Abstract
The main feature of large-scale multi-objective optimization problems (LSMOP) is to optimize multiple conflicting objectives while considering thousands of decision variables at the same time. Since the purpose of effective LSMOP algorithm is escaping from local optimum in large search space, the current research is focused on decision variable analysis or grouping, which easily leads to excessive computational complexity due to the large-scale decision variables. In order to maintain the diversity of the population while avoiding the computational complexity caused by large-scale decision variables, we propose a Probabilistic Prediction Model based on trend prediction model (TPM) and Generating-Filtering strategy to tackle LSMOP. Since TPM has an individual-based evolution mechanism, the computational complexity of the proposed algorithm is independent of decision variables, which maintains low complexity of the evolutionary algorithm while ensuring that the algorithm can converge to the Pareto optimal Front(POF). We compared the proposed algorithm with several state-of-the-art algorithms for different benchmark functions. The experimental results and complexity analysis have demonstrated that the proposed algorithm has significant improvement in terms of its performance and computational efficiency in large-scale multi-objective optimization.
This work was supported by the National Natural Science Foundation of China (No. 61673328).
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This work was supported by the National Natural Science Foundation of China (No. 61673328).
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Hong, H., Ye, K., Jiang, M., Tan, K.C. (2021). Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_48
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