Open Access

ARTICLE

Language Education Optimization: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

by Pavel Trojovský^1,*, Mohammad Dehghani¹, Eva Trojovská¹, Eva Milkova²

1 Department of Mathematics, Faculty of Science, University of Hradec Králové, Hradec Králové, 500 03, Czech Republic
2 Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, Hradec Králové, 500 03, Czech Republic

* Corresponding Author: Pavel Trojovský. Email:

(This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)

Computer Modeling in Engineering & Sciences 2023, 136(2), 1527-1573. https://doi.org/10.32604/cmes.2023.025908

Received 05 August 2022; Accepted 19 October 2022; Issue published 06 February 2023

Abstract

In this paper, based on the concept of the NFL theorem, that there is no unique algorithm that has the best performance for all optimization problems, a new human-based metaheuristic algorithm called Language Education Optimization (LEO) is introduced, which is used to solve optimization problems. LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules. LEO is mathematically modeled in three phases: (i) students selecting their teacher, (ii) students learning from each other, and (iii) individual practice, considering exploration in local search and exploitation in local search. The performance of LEO in optimization tasks has been challenged against fifty-two benchmark functions of a variety of unimodal, multimodal types and the CEC 2017 test suite. The optimization results show that LEO, with its acceptable ability in exploration, exploitation, and maintaining a balance between them, has efficient performance in optimization applications and solution presentation. LEO efficiency in optimization tasks is compared with ten well-known metaheuristic algorithms. Analyses of the simulation results show that LEO has effective performance in dealing with optimization tasks and is significantly superior and more competitive in combating the compared algorithms. The implementation results of the proposed approach to four engineering design problems show the effectiveness of LEO in solving real-world optimization applications.

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