Abstract
Problem decomposition and subcomponent optimization play a key role in cooperative coevolution (CC) for large scale global optimization. In this paper, we firstly introduce a new variable interactions identification (VII) method to recognize the indirect decision variables. Then, we proposed a new reallocate computational resources method, aims to give more computational resources to the more important subcomponents. Hence, a novel ITÖ algorithm with cooperative coevolution (CCITÖ) strategy based on above two strategies is proposed. In order to understand the characteristics of CCITÖ, we have carried out extensive computational studies on the CEC’2010 benchmark function. Experimental results show that our algorithm achieves competitive results compared with other four state-of-the-art algorithms in the large scale global optimization problems.
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Wang, Y., Dong, W., Dong, X. (2018). ITÖ Algorithm with Cooperative Coevolution for Large Scale Global Optimization. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-13-1651-7_4
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DOI: https://doi.org/10.1007/978-981-13-1651-7_4
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