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

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.

Focus and Shake Algorithm: A New Stochastic Optimization Employing Strict and Randomized Dimension Mappings

“…In the strict acceptance, a new solution is accepted only if it provides enhancement. This option can be found in many metaheuristics, such as MA [19], TIA [28], and so on. In the non-strict acceptance, a new solution is accepted without considering the relative quality between the new solution and the current solution.…”

“…It means that each agent searches inside the search space based on the direction of a reference. FSA is not embedded with the neighborhood search as it is commonly found in several new swarm-based metaheuristics like MA [19], LOA [6], LEO [18].…”

“…In the competing assessment, FSA competes with five brand new swarm-based metaheuristics: migration algorithm (MA), total interaction algorithm (TIA), lyrebird optimization algorithm (LOA), osprey optimization algorithm (OOA), and kookaburra optimization algorithm (KOA). The result exhibits that FSA is finer than MA, TIA, LOA, OOA, and KOA in 19,21,21,19,and 20 functions out of 23 functions respectively. The result also shows that the superiority of FSA takes place in both unimodal and multimodal problems.…”

“…These social activities range from education or training, social movement, and so on. Some of these metaheuristics are chef-based optimization algorithm (CBOA) [16], driving training-based optimization (DTBO) [17], language education optimization (LEO) [18], migration algorithm (MA) [19], paint optimizer (PO) [20], deep sleep optimization (DSO) [21], mother optimization algorithm (MOA) [22], sewing training-based optimization (STBO) [23], electionbased optimization algorithm (EBOA) [24], and so on.…”