An Effective Adjustment to the Integration of Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Environments
Although particle swarm optimization (PSO) is a powerful evolutionary algorithm for solving nonlinear optimization problems in deterministic environments, many practical problems have some stochastic noise. The optimal computing budget allocation (OCBA) has been integrated into PSO in various ways t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9201437/ |
| _version_ | 1818351301682003968 |
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| author | Seon Han Choi Jang Won Bae |
| author_facet | Seon Han Choi Jang Won Bae |
| author_sort | Seon Han Choi |
| collection | DOAJ |
| description | Although particle swarm optimization (PSO) is a powerful evolutionary algorithm for solving nonlinear optimization problems in deterministic environments, many practical problems have some stochastic noise. The optimal computing budget allocation (OCBA) has been integrated into PSO in various ways to cope with this. The OCBA can mitigate the effect of noise on PSO by selecting the best solution under a limited evaluation budget. Recently, with the increasing complexity of PSO applications, the evaluation costs are also increasing rapidly, which has sparked the need for more efficient PSO in stochastic environments. This article proposes a simple yet effective adjustment to the integration of OCBA to further improve the efficiency of PSO. The proposed adjustment allows OCBA to expand its search space to find the global best position more correctly such that the entire swarm can move on a better direction under stochastic noise. The experimental results on various benchmarks demonstrate the improved performance of PSO by the proposed adjustment under a limited budget compared with the latest studies. In addition, the results regarding fighters' evasion flight optimization emphasize the practical need for the proposed adjustment. |
| first_indexed | 2024-12-13T18:35:34Z |
| format | Article |
| id | doaj.art-e29697c384734f59af0f74c713618bd3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:35:34Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e29697c384734f59af0f74c713618bd32022-12-21T23:35:22ZengIEEEIEEE Access2169-35362020-01-01817365417366510.1109/ACCESS.2020.30255599201437An Effective Adjustment to the Integration of Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic EnvironmentsSeon Han Choi0https://orcid.org/0000-0003-3332-7723Jang Won Bae1Department of IT Convergence and Application Engineering, Pukyong National University, Busan, South KoreaSchool of Industrial Management, Korea University of Technology and Education, Cheonan, South KoreaAlthough particle swarm optimization (PSO) is a powerful evolutionary algorithm for solving nonlinear optimization problems in deterministic environments, many practical problems have some stochastic noise. The optimal computing budget allocation (OCBA) has been integrated into PSO in various ways to cope with this. The OCBA can mitigate the effect of noise on PSO by selecting the best solution under a limited evaluation budget. Recently, with the increasing complexity of PSO applications, the evaluation costs are also increasing rapidly, which has sparked the need for more efficient PSO in stochastic environments. This article proposes a simple yet effective adjustment to the integration of OCBA to further improve the efficiency of PSO. The proposed adjustment allows OCBA to expand its search space to find the global best position more correctly such that the entire swarm can move on a better direction under stochastic noise. The experimental results on various benchmarks demonstrate the improved performance of PSO by the proposed adjustment under a limited budget compared with the latest studies. In addition, the results regarding fighters' evasion flight optimization emphasize the practical need for the proposed adjustment.https://ieeexplore.ieee.org/document/9201437/Particle swarm optimizationoptimal computing budget allocationstochastic environmentcomputational efficiency |
| spellingShingle | Seon Han Choi Jang Won Bae An Effective Adjustment to the Integration of Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Environments IEEE Access Particle swarm optimization optimal computing budget allocation stochastic environment computational efficiency |
| title | An Effective Adjustment to the Integration of Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Environments |
| title_full | An Effective Adjustment to the Integration of Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Environments |
| title_fullStr | An Effective Adjustment to the Integration of Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Environments |
| title_full_unstemmed | An Effective Adjustment to the Integration of Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Environments |
| title_short | An Effective Adjustment to the Integration of Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Environments |
| title_sort | effective adjustment to the integration of optimal computing budget allocation for particle swarm optimization in stochastic environments |
| topic | Particle swarm optimization optimal computing budget allocation stochastic environment computational efficiency |
| url | https://ieeexplore.ieee.org/document/9201437/ |
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