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A Constraint Priority Decision framework for constrained multi-objective optimization with complex feasible regions
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DOI：10.1016/j.asoc.2025.112873.png)
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Abstract
En 中文
Constrained multi-objective optimization problems (CMOPs) present significant challenges due to the simultaneous consideration of objectives and constraints, which becomes particularly arduous when the feasible regions are exceedingly complex. Most of the existing algorithms fail to obtain high-quality solutions for the CMOPs with complex feasible regions. To address this issue, this paper proposes a Constraint Priority Decision framework applied to multi-stage evolutionary algorithms, which incorporates constraints sequentially throughout the solution process to facilitate the retention of optimal diversity and feasibility within the population. Specifically, the proposed framework employs a traditional multi-objective evolutionary algorithm as the optimizer and decomposes various constraints of the CMOP. These constraints are introduced independently into the optimizer, generating an index value for each respective constraint. Following this, a judgment matrix is constructed based on these indices to grade the constraints, thus facilitating the establishment of a priority sequence for multiple constraints. Furthermore, a two-stage strategy is implemented in this study. After incorporating all constraints into the algorithm, the epsilon-constrained method is utilized to impose constraints on the entire problem to increase the genetic diversity of the population while maintaining the feasibility of the population. The experimental results derived from four popular benchmark suites and six real-world applications indicate that the proposed framework surpassed multiple state-of-the-art constrained multi-objective evolutionary algorithms in addressing CMOPs with complex feasible regions.
Keywords:
Constraint-handling priority
Constrained multi-objective optimization
Evolutionary algorithm
Analytic hierarchy process
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