Optimization of the Distance Between Swarms Using Soft Computing
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Optimization of the Distance Between Swarms Using Soft Computing Savita Kumari1 · Brahmjit Singh1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Particle swarm optimization (PSO) is a dynamic nature-influenced optimization technique. PSO optimization technique can resolve the best solution in minimum iterations and operates more effectively and efficiently. But, the other optimization techniques like particle swarm optimization with passive congregation (PSOPC) technique and dissipative particle swarm optimization (DPSO) technique gives better solution in fewer iterations as compared to PSO. In this paper, the distance between swarms is optimized and compared to all the optimization techniques. Simulation results demonstrate that the PSOPC optimization technique delivers better results than the PSO and DPSO optimization techniques. Keywords Particle · Swarm · Optimization · Velocity · Distance
1 Introduction If a system consists of inputs and produces outputs after performing certain functions, then by constructing a mathematical model of the system, an optimization issue is generated and an optimization technique would be enforced for the result. Various algorithms have been developed to resolve difficult problems and an optimization technique may be preferred [1] accounting of factors such as the behavior of the functions, difficulty of the problem, and computational power. Nature is generally helped for motivation in optimization techniques such as PSO optimization technique [2], spider monkey optimization technique [3], ant colony optimization technique [4], lion optimization technique [5], bat optimization algorithm technique [6], genetic algorithm [7], etc. All these techniques are related to nature-motivated optimization techniques. The number of iterations is required to be implemented, to find out which optimization technique is better to resolve optimization issue. PSO optimization technique is a dynamic nature- influenced optimization technique. PSO optimization technique was initially proposed for inventing social behavior, but the optimization technique was interpreted and it was concluded that the particles were really achieving optimization. The advantage of PSO optimization technique is to solve difficult problems with fast convergence rate and it is simple to implement. After * Savita Kumari [email protected] 1
Department of Electronics and Communication Engineering, NIT Kurukshetra, Haryana, India
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S. Kumari, B. Singh
comparison with the other optimization methods, the result proves that PSO technique is more efficient to achieve best solution [8–12]. Based on the control parameters in PSO optimization technique, the execution of the optimization technique is transformed [13], [14]. To detect the global best solution [15], the PSO optimization technique requires the number of function evaluations. So, the PSO optimization technique is ineffective at many times. Thus, a modification in the PSO optimization technique is nece
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