Abstract
In this paper, a well performing approach in the context of Multi-Objective Evolutionary Algorithm (MOEA) is investigated due to its complexity. This approach called NSCCGA is based on previously introduced approach called NSGA-II. NSCCGA performs better than NSGA-II but with a heavy load of computational complexity. Here, a novel approach called GBCCGA is introduced based on MOCCGA with some modifications. The main difference between GBCCGA and MOCCGA is in their niching technique which instead of the traditional sharing mechanism in MOCCGA, a novel grid-based technique is used in GBCCGA. The reported results show that GBCCGA performs roughly the same as NSCCGA but with very low computational complexity with respect to the original MOCCGA.
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Fard, S.M., Hamzeh, A., Ziarati, K. (2009). A Grid Based Cooperative Co-evolutionary Multi-Objective Algorithm. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_19
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DOI: https://doi.org/10.1007/978-3-642-05253-8_19
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