On a New Human-Based Metaheuristic Optimization Algorithm Based on Imitating Foreign Language Education Process

Abstract

The paper presents a new human-based metaheuristic algorithm called Language Education Optimization (LEO). 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 twenty-three benchmark functions of a variety of unimodal and multimodal types. The optimization results show that LEO, with its acceptable ability to explore, exploit, and maintain a balance between them, has efficient performance in optimization applications and in 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.