPSO),the big problem is that it suffers from premature convergence,that is,in complex optimization problems,it may easily get trapped in local optima.In order to mitigate p...In the standard particle swarm optimization(SPSO),the big problem is that it suffers from premature convergence,that is,in complex optimization problems,it may easily get trapped in local optima.In order to mitigate premature convergence problem,this paper presents a new algorithm,which is called particle swarm optimization(PSO) with directed mutation,or DMPSO.The main idea of this algorithm is to "let the best particle(the smallest fitness of the particle swarm) become more excellent and the worst particle(the largest fitness of the particle swarm) try to be excellent".The new algorithm is tested on a set of eight benchmark functions,and compared with those of other four PSO variants.The experimental results illustrate the effectiveness and efficiency of the DMPSO.The comparisons show that DMPSO significantly improves the performance of PSO and searching accuracy.展开更多
基金National Natural Science Foundation of China(No.60905039)
文摘In the standard particle swarm optimization(SPSO),the big problem is that it suffers from premature convergence,that is,in complex optimization problems,it may easily get trapped in local optima.In order to mitigate premature convergence problem,this paper presents a new algorithm,which is called particle swarm optimization(PSO) with directed mutation,or DMPSO.The main idea of this algorithm is to "let the best particle(the smallest fitness of the particle swarm) become more excellent and the worst particle(the largest fitness of the particle swarm) try to be excellent".The new algorithm is tested on a set of eight benchmark functions,and compared with those of other four PSO variants.The experimental results illustrate the effectiveness and efficiency of the DMPSO.The comparisons show that DMPSO significantly improves the performance of PSO and searching accuracy.
