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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50020392" target="_blank" >RIV/62690094:18470/23:50020392 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.techscience.com/CMES/online/detail/19111" target="_blank" >https://www.techscience.com/CMES/online/detail/19111</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmes.2023.028314" target="_blank" >10.32604/cmes.2023.028314</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos;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&apos;s results are compared with the performance of twelve well-known metaheuristic algorithms. The optimization results show the proposed MA approach&apos;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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos;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&apos;s results are compared with the performance of twelve well-known metaheuristic algorithms. The optimization results show the proposed MA approach&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computer Modeling in Engineering &amp; Sciences

ISSN

1526-1492

e-ISSN

1526-1506

Svazek periodika

137

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

36

Strana od-do

1695-1730

Kód UT WoS článku

000962854800001

EID výsledku v databázi Scopus

2-s2.0-85165172667