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An adaptive uniform search framework for constrained multi-objective optimization ☆
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AI
DOI：10.1016/j.asoc.2024.111800.png)
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Abstract
En 中文
This paper proposes an adaptive uniform search framework designed for constrained multi -objective optimization. The framework comprises three key components: a global uniform exploration strategy, a local greedy exploitation strategy, and a search switch mechanism. These components work together to facilitate comprehensive exploration of promising areas while maintaining a balance between global exploration and local exploitation. Specifically, the global uniform exploration strategy ensures even distribution within promising areas, preventing any oversights during exploration. The local greedy exploitation strategy divides these areas into sub -areas and employs a feasibility -led constraint handling technique to enhance efficiency in identifying optimal solutions. Additionally, the search switch dynamically adjusts the search strategy between global exploration and local exploitation. Numerical simulations on various benchmark suites and real -world problem demonstrate the strong performance of the framework in addressing constrained multi -objective optimization problems. The comparison results show that compared with eight recently proposed algorithms, the proposed framework is more robust in solving diverse constrained multi -objective optimization problems.
Keywords:
Uniform search
Constraint handling
Multi-objective optimization
Evolutionary algorithm