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 of PSO algorithm in high-dimensional search space, some algorithms such as Cooperative PSO offered. Accordingly, in the present article, we intend, in order to develop and improve PSO algorithm take advantage of some optimization methods such as Cooperatives PSO, Comprehensive Learning PSO and fuzzy logic, while enjoying the benefits of some functions and procedures such aslocal search function and Coloning procedure, propose the Enhanced Comprehensive Learning Cooperative Particle Swarm Optimization with Fuzzy Inertia Weight (ECLCFPSO-IW) algorithm. By proposing this algorithm we try to improve mentioned deficiencies of PSO and get better performance in high dimensions.
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