Abstract
For multiobjective optimization problems with large-scale decision variables, it is difficult to optimize all the decision variables at the same time. With the divide and conquer strategy, the decision variable analysis technique is applied to analyze the variables’ property and divide the variables into subcomponents. However it takes too much time to analyze a large-scale set of decision variables. In this paper, we propose a distributed decision variable analysis algorithm. The proposed algorithm divides all the variables into subcomponents assigns each of them to a computation node. We test the proposed algorithm on some popular multiobjcetive optimization problems with large-scale decision variables and the results show that the proposed algorithm can boost the analysis process effectively.
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Wang, Z., Gong, M., Xie, T. (2016). Decision Variable Analysis Based on Distributed Computing. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_40
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DOI: https://doi.org/10.1007/978-981-10-3611-8_40
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