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Variable-universe fuzzy logic controller-based constrained multi- and many-objective evolutionary algorithm
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DOI：10.1007/s12293-026-00516-3.png)
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Abstract
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The $$\varepsilon $$ constraint method is an important technique for handling constraints in constrained optimization problems. The proper selection of $$\varepsilon $$ is highly dependent on the population distribution in each generation, making it exceedingly difficult to derive an analytical expression for $$\varepsilon $$ . Consequently, this nonlinear dependency inevitably compromises the performance of conventional $$\varepsilon $$ -constraint methods. To tackle this issue, we incorporate the concept of variable universes of discourse into a fuzzy logic controller. In this paper, we propose a variable-universe fuzzy logic controller (C-VUFC) to adaptively determine $$\varepsilon $$ . By exploiting fuzzy if-then rules and membership functions, the proposed controller explicitly models the inherently nonlinear relationship between $$\varepsilon $$ and the population distribution. Furthermore, we develop a domain adaptation mechanism that dynamically adjusts the universes of discourse based on historical constraint violation trends, thereby improving the adaptability of $$\varepsilon $$ selection. Therefore, the proposed approach outperforms existing methods. Two novel algorithms are designed by integrating the proposed controller. The proposed algorithms are compared with state-of-the-art algorithms and the results demonstrate their effectiveness.
Keywords:
Constrained Optimization
Multi-objective Optimization
Evolutionary Algorithms
Fuzzy Logic Controller
Journal
IF:
2.3
Papers:
447
Citations:
718
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Cited Papers