Enhancing social emotional optimization algorithm using local search
Many problems in science and engineering can be converted into optimization problems. Social emotional optimization algorithm (SEOA) is a promising optimization technique, which has been successfully applied in various fields . However, it may suffer from slow convergence rate when tackling some complex optimization problems. In order to accelerate the convergence rate, an enhanced social emotional optimization algorithm using local search (ELSEOA) is proposed. In ELSEOA, it utilizes a local search strategy to accelerate the convergence rate. Moreover, ELSEOA conducts the Levy distribution-based emotional simulation strategy to better imitate the emotional changes in the human emotional system. The experimental results over 15 classical test functions show that ELSEOA can achieve better performance than the traditional SEOA and other optimization algorithms on the majority of the test functions.
KeywordsEvolutionary algorithm Global optimization Social emotional optimization Local search
This work was supported in part by the National Natural Science Foundation of China (Nos. 41261093, 41561091, and 61462036), by Natural Science Foundation of Jiangxi, China (Nos. 20151BAB217010 and 20151BAB201015).
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Conflicts of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
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