| Summary: | 碩士 === 中原大學 === 資訊管理研究所 === 95 === Particle swarm optimization algorithm (PSO) is motivated from the simulation of simplified social behavior of bird flocking, fish schooling and human. The PSO is simple and easy to implement on many optimization problems. And it is demonstrated that it is an efficient way to solve the optimization question. However, it doesn’t overcome the problems of avoiding premature convergence and escaping local optima.
On multi-modal test problems the PSO tends to suffer from premature convergence and a decrease of diversity in search space. That leads to fitness stagnation of swarm. Therefore to keep high diversity is crucial for preventing premature convergence and trapping in local solution. In the classical PSO, a constant or linearly decreasing inertia weight is used for solving the optimization problem, but it is unable to solve the phenomenon of stagnation.
In this research, a diversity-based inertia weight strategy and the activation of swarm in the PSO are proposed. The results show that a diversity-based inertia weight strategy for PSO improves exploitation and exploration ability, but still keeps a rapid convergence and still fine precision.
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