Cargando…

Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs

Many pivotal and knotty engineering problems in practical applications boil down to optimization problems, which are difficult to resolve using traditional mathematical optimization methods. Metaheuristics are efficient algorithms for solving complex optimization problems while keeping computational...

Descripción completa

Detalles Bibliográficos
Autores principales: Peng, Min, Jing, Wenlong, Yang, Jianwei, Hu, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10123685/
https://www.ncbi.nlm.nih.gov/pubmed/37092414
http://dx.doi.org/10.3390/biomimetics8020162
_version_ 1785029710561935360
author Peng, Min
Jing, Wenlong
Yang, Jianwei
Hu, Gang
author_facet Peng, Min
Jing, Wenlong
Yang, Jianwei
Hu, Gang
author_sort Peng, Min
collection PubMed
description Many pivotal and knotty engineering problems in practical applications boil down to optimization problems, which are difficult to resolve using traditional mathematical optimization methods. Metaheuristics are efficient algorithms for solving complex optimization problems while keeping computational costs reasonable. The carnivorous plant algorithm (CPA) is a newly proposed metaheuristic algorithm, inspired by its foraging strategies of attraction, capture, digestion, and reproduction. However, the CPA is not without its shortcomings. In this paper, an enhanced multistrategy carnivorous plant algorithm called the UCDCPA is developed. In the proposed framework, a good point set, Cauchy mutation, and differential evolution are introduced to increase the algorithm’s calculation precision and convergence speed as well as heighten the diversity of the population and avoid becoming trapped in local optima. The superiority and practicability of the UCDCPA are illustrated by comparing its experimental results with several algorithms against the CEC2014 and CEC2017 benchmark functions, and five engineering designs. Additionally, the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank-sum tests. The findings show that these introduced strategies provide some improvements in the performance of the CPA, and the accuracy and stability of the optimization results provided by the proposed UCDCPA are competitive against all algorithms. To conclude, the proposed UCDCPA offers a good alternative to solving optimization issues.
format Online
Article
Text
id pubmed-10123685
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-101236852023-04-25 Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs Peng, Min Jing, Wenlong Yang, Jianwei Hu, Gang Biomimetics (Basel) Article Many pivotal and knotty engineering problems in practical applications boil down to optimization problems, which are difficult to resolve using traditional mathematical optimization methods. Metaheuristics are efficient algorithms for solving complex optimization problems while keeping computational costs reasonable. The carnivorous plant algorithm (CPA) is a newly proposed metaheuristic algorithm, inspired by its foraging strategies of attraction, capture, digestion, and reproduction. However, the CPA is not without its shortcomings. In this paper, an enhanced multistrategy carnivorous plant algorithm called the UCDCPA is developed. In the proposed framework, a good point set, Cauchy mutation, and differential evolution are introduced to increase the algorithm’s calculation precision and convergence speed as well as heighten the diversity of the population and avoid becoming trapped in local optima. The superiority and practicability of the UCDCPA are illustrated by comparing its experimental results with several algorithms against the CEC2014 and CEC2017 benchmark functions, and five engineering designs. Additionally, the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank-sum tests. The findings show that these introduced strategies provide some improvements in the performance of the CPA, and the accuracy and stability of the optimization results provided by the proposed UCDCPA are competitive against all algorithms. To conclude, the proposed UCDCPA offers a good alternative to solving optimization issues. MDPI 2023-04-17 /pmc/articles/PMC10123685/ /pubmed/37092414 http://dx.doi.org/10.3390/biomimetics8020162 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Peng, Min
Jing, Wenlong
Yang, Jianwei
Hu, Gang
Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs
title Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs
title_full Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs
title_fullStr Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs
title_full_unstemmed Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs
title_short Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs
title_sort multistrategy-boosted carnivorous plant algorithm: performance analysis and application in engineering designs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10123685/
https://www.ncbi.nlm.nih.gov/pubmed/37092414
http://dx.doi.org/10.3390/biomimetics8020162
work_keys_str_mv AT pengmin multistrategyboostedcarnivorousplantalgorithmperformanceanalysisandapplicationinengineeringdesigns
AT jingwenlong multistrategyboostedcarnivorousplantalgorithmperformanceanalysisandapplicationinengineeringdesigns
AT yangjianwei multistrategyboostedcarnivorousplantalgorithmperformanceanalysisandapplicationinengineeringdesigns
AT hugang multistrategyboostedcarnivorousplantalgorithmperformanceanalysisandapplicationinengineeringdesigns