Fast chaotic optimization algorithm based on spatiotemporal maps for global optimization

Anis Naanaa

Applied Mathematics and Computation, 2015, vol. 269, issue C, 402-411

Abstract: Recently, many researches have tackled chaos optimization algorithms (COAs) as an attractive method of global optimization. Considering the statistical property such as the probability density function (PDF) of the chaotic sequences, the search ability of COA can improve the global searching capability by escaping the local solutions than classical stochastic optimization algorithms. This paper proposes a novel method for global optimization using spatiotemporal map to improve the performance of the COA. The experimental results of typical nonlinear multimodal benchmark functions optimization show that spatiotemporal COA map (SCOA) improves the convergence and high efficiency compared to five hybrid optimization algorithms, which are the Monte Carlo-BFGS algorithm (MC-BFGS), Logistic map based chaos-BFGS algorithm (LM-BFGS), Skew Tent map based chaos-BFGS algorithm (STM-BFGS), COA based on the Logistic map (LM-COA) and COA based on the Skew Tent map (STM-COA).

Keywords: Chaos optimization algorithms; Spatiotemporal; Chaotic map; Nonlinear test functions (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:269:y:2015:i:c:p:402-411

DOI: 10.1016/j.amc.2015.07.111

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