A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems based on Preschool Education

Abstract

Abstract This paper presents the Preschool Education Optimization Algorithm (PEOA), a novel metaheuristic algorithm designed to tackle optimization problems. Inspired by the concept of preschool education, PEOA is divided into three distinct phases (i) the gradual growth of the preschool teacher's educational influence, (ii) individual knowledge development guided by the teacher, and (iii) individual increase of knowledge and self-awareness. The algorithm's effectiveness in optimization is evaluated using twenty-three standard benchmark functions encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types. The results demonstrate that PEOA excels in exploration, exploitation, and maintaining a balance between them throughout the optimization process. To provide a comprehensive analysis, the performance of PEOA is compared against ten well-known metaheuristic algorithms. The simulation results reveal that PEOA consistently outperforms the compared algorithms, yielding superior outcomes for the majority of benchmark functions. Furthermore, the implementation of PEOA in solving four engineering design problems illustrates its efficacy in real-world optimization applications.