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Particle Swarm Optimization Algorithm Based on Chaotic Theory and Adaptive Inertia Weight

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Fixed inertia weight and premature convergence are obvious flaws of particle swarm algorithm. On the basis of a detailed analysis of the relationship among the inertia weight, population size, particle fitness and search space dimension, a dynamic adaptive adjustment strategy for inertia weight is proposed, which effectively enhances the global and local optimization ability of the algorithm. For premature problem, the chaotic mapping method is used to increase the diversity of the population, while taking advantage of the negative gradient direction to adjust group extreme, greatly reducing the probability of fall into the local extreme. The correctness and effectiveness of the proposed algorithm are demonstrated by comparison with other algorithms on multiple test functions commonly used.
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Keywords: ADAPTIVE; CHAOTIC; INERTIA WEIGHT; PARTICLE SWARM OPTIMIZATION ALGORITHM

Document Type: Research Article

Publication date: April 1, 2017

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  • Journal of Nanoelectronics and Optoelectronics (JNO) is an international and cross-disciplinary peer reviewed journal to consolidate emerging experimental and theoretical research activities in the areas of nanoscale electronic and optoelectronic materials and devices into a single and unique reference source. JNO aims to facilitate the dissemination of interdisciplinary research results in the inter-related and converging fields of nanoelectronics and optoelectronics.
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