A highly efficient particle swarm optimizer for super high-dimensional complex functions optimization

Abstract

Because of the complexity of super high-dimensional complex functions with the large numbers of global and local optima, the general particle swarm optimization methods are slow speed on convergence and easy to be trapped in local optima. In this paper, a highly efficient particle swarm optimizer is proposed, which employ the adaptive strategy of inertia factor, global optimum,search space and velocity in each cycle to plan large-scale space global search and refined local search as a whole according to the fitness change of swarm in optimization process of the functions, and to quicken convergence speed, avoid premature problem, economize computational expenses, and obtain global optimum. We test the new algorithm and compare

Predatory Search-based Chaos Turbo Particle Swarm Optimisation (PS-CTPSO): A new particle swarm optimisation algorithm for Web service combination problems

“…The main approach to improve the PSO algorithm, focuses on the optimisation of its performance by dynamically adjusting the search step length, as well as optimising the particle update strategy [10,11]. Another approach involves the combination of the PSO algorithm with other intelligent optimisation algorithms, such as genetic algorithm, ant colony algorithm, simulated annealing algorithm, etc.…”

CS-PSO: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems

“…With fewer parameters, PSO algorithm can achieve a faster convergence, while being simpler and easier to implement (Xu et al 2012). PSO has already been applied to many fields, such as electric power systems, job scheduling of workshops, wireless sensor networks, route planning, and robotics (Lei 2014; Kumari and Jha 2014;Yao et al 2012;Liao et al 2012;Lee and Kim 2013). However, the performance of PSO still has space for improvement.…”