Abstract: Learning to optimize and automated algorithm design are attracting increasing attention, but it is still in its infancy in constrained multiobjective optimization evolutionary algorithms ...
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Supply chain disruption risk models often separate risk prioritization from mitigation design, limiting their ability to support actionable decision-making under uncertainty. This study addresses this ...
ABSTRACT: Multi-objective optimization remains a significant and realistic problem in engineering. A trade-off among conflicting objectives subject to equality and inequality constraints is known as ...
Multi-objective fuzzy Flexible Job shop Scheduling Problems (MofFJSPs) aim to optimize multiple fuzzy and conflicting objectives by finding the sequences of jobs and machines under realistic ...
In the field of multi-objective evolutionary optimization, prior studies have largely concentrated on the scalability of objective functions, with relatively less emphasis on the scalability of ...
Abstract: dynamic multiobjective optimization (DMO) problems are prevalent in many practical applications and have garnered significant attention from both industry and academia, leading to the ...
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