An application of swarm optimization to nonlinear programming

摘要: Particle swarm optimization (PSO) is an technique based on population, which has similarities to other evolutionary algorithms. It initialized with a population of random solutions and searches for optima by updating generations. become the hotspot computation because its excellent performance simple implementation. After introducing basic principle PSO, particle algorithm embedded constraint fitness priority-based ranking method proposed in this paper solve nonlinear programming problem. By designing function constraints-handling method, PSO can evolve dynamic neighborhood varied inertia weighted value find global optimum. The results from preliminary investigation are quite promising show that reliable applicable almost all problems multiple-dimensional, complex constrained programming. proved be efficient robust testing some example benchmarks problems.