Enhancing social emotional optimization algorithm using local search
- 335 Downloads
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).
Compliance with ethical standards
Conflicts of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
- Cui Z, Cai X (2010) Using social cognitive optimization algorithm to solve nonlinear equations. In: 9th IEEE International Conference on Cognitive Informatics (ICCI), p 199–203Google Scholar
- Cui Z, Shi Z , Zeng J (2010) Using social emotional optimization algorithm to direct orbits of chaotic systems. In: Swarm, Evolutionary, and Memetic Computing, p 389–395Google Scholar
- Guo Z, Huang H, Deng C, Yue X, Wu Z (2015a) An enhanced differential evolution with elite chaotic local search. Comput Intell Neurosci 11Google Scholar
- Li X, Cui Z (2012) Using nw small-world model to improve the performance of social emotional optimization algorithm. In: Proceedings of 2012 International Conference on Modelling, Identification and Control (ICMIC), p 1123–1128Google Scholar
- Ram G, Mandal D, Kar R, Ghosal SP (2014) Social emotional optimization algorithm for beamforming of linear antenna arrays. In: TENCON 2014-2014 IEEE Region 10 Conference, p 1–5Google Scholar
- Shen J, Tan H, Wang J, Wang J, Lee S (2015) A novel routing protocol providing good transmission reliability in underwater sensor networks. J Internet Technol 16(1):171–178Google Scholar
- Wei Z, Cui Z, Zeng J (2012) Social emotional optimisation algorithm with emotional model. Int J Comput Sci Eng 7(2):125–132Google Scholar
- Wu J, Cui Z, Liu J (2011) A hybrid social emotional optimization algorithm with metropolis rule. In: Proceedings of 2011 International Conference on Modelling, Identification and Control (ICMIC), p 363–370Google Scholar
- Xia Z, Wang X, Sun X, Liu Q, Xiong N (2014a) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimedia Tools and Applications, p 1–16Google Scholar
- Xu Y, Cui Z, Zeng J (2010) Social emotional optimization algorithm for nonlinear constrained optimization problems. In: Swarm, Evolutionary, and Memetic Computing, p 583–590Google Scholar
- Zheng Y, Jeon B, Xu D, Wu QM, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy c-means algorithm. J Intell Fuzzy Syst 28(2):961–973Google Scholar