Abstract
The standard particle swarm optimization (PSO) algorithm is the boundary constraints of simple variables, which can hardly be directly applied in the constrained optimization. Furthermore, the standard PSO algorithm often fails to obtain the global optimal solution when the dimensionality is high for unconstrained optimization. Thus, an improved PSO-based extended domain method (IPSO-EDM) is proposed to solve engineering optimization problems. The core idea of this method is that the original feasible region is expanded in the constrained optimization which is transformed into the unconstrained optimization by combining the ergodicity of chaos optimization and the evolutionary variation to realize global search. In addition, to verify the effectiveness of the IPSO-EDM, an unconstrained optimization case study, four constrained optimization case studies, and one engineering example are investigated. The results indicate that the computational accuracy of the IPSO-EDM is comparable to that provided by the existing literature, and the computational efficiency of the IPSO-EDM is significantly improved. Meanwhile, this method has conspicuous global search ability and stability in engineering optimization.
Highlights
Optimization began in the 17th century, which originated from differential and integral calculus invented by Newton and Leibnitz. en, optimization algorithms [1,2,3,4,5] were rapidly developed, such as artificial neural network, simulated annealing, genetic algorithm, ant colony optimization, and particle swarm optimization (PSO)
(1) e original feasible region is expanded, and some points that are closer to the constrained optimal point in the feasible region are contained as feasible points, which provide preferable function information compared with the points in the original feasible region. is approach uses the current location information of the particles and particle swarm to determine the speed of the particles
(2) e constrained optimization is transformed into the unconstrained optimization by combining the ergodicity of chaos optimization and the evolutionary variation to realize global search. e logistic chaotic system equation is applied in the PSO algorithm, and the mutation operator is introduced in the evolutionary variation strategy to escape local optimal and maintain its vitality of the particle swarm, which prevents the particle swarm from falling into the condition of “precocity” at the earlier iteration
Summary
Optimization began in the 17th century, which originated from differential and integral calculus invented by Newton and Leibnitz. en, optimization algorithms [1,2,3,4,5] were rapidly developed, such as artificial neural network, simulated annealing, genetic algorithm, ant colony optimization, and particle swarm optimization (PSO). En, optimization algorithms [1,2,3,4,5] were rapidly developed, such as artificial neural network, simulated annealing, genetic algorithm, ant colony optimization, and particle swarm optimization (PSO) All these methods were widely used in different fields [6,7,8,9,10,11,12], such as chemical engineering, biomedicine, navigation, robot, automobile, architecture, and aerospace. Yi et al [19] presented a parallel chaotic local search algorithm to solve constrained engineering design problems. Based on the above research studies, a new methodology named improved particle swarm optimization-based extended domain method (IPSO-EDM) is proposed to investigate engineering optimization.
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