New Multiobjectve PSO Algorithm for Nonlinear Constrained Programming Problems

摘要

A new approach is presented to solve nonlinear con-strained programming problems (NLCPs) by us-ing particle swarm algorithm(PSO). It neither uses any penalty functions, nor distinguish the feasi-ble solutions and the infeasible solutions including swarm. The new technique treats the NLCPs as a bi-objective optimization problem, one objective is the original objective of NLCPs, and the other is the degree violation of constraints. As we pre-fer to keep the ratio of infeasible solutions so as to increase the diversity of swarm and avoid the de-fect of conventional over-penalization, a new fitness function is designed based on the second objective.In order to make the PSO escape from the local optimum easily, we also design a adaptively dy-namically changing inertia weight. The numerical experiment shows that the algorithm is effective.