Details of parameter settings.
<div><p>This paper analyzes the shortcomings of the traditional Whale Optimization Algorithm (WOA), mainly including the tendency to fall into local optima, slow convergence speed, and insufficient global search ability for high-dimensional and complex optimization problems. An improved...
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2025
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| _version_ | 1852017032097169408 |
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| author | Yanzhao Gu (21192659) |
| author2 | Junhao Wei (6816803) Zikun Li (2460040) Baili Lu (21192662) Shirou Pan (21568325) Ngai Cheong (21192665) |
| author2_role | author author author author author |
| author_facet | Yanzhao Gu (21192659) Junhao Wei (6816803) Zikun Li (2460040) Baili Lu (21192662) Shirou Pan (21568325) Ngai Cheong (21192665) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yanzhao Gu (21192659) Junhao Wei (6816803) Zikun Li (2460040) Baili Lu (21192662) Shirou Pan (21568325) Ngai Cheong (21192665) |
| dc.date.none.fl_str_mv | 2025-09-03T17:24:11Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0322494.t010 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Details_of_parameter_settings_/30043959 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shows great potential longer computation times experimental results showed excellent woa variants enhanced prey encircling cosine search strategies comparative scalability experiment adaptive parameter adjustment pressure vessel design global search ability engineering optimization problems demonstrating stronger stability compositional optimization problems complex optimization problems gwoa significantly improves basic metaheuristic algorithms slow convergence speed may also fall optimization ability convergence speed scale problems spring design solution stability may lead convergence efficiency xlink "> test functions strategy integration solution accuracy several experiments paper analyzes overall efficiency met constraints local optima latest algorithms handling large efficient tool covering unimodal 46 %. |
| dc.title.none.fl_str_mv | Details of parameter settings. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>This paper analyzes the shortcomings of the traditional Whale Optimization Algorithm (WOA), mainly including the tendency to fall into local optima, slow convergence speed, and insufficient global search ability for high-dimensional and complex optimization problems. An improved Whale Optimization Algorithm (GWOA) is proposed to overcome these issues. By integrating several improvement strategies, such as adaptive parameter adjustment, enhanced prey encircling, and sine-cosine search strategies, GWOA significantly enhances global search ability and convergence efficiency. However, GWOA increases computational complexity, which may lead to longer computation times when handling large-scale problems. It may also fall into local optima in high-dimensional cases. Several experiments were conducted to verify the effectiveness of GWOA. First, 23 classic benchmark functions were tested, covering unimodal, multimodal, and compositional optimization problems. GWOA was compared with other basic metaheuristic algorithms, excellent WOA variants, and the latest algorithms. Then, a comparative scalability experiment is performed on GWOA. The experimental results showed that GWOA achieved better convergence speed and solution accuracy than other algorithms in most test functions, especially in multimodal and compositional optimization problems, with an Overall Efficiency (OE) value of 74.46%. In engineering optimization problems, such as pressure vessel design and spring design, GWOA effectively reduced costs and met constraints, demonstrating stronger stability and optimization ability. In conclusion, GWOA significantly improves the global search ability, convergence speed, and solution stability through multi-strategy integration. It shows great potential in solving complex optimization problems and provides an efficient tool for engineering optimization applications.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_2f8abd2a1c2e216313f2fd579b8a9d6e |
| identifier_str_mv | 10.1371/journal.pone.0322494.t010 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30043959 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Details of parameter settings.Yanzhao Gu (21192659)Junhao Wei (6816803)Zikun Li (2460040)Baili Lu (21192662)Shirou Pan (21568325)Ngai Cheong (21192665)BiophysicsBiotechnologySpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedshows great potentiallonger computation timesexperimental results showedexcellent woa variantsenhanced prey encirclingcosine search strategiescomparative scalability experimentadaptive parameter adjustmentpressure vessel designglobal search abilityengineering optimization problemsdemonstrating stronger stabilitycompositional optimization problemscomplex optimization problemsgwoa significantly improvesbasic metaheuristic algorithmsslow convergence speedmay also falloptimization abilityconvergence speedscale problemsspring designsolution stabilitymay leadconvergence efficiencyxlink ">test functionsstrategy integrationsolution accuracyseveral experimentspaper analyzesoverall efficiencymet constraintslocal optimalatest algorithmshandling largeefficient toolcovering unimodal46 %.<div><p>This paper analyzes the shortcomings of the traditional Whale Optimization Algorithm (WOA), mainly including the tendency to fall into local optima, slow convergence speed, and insufficient global search ability for high-dimensional and complex optimization problems. An improved Whale Optimization Algorithm (GWOA) is proposed to overcome these issues. By integrating several improvement strategies, such as adaptive parameter adjustment, enhanced prey encircling, and sine-cosine search strategies, GWOA significantly enhances global search ability and convergence efficiency. However, GWOA increases computational complexity, which may lead to longer computation times when handling large-scale problems. It may also fall into local optima in high-dimensional cases. Several experiments were conducted to verify the effectiveness of GWOA. First, 23 classic benchmark functions were tested, covering unimodal, multimodal, and compositional optimization problems. GWOA was compared with other basic metaheuristic algorithms, excellent WOA variants, and the latest algorithms. Then, a comparative scalability experiment is performed on GWOA. The experimental results showed that GWOA achieved better convergence speed and solution accuracy than other algorithms in most test functions, especially in multimodal and compositional optimization problems, with an Overall Efficiency (OE) value of 74.46%. In engineering optimization problems, such as pressure vessel design and spring design, GWOA effectively reduced costs and met constraints, demonstrating stronger stability and optimization ability. In conclusion, GWOA significantly improves the global search ability, convergence speed, and solution stability through multi-strategy integration. It shows great potential in solving complex optimization problems and provides an efficient tool for engineering optimization applications.</p></div>2025-09-03T17:24:11ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0322494.t010https://figshare.com/articles/dataset/Details_of_parameter_settings_/30043959CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300439592025-09-03T17:24:11Z |
| spellingShingle | Details of parameter settings. Yanzhao Gu (21192659) Biophysics Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shows great potential longer computation times experimental results showed excellent woa variants enhanced prey encircling cosine search strategies comparative scalability experiment adaptive parameter adjustment pressure vessel design global search ability engineering optimization problems demonstrating stronger stability compositional optimization problems complex optimization problems gwoa significantly improves basic metaheuristic algorithms slow convergence speed may also fall optimization ability convergence speed scale problems spring design solution stability may lead convergence efficiency xlink "> test functions strategy integration solution accuracy several experiments paper analyzes overall efficiency met constraints local optima latest algorithms handling large efficient tool covering unimodal 46 %. |
| status_str | publishedVersion |
| title | Details of parameter settings. |
| title_full | Details of parameter settings. |
| title_fullStr | Details of parameter settings. |
| title_full_unstemmed | Details of parameter settings. |
| title_short | Details of parameter settings. |
| title_sort | Details of parameter settings. |
| topic | Biophysics Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shows great potential longer computation times experimental results showed excellent woa variants enhanced prey encircling cosine search strategies comparative scalability experiment adaptive parameter adjustment pressure vessel design global search ability engineering optimization problems demonstrating stronger stability compositional optimization problems complex optimization problems gwoa significantly improves basic metaheuristic algorithms slow convergence speed may also fall optimization ability convergence speed scale problems spring design solution stability may lead convergence efficiency xlink "> test functions strategy integration solution accuracy several experiments paper analyzes overall efficiency met constraints local optima latest algorithms handling large efficient tool covering unimodal 46 %. |