Abstract
This work proposes an enhanced particle swarm optimization scheme that improves upon the performance of the standard particle swarm optimization algorithm. The proposed algorithm is based on chaos search to solve the problems of stagnation, which is the problem of being trapped in a local optimum and with the risk of premature convergence. Type1′′constriction is incorporated to help strengthen the stability and quality of convergence, and adaptive learning coefficients are utilized to intensify the exploitation and exploration search characteristics of the algorithm. Several well known benchmark functions are operated to verify the effectiveness of the proposed method. The test performance of the proposed method is compared with those of other popular population-based algorithms in the literature. Simulation results clearly demonstrate that the proposed method exhibits faster convergence, escapes local minima, and avoids premature convergence and stagnation in a high-dimensional problem space. The validity of the proposed PSO algorithm is demonstrated using a fuzzy logic-based maximum power point tracking control model for a standalone solar photovoltaic system.
Highlights
Swarm intelligence is becoming one of the hottest areas of research in the field of computational intelligence especially with regard to self-organizing and decentralized systems
These codes correspond to the proposed Particle swarm optimization (PSO), the standard PSO, the firefly algorithm (FA) [55, 56], ant colony optimization (ACO) [57, 58], and differential evolution (DE) [59, 60], which are evaluated using the benchmark test functions for comparative purposes
This paper presents a novel technique with promising new features to enhance the performance and robustness of the standard PSO for solving optimization problems
Summary
Swarm intelligence is becoming one of the hottest areas of research in the field of computational intelligence especially with regard to self-organizing and decentralized systems. Stagnation occurs if the majority of particles are concentrated at the best position that is disclosed by the neighbors or the swarm This fact has in recent years motivated various investigations by several researchers on variants of the particle swarm optimization, in an attempt to improve the performance of exploitation and exploration and to eliminate the aforementioned problems. (a) The particles may be positioned in a region that has a lower quality index than previously, leading to a risk of premature convergence, trapping in local optima, and the impossibility of further improvement of the best positions of the particles because the inertia weight, cognitive factors, and social learning factors in the algorithm are not adaptive or self-organizing. (c) Self-organizing, adaptive cognitive, and social learning coefficients [41] are integrated to improve the exploitation and exploration search of the particle swarm optimization algorithm.
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