This study proposes an enhanced particle swarm optimizer incorporating a weighted particle (EPSOWP) to improve the evolutionary performance for a set of benchmark functions. In conventional particle swarm optimizer (PSO), there are two principal forces to guide the moving direction of each particle. However, if the current particle lies too close to either the personal best particle or the global best particle, the velocity is mainly updated by only one term. As a result, search step becomes smaller and the optimization of the swarm is likely to be trapped into a local optimum. To address this problem, we define a weighted particle for incorporation into the particle swarm optimization. Because the weighted particle has a better opportunity getting closer to the optimal solution than the global best particle during the evolution, the EPSOWP is capable of guiding the swarm to a better direction to search the optimal solution. Simulation results show the effectiveness of the EPSOWP, which outperforms various evolutionary algorithms on a selected set of benchmark functions. Furthermore, the proposed EPSOWP is applied to controller design and parameter identification for an inverted pendulum system as well as parameter learning of neural network for function approximation to show its viability to solve practical design problems.
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