Skill Optimization Algorithm: A New Human-Based Metaheuristic Technique

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18440%2F23%3A50019867" target="_blank" >RIV/62690094:18440/23:50019867 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.techscience.com/cmc/v74n1/49794" target="_blank" >https://www.techscience.com/cmc/v74n1/49794</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmc.2023.030379" target="_blank" >10.32604/cmc.2023.030379</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Skill Optimization Algorithm: A New Human-Based Metaheuristic Technique

Popis výsledku v původním jazyce

Metaheuristic algorithms are widely used in solving optimiza-tion problems. In this paper, a new metaheuristic algorithm called Skill Optimization Algorithm (SOA) is proposed to solve optimization problems. The fundamental inspiration in designing SOA is human efforts to acquire and improve skills. Various stages of SOA are mathematically modeled in two phases, including: (i) exploration, skill acquisition from experts and (ii) exploitation, skill improvement based on practice and individual effort. The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types. The optimization results show that SOA, by balancing exploration and exploitation, is able to provide good performance and appro-priate solutions for optimization problems. In addition, the performance of SOA in optimization is compared with ten metaheuristic algorithms to evalu-ate the quality of the results obtained by the proposed approach. Analysis and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achieves much more competitive results.

Název v anglickém jazyce

Skill Optimization Algorithm: A New Human-Based Metaheuristic Technique

Popis výsledku anglicky

Metaheuristic algorithms are widely used in solving optimiza-tion problems. In this paper, a new metaheuristic algorithm called Skill Optimization Algorithm (SOA) is proposed to solve optimization problems. The fundamental inspiration in designing SOA is human efforts to acquire and improve skills. Various stages of SOA are mathematically modeled in two phases, including: (i) exploration, skill acquisition from experts and (ii) exploitation, skill improvement based on practice and individual effort. The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types. The optimization results show that SOA, by balancing exploration and exploitation, is able to provide good performance and appro-priate solutions for optimization problems. In addition, the performance of SOA in optimization is compared with ten metaheuristic algorithms to evalu-ate the quality of the results obtained by the proposed approach. Analysis and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achieves much more competitive results.

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

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

CMC-Computers, Materials &amp; Continua

ISSN

1546-2218

e-ISSN

1546-2226

Svazek periodika

74

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

24

Strana od-do

179-202

Kód UT WoS článku

000890984200006

EID výsledku v databázi Scopus

2-s2.0-85139056590