Feasible Regions Identification based on Historical Solutions for Constrained Optimization Problems
Date Issued
June 8, 2025
Author(s)
Shan, Mengli
China University of Geosciences
Peng, Mai
Liu, Xiaobo
Abstract
The presence of constraints often leads to the formation of narrow and fragmented feasible regions within the search region, presenting significant challenges for optimization problem-solving. This paper introduces a novel approach, Feasible Regions Identification based on Historical Solutions (FRIHS), designed to address these challenges. FRIHS leverages previously evaluated solutions to partition the search region into ε-feasible and ε-infeasible regions. Additionally, by analyzing the correlations among constraints, they are reformulated as auxiliary objectives, effectively transforming the constrained optimization problem into a constrained multi-objective optimization problem. The method employs the classical evolutionary algorithm Differential Evolution and the multi-objective method NSGA-III to search the most promising feasible regions. The effectiveness of FRIHS is evaluated through a comparative analysis with five advanced constraint-handling algorithms across a benchmark test suite. Experimental results indicate that the proposed approach demonstrates competitive performance on the test problems.
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 62476006, in part by the Hubei Provincial Natural Science Foundation of China under Grant 2023AFA049, in part by the Fundamental Research Funds of the AUST under Grant 2024JBZD0007, and in part by the SmartPLAIGO: Advancing Public Health services through AI driven GIS optimization, funded by the European Union Recovery and Resilience Facility of the NextGenerationEU instrument through the Research and Innovation Foundation under the Grant Agreement No CODEVELOPAG-SH-HE/0823/0112.
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