Open Access
ARTICLE
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.
Keywords
Cite This Article
Trojovský, P., Dehghani, M. (2023). Migration Algorithm: A New Human-Based Metaheuristic Approach for Solving Optimization Problems.
CMES-Computer Modeling in Engineering & Sciences, 137(2), 1695–1730.