Fast chaotic optimization algorithm based on spatiotemporal maps for global optimization

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).