Abstract
In this paper, the independent convergent and non-convergent decision variables are firstly obtained by analyzing the contribution of decision variables to the objective function based on the existing research results of multi-objective optimization algorithms. Secondly, according to their characteristics, the multi-population is employed, so that the population can search the corresponding multiple Pareto optimal solution set in each individual environment. Then, when the problem changes, two more targeted response strategies are proposed for different types of decision variables and their effects on the objective function. As the environment changes, the algorithm can ensure the rapid convergence of the population in the objective space, while maintaining the diversity of the population in the decision space and the objective space. Therefore, the proposed algorithm has the ability of quickly respond to the change of the problem and maintain the diversity of the solution set.
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Acknowledgements
This work was jointly supported by National Key R&D Program of China (2021ZD0111502), National Natural Science Foundation of China (51907112, U2066212), Jiangxi"Double Thousand Plan" Project (JXSQ20210019), Natural Science Foundation of Guangdong Province of China (2021A1515011709), Scientific Research Staring Foundation of Shantou University (NTF20009). The funding body have played a role in the purchase of experimental equipment and expert consultation.
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Xu, B., Chen, Y., Li, K., Fan, Z., Gong, D., Bao, L. (2023). Dynamic Multi-modal Multi-objective Evolutionary Optimization Algorithm Based on Decomposition. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_31
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