A Hybrid Particle Swarm Optimization Algorithm for Dynamic Environments

Abstract:

Chaos is a non-linear phenomenon that widely exists in the nature. Due to the ease of implementation and its special ability to avoid being trapped in local optima, chaos has been a novel optimization technique and chaos-based searching algorithms have aroused intense interests. Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in static environments. When the particle swarm optimization (PSO) algorithm is used in dynamic multi-objective problems, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, a hybrid PSO algorithm based on chaos algorithm is brought forward. The hybrid PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the chaos algorithm. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on moving peaks benchmark.

• Periodical: Advanced Materials Research (Volumes 926-930)
• Pages: 3338-3341
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.926-930.3338
• Online since: May 2014
• Authors: Hong Mei Ni*, Zhian Yi, Jin Yue Liu
• Keywords: Chaos, Dynamic Environments, Particle Swarm Optimization Algorithm

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