To overcome the difficulties occurring in the particle swarm optimization (PSO) such as the problem of premature convergence and / or slow search speed, this paper proposes an improved PSO (IPSO) algorithm to guarante...
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To overcome the difficulties occurring in the particle swarm optimization (PSO) such as the problem of premature convergence and / or slow search speed, this paper proposes an improved PSO (IPSO) algorithm to guarantee the convergence and get the global optimization solution for complex problems. Particles in PSO are anticipated to fly to their best position in the process of searching, but some particles will depart from the ideal routine owing to the uncertainty factors. In this paper a special mutation operator is introduced to make particles explore the search space more efficiently. The novel strategy produces a variation of the speed, which makes the algorithm explore the local and global minima thoroughly at the same time and improve the capability of the algorithm. The computational study of examples shows that the proposed method can not only significantly speed up the convergence, but also effectively solve the premature convergence problem.
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