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).


The produced results for the benchmarks and engineering problems show that in general the accuracy and diversity of the proposed algorithm are better compared to the MOWCA and MODA. However, the NSGA-II outperformed the proposed work in some of the cases and showed better accuracy and diversity. Nevertheless, in problems, such as coil compression spring design problem, the quality of solutions produced by the proposed algorithm outperformed all the participated algorithms. Moreover, in regard to the processing time, the proposed work provided better results compared with all the participated algorithms.

Professor Tarik A. Rashid
University of Kurdistan Hewler

Read the Original

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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