Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems

Abstract

Abstract. We investigate the performance of the recently proposed Unified Particle Swarm Optimization method on constrained engineering optimization problems. For this purpose, a penalty function approach is employed and the algorithm is modified to preserve feasibility of the encountered solutions. The algorithm is illustrated on four well-known engineering problems with promising results. Comparisons with the standard local and global variant of Particle Swarm Optimization are reported and discussed.

A Cuckoo Search Algorithm with Complex Local Search Method for Solving Engineering Structural Optimization Problem

“…If ) (t K is too large, the objective function is poor in the boundary of the feasible region. Therefore, this paper handles the constraints by adopting a non-fixed multi-segment mapping penalty function method [7]. The approach is as follows.…”

“…The approaches applied to this problem include six different numerical optimization techniques, a standard cuckoo search algorithm(CS) [20] ,a fish swarm optimization algorithm (FSO) [7], a co-evolutionary particle swarm optimization for constrained optimization tasks (CPSO)[21],a bat algorithm(BA) [22], an effective hybrid cuckoo search algorithm for constrained global optimization (HCS-LSAL) [6], and a hybrid nelder-mead simplex search and particle swarm optimization (NM-PSO) [ Table 5, the opti-mum value, the average value and the worst value of the ICS algorithm are better than those of the other 6 algorithms. …”

Desarrollo De Un Proceso De Morfogénesis Basado en Computación Heurística Multi-Objetiva Para Una Estructura Reticular De Cubierta

“…Finalmente, la actualización de la posición de la partícula estará determinada por (Parsopoulos y Vrahatis, 2004): En cuanto a la aplicación de restricciones, éstas se manejaron por medio del acercamiento de función de penalización (Parsopoulos y Vrahatis, 2002b). Este acercamiento se fundamenta en redefinir la función objetivo, en este caso , por medio de la inclusión de una función que penaliza, proporcionalmente, las soluciones que incumplen con las restricciones impuestas; entre más distante esté la solución del cumplimiento de la restricción, el valor de penalización será mayor.…”

Generic Optimization Program User Manual Version 3.0.0

“…present a PSO algorithm for minimax problems [LPV02b] and for integer programming [LPV02a]. In [PV02a], Parsopoulos and Vrahatis discuss the implementation of inequality and equality constraints to solve problem P cg defined in (4.3).…”