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Conventional particle swarm optimization (PSO) is an appropriate optimization method, yet it suffers from some drawbacks. Trapping in local minimums or premature convergence of particles leads to unsatisfactory levels of optimization. In this paper, a new method for improving PSO is provided. In the proposed method (FAIPSO), the acceleration coefficients c 1 and c 2 are adaptively adjusted for each particle in each iteration. For the adaptive controlling of the acceleration coefficients, a fuzzy inference system is used. This fuzzy inference system comprises six inputs, two outputs, and ten rules. In order to reduce inertia weight (ω), a parabolic model is used. In addition to this, a range of vision (Mu) is defined for each of the particles and every one of the particles searches within this range. This range of vision changes adaptively. In order to adaptively control the range of vision, a fuzzy inference system is employed. This system has two inputs, one output, and 14 rules. To test the proposed method, 16 benchmarks, each inheriting special characteristics, are used. The performance of the proposed method was compared with that of ten types of PSOs (each of which are among the reputable works of the PSO subject). According to the results, the proposed method shows a good performance and is more appropriate than other methods.

This paper presents an exposition of a new method of swarm intelligence-based algorithm for optimization. Modeling swallow swarm movement and their other behavior, this optimization method represents a new optimization method. There are three kinds of particles in this method: explorer particles, aimless particles, and leader particles. Each particle has a personal feature but all of them have a central colony of flying. Each particle exhibits an intelligent behavior and, perpetually, explores its surroundings with an adaptive radius. The situations of neighbor particles, local leader, and public leader are considered, and a move is made then. Swallow swarm optimization algorithm has proved high efficiency, such as fast move in flat areas (areas that there is no hope to find food and, derivation is equal to zero), not getting stuck in local extremum points, high convergence speed, and intelligent participation in the different groups of particles. SSO algorithm has been tested by 19 benchmark functions. It achieved good results in multimodal, rotated and shifted functions. Results of this method have been compared to standard PSO, FSO algorithm, and ten different kinds of PSO. Refereed/Peer-reviewed