Abstract
Intelligent manufacturing is an important part of Industry 4.0; artificial intelligence technology is a necessary means to realize intelligent manufacturing. This requires the exploration of pattern recognition, computer vision, intelligent optimization, and other related technologies. Particle swarm optimization (PSO) algorithm is an optimization algorithm inspired by the foraging behavior of birds. PSO was an intelligent technology and an efficient optimization algorithm verified by a lot of research and experiments. In this paper, the traditional PSO algorithm is compared with genetic algorithms (GA) to illustrate the performance of the traditional PSO algorithm. By analyzing the advantages and disadvantages of the traditional PSO algorithm, the traditional PSO algorithm is improved through introducing into the sharing information mechanism and the competition strategy, called information sharing based PSO (IPSO). The novel algorithm IPSO was the rapid convergence speed similar to the traditional PSO and enhanced the global search capability. Our experimental results show that IPSO has better performance than the traditional PSO and the GA algorithm on benchmark functions, especially for difficult functions.
Highlights
Particle swarm optimization (PSO) algorithm was a bioinspired intelligent technology proposed by Kennedy in 1995 [1]
Since particles in the passive direction are not always worse than those in the active direction, the new algorithm will select the best particle from active and passive directions for the update operation. en a competitive strategy is introduced into the new algorithm. is competitive strategy prompts the worst information to compete with the best. is makes the algorithm reduce the probability of falling into the local optimum solution, and can increase the probability of converging to the optimal solution, and the time complexity is only double that of the traditional PSO algorithm
On the rest three difficult functions, F4, F5, and F6, our information sharing based PSO (IPSO) significantly improves the success rate of finding the optimal solution
Summary
Particle swarm optimization (PSO) algorithm was a bioinspired intelligent technology proposed by Kennedy in 1995 [1]. It was a metaheuristic optimization inspired by the foraging behavior of birds [2]. When the PSO algorithm was first published, it attracted extensive attention from the optimization field scholars. Before long, it became the focus of research in the optimization field. Wu et al propose a swarm-intelligence-based method-Particle Swarm Optimization (PSO) algorithm to handle the elastic parameter inversion problem. Gong et al proposed a discrete framework of the particle swarm optimization algorithm. Based on the proposed discrete framework, a multiobjective discrete particle swarm optimization algorithm is proposed to solve the network clustering problem. Wu et al introduced particle swarm optimization algorithm into KNN multilabel classification and made adjustments to Euclidean distance
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