Abstract
Multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) decomposes an multiobjective optimization problem into a number of single-objective subproblems and solves them in a cooperative manner. The subproblems can be designed by various scalarization methods, e.g., the weighted sum (WS) method, the Tchebycheff (TCH) method, and the penalty-based boundary intersection (PBI) method. In this paper, we investigate the PBI method with different parameter settings, and propose a way to set the parameter appropriately. Experimental results suggest that the PBI method with our proposed parameter setting works very well.
This work was supported by the National Natural Science Foundation of China (Grant No. 11991023, 62076197, 61876163 and 62072364) and the Key Project of Science and Technology Innovation 2030 sponsored by the Ministry of Science and Technology of China (Grant No. 2018AAA0101301).
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Notes
- 1.
\(F'\) may not be in the feasible objective region. But we are seeking for a common principle, so we have to take the situation into consideration where there is an MOP such that \(F'\) is in the feasible objective region.
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Wang, Z., Deng, J., Zhang, Q., Yang, Q. (2021). On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D. 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_33
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