MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation
Particle Swarm Optimization is a metaheuristic optimization algorithm widely used across a broad range of applications. The algorithm has certain primary advantages such as its ease of implementation, high convergence accuracy, and fast convergence speed. Nevertheless, since its origin in 1995, Part...
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| Format: | Article |
| Language: | English |
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MDPI
2022
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| Online Access: | https://repository.ugm.ac.id/282119/1/Ardiansyah%20et%20al.%20-%202022%20-%20MUCPSO%20A%20modified%20chaotic%20particle%20swarm%20optimiza.pdf |
| _version_ | 1797037537748844544 |
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| author | Ardiansyah, Ardiansyah Ferdiana, Ridi Permanasari, Adhistya Erna |
| author_facet | Ardiansyah, Ardiansyah Ferdiana, Ridi Permanasari, Adhistya Erna |
| author_sort | Ardiansyah, Ardiansyah |
| collection | UGM |
| description | Particle Swarm Optimization is a metaheuristic optimization algorithm widely used across a broad range of applications. The algorithm has certain primary advantages such as its ease of implementation, high convergence accuracy, and fast convergence speed. Nevertheless, since its origin in 1995, Particle swarm optimization still suffers from two primary shortcomings, i.e., premature convergence and easy trapping in local optima. Therefore, this study proposes modified chaotic particle swarm optimization with uniform particle initialization to enhance the comprehensive performance of standard particle swarm optimization by introducing three additional schemes. Firstly, the initialized swarm is generated through a uniform approach. Secondly, replacing the linear inertia weight by introducing the nonlinear chaotic inertia weight map. Thirdly, by applying a personal learning strategy to enhance the global and local search to avoid trap in local optima. The proposed algorithm is examined and compared with standard particle swarm optimization, two recent particle swarm optimization variants, and a natureinspired algorithm using three software effort estimation methods as benchmark functions: Use case points, COCOMO, and Agile. Detailed investigations prove that the proposed schemes work well to develop the proposed algorithm in an exploitative manner, which is created by a uniform particle initialization and avoids being trapped on the local optimum solution in an explorative manner and is generated by a personal learning strategy and chaotic‐based inertia weight. © 2022 by the authors. |
| first_indexed | 2024-03-14T00:04:48Z |
| format | Article |
| id | oai:generic.eprints.org:282119 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:04:48Z |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2821192023-11-30T02:02:52Z https://repository.ugm.ac.id/282119/ MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation Ardiansyah, Ardiansyah Ferdiana, Ridi Permanasari, Adhistya Erna Electrical and Electronic Engineering not elsewhere classified Particle Swarm Optimization is a metaheuristic optimization algorithm widely used across a broad range of applications. The algorithm has certain primary advantages such as its ease of implementation, high convergence accuracy, and fast convergence speed. Nevertheless, since its origin in 1995, Particle swarm optimization still suffers from two primary shortcomings, i.e., premature convergence and easy trapping in local optima. Therefore, this study proposes modified chaotic particle swarm optimization with uniform particle initialization to enhance the comprehensive performance of standard particle swarm optimization by introducing three additional schemes. Firstly, the initialized swarm is generated through a uniform approach. Secondly, replacing the linear inertia weight by introducing the nonlinear chaotic inertia weight map. Thirdly, by applying a personal learning strategy to enhance the global and local search to avoid trap in local optima. The proposed algorithm is examined and compared with standard particle swarm optimization, two recent particle swarm optimization variants, and a natureinspired algorithm using three software effort estimation methods as benchmark functions: Use case points, COCOMO, and Agile. Detailed investigations prove that the proposed schemes work well to develop the proposed algorithm in an exploitative manner, which is created by a uniform particle initialization and avoids being trapped on the local optimum solution in an explorative manner and is generated by a personal learning strategy and chaotic‐based inertia weight. © 2022 by the authors. MDPI 2022 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/282119/1/Ardiansyah%20et%20al.%20-%202022%20-%20MUCPSO%20A%20modified%20chaotic%20particle%20swarm%20optimiza.pdf Ardiansyah, Ardiansyah and Ferdiana, Ridi and Permanasari, Adhistya Erna (2022) MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation. Applied Sciences (Switzerland), 12 (3). pp. 1-24. ISSN 20763417 https://www.mdpi.com/2076-3417/12/3/1081 |
| spellingShingle | Electrical and Electronic Engineering not elsewhere classified Ardiansyah, Ardiansyah Ferdiana, Ridi Permanasari, Adhistya Erna MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation |
| title | MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation |
| title_full | MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation |
| title_fullStr | MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation |
| title_full_unstemmed | MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation |
| title_short | MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation |
| title_sort | mucpso a modified chaotic particle swarm optimization with uniform initialization for optimizing software effort estimation |
| topic | Electrical and Electronic Engineering not elsewhere classified |
| url | https://repository.ugm.ac.id/282119/1/Ardiansyah%20et%20al.%20-%202022%20-%20MUCPSO%20A%20modified%20chaotic%20particle%20swarm%20optimiza.pdf |
| work_keys_str_mv | AT ardiansyahardiansyah mucpsoamodifiedchaoticparticleswarmoptimizationwithuniforminitializationforoptimizingsoftwareeffortestimation AT ferdianaridi mucpsoamodifiedchaoticparticleswarmoptimizationwithuniforminitializationforoptimizingsoftwareeffortestimation AT permanasariadhistyaerna mucpsoamodifiedchaoticparticleswarmoptimizationwithuniforminitializationforoptimizingsoftwareeffortestimation |