New Multiobjective PSO Algorithm for Nonlinear Constrained Programming Problems Export

@incollection{citeulike:3280431, abstract = {A new approach is presented to solve nonlinear constrained programming problems (NLCPs) by using particle swarm algorithm (PSO). It neither uses any penalty functions, nor distinguish the feasible 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 prefer to keep the ratio of infeasible solutions so as to increase the diversity of swarm and avoid the defect 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 dynamically changing inertia weight. The numerical experiment shows that the algorithm is effective.}, author = {Liu, Chun-An}, citeulike-article-id = {3280431}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-1-4020-8387-7_165}, doi = {10.1007/978-1-4020-8387-7_165}, journal = {Advances in Cognitive Neurodynamics ICCN 2007}, keywords = {constraint-programming, multi-objective, particle-swarm-optimization}, pages = {955--962}, posted-at = {2008-09-17 15:37:16}, priority = {2}, title = {New Multiobjective PSO Algorithm for Nonlinear Constrained Programming Problems}, url = {http://dx.doi.org/10.1007/978-1-4020-8387-7_165}, year = {2008} }

TY - CHAP ID - citeulike:3280431 L3 - citeulike-article-id:3280431 N2 - A new approach is presented to solve nonlinear constrained programming problems (NLCPs) by using particle swarm algorithm (PSO). It neither uses any penalty functions, nor distinguish the feasible 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 prefer to keep the ratio of infeasible solutions so as to increase the diversity of swarm and avoid the defect 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 dynamically changing inertia weight. The numerical experiment shows that the algorithm is effective. JF - Advances in Cognitive Neurodynamics ICCN 2007 EP - 962 TI - New Multiobjective PSO Algorithm for Nonlinear Constrained Programming Problems SP - 955 KW - constraint-programming KW - multi-objective KW - particle-swarm-optimization AU - Liu, Chun-An PY - 2008/// UR - http://dx.doi.org/10.1007/978-1-4020-8387-7_165 ER -