An improved PSO algorithm for solving nonlinear programing problems with constrained conditions

• Published in: International journal of modeling, simulation and scientific computing Vol. 12; no. 1; p. 2150001
• Main Author: Chang, Wei-Der
• Format: Journal Article
• Language: English
• Published: Hackensack World Scientific Publishing Company 01.02.2021; World Scientific Publishing Co. Pte., Ltd
• Subjects: Algorithms; Design optimization; Linear programming; Nonlinear programming; Optimization; Particle swarm optimization; improved particle swarm optimization (IPSO); nonlinear programming; Engineering optimization; constrained conditions
• ISSN: 1793-9623; 1793-9615
• DOI: 10.1142/S179396232150001X

Engineering optimization problems can be always classified into two main categories including the linear programming (LP) and nonlinear programming (NLP) problems. Each programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized system. This paper will focus on the issue about the design problem of NLP with the constrained conditions. The employed method for such NLP problems is a variant of particle swarm optimization (PSO), named improved particle swarm optimization (IPSO). The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization problems. In this work, many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO algorithm. Simulation results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions.