Journal Article10.1016/j.matcom.2024.11.009
Competition-based two-stage evolutionary algorithm for constrained multi-objective optimization
Lupeng Hao,Weihang Peng,Junhua Liu,Wei Zhang,Yuan Li,Kirby R. Qin +5 more
About: This article is published in Mathematics and Computers in Simulation. The article was published on 01 Nov 2024. The article focuses on the topics: Stage (stratigraphy) & Computer science.
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References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach
Himanshu Jain,Kalyanmoy Deb +1 more
TL;DR: This paper extends NSGA-III to solve generic constrained many-objective optimization problems and suggests three types of constrained test problems that are scalable to any number of objectives and provide different types of challenges to a many- objective optimizer.
Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization
TL;DR: A parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multiobjective optimization that maintains two collaborative archives simultaneously and develops a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status.
A Coevolutionary Framework for Constrained Multiobjective Optimization Problems
TL;DR: A coevolutionary framework for constrained multiobjective optimization, which solves a complex CMOP assisted by a simple helper problem and is compared to several state-of-the-art algorithms tailored for CMOPs.
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Comparison of multi-objective optimization methodologies for engineering applications
TL;DR: Four multi-objective optimization techniques are analyzed by describing their formulation, advantages and disadvantages and the effectiveness of the selected techniques for engineering design purposes is verified by comparing the results obtained by solving a few benchmarks and a real structural engineering problem concerning an engine bracket of a car.
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