What is it about?
In this work, a new multi-objective optimization algorithm called multi-objective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of moving graduated students from high school to college. The proposed technique produces a set of non-dominated solutions. To test the ability and efficacy of the proposed multi-objective algorithm, it is applied to a group of benchmarks and five real-world engineering optimization problems.
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Why is it important?
Several widely used metrics are employed in the quantitative statistical comparisons. The proposed algorithm is compared with three multi-objective algorithms: Multi-Objective Water Cycle Algorithm (MOWCA), Non-dominated Sorting Genetic Algorithm (NSGA-II), and Multi-Objective Dragonfly Algorithm (MODA).
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This page is a summary of: Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems, Neural Computing and Applications, January 2022, Springer Science + Business Media,
DOI: 10.1007/s00521-021-06811-z.
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