Open Access

ARTICLE

Migration Algorithm: A New Human-Based Metaheuristic Approach for Solving Optimization Problems

by Pavel Trojovský^*, Mohammad Dehghani

Department of Mathematics, 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, 137(2), 1695-1730. https://doi.org/10.32604/cmes.2023.028314

Received 12 December 2022; Accepted 16 February 2023; Issue published 26 June 2023

Abstract

This paper introduces a new metaheuristic algorithm called Migration Algorithm (MA), which is helpful in solving optimization problems. The fundamental inspiration of MA is the process of human migration, which aims to improve job, educational, economic, and living conditions, and so on. The mathematical modeling of the proposed MA is presented in two phases to empower the proposed approach in exploration and exploitation during the search process. In the exploration phase, the algorithm population is updated based on the simulation of choosing the migration destination among the available options. In the exploitation phase, the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions. MA’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite. In addition, MA’s results are compared with the performance of twelve well-known metaheuristic algorithms. The optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems. The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions. Also, the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications.

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