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In this paper, a particle swarm optimization method with a new strategy for inertia weight has been considered. The author abandoned the commonly used linear inertia weight and proposed a new dynamic inertia weight based on fitness of the particles. The new weight is a function of the best and the worst fitness of the particles. The considered NIWPSO algorithm was tested on a set of benchmark functions...
Firefly algorithm (FA), first put forward by a Cambridge scholar Yang, is a kind of swarm intelligent algorithm imitating nature fireflies' predation and courtship behaviour. It has been widely studied and gradually applied to different engineering fields. However, the basic FA has several shortcomings such as easiness to fall into local optimum, premature convergence, et al. Recent years have witnessed...
Particle Swarm Optimization (PSO) is an evolution-nary computation technique. Separate adjustment to inertia weight and learning factors in PSO undermines the integrity and intelligent characteristic in the evolutionary process of particle swarm to some extent, thus it is not suitable for solving most complicated optimization problems. On the basis of previous researches, the aim of this study was...
So far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improving defects of PSO such as trapping in local optimums and early convergence has been done. Moreover to overcome the problem of inefficiency...
This paper presents a human cognition inspired particle swarm optimization algorithm, and is referred as Cognition Inspired Particle Swarm Optimization (CIPSO). As suggested by the human learning psychology, the particles control the cognition based on their global performance and also the social cognition does not influence one-self directly based on his current knowledge. Hence, in the proposed...
Inspired from social behavior of organisms such as bird flocking, particle swarm optimization(PSO) has rapid convergence speed and has been successfully applied in many optimization problems. in this paper, we present a dynamic particle swarm optimization algorithm to enhance the performance of standard PSO. We design a novel function to compute the initial dynamic inertia weight, and then calculate...
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