A-DVM: A Self-Adaptive Variable Matrix Decision Variable Selection Scheme for Multimodal Problems
Artificial Bee Colony (ABC) is a Swarm Intelligence optimization algorithm well known for its versatility. The selection of decision variables to update is purely stochastic, incurring several issues to the local search capability of the ABC. To address these issues, a self-adaptive decision variabl...
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| Format: | Article |
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MDPI AG
2020-09-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/9/1004 |
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| author | Marco Antonio Florenzano Mollinetti Bernardo Bentes Gatto Mário Tasso Ribeiro Serra Neto Takahito Kuno |
| author_facet | Marco Antonio Florenzano Mollinetti Bernardo Bentes Gatto Mário Tasso Ribeiro Serra Neto Takahito Kuno |
| author_sort | Marco Antonio Florenzano Mollinetti |
| collection | DOAJ |
| description | Artificial Bee Colony (ABC) is a Swarm Intelligence optimization algorithm well known for its versatility. The selection of decision variables to update is purely stochastic, incurring several issues to the local search capability of the ABC. To address these issues, a self-adaptive decision variable selection mechanism is proposed with the goal of balancing the degree of exploration and exploitation throughout the execution of the algorithm. This selection, named Adaptive Decision Variable Matrix (A-DVM), represents both stochastic and deterministic parameter selection in a binary matrix and regulates the extent of how much each selection is employed based on the estimation of the sparsity of the solutions in the search space. The influence of the proposed approach to performance and robustness of the original algorithm is validated by experimenting on 15 highly multimodal benchmark optimization problems. Numerical comparison on those problems is made against the ABC and their variants and prominent population-based algorithms (e.g., Particle Swarm Optimization and Differential Evolution). Results show an improvement in the performance of the algorithms with the A-DVM in the most challenging instances. |
| first_indexed | 2024-03-10T16:28:36Z |
| format | Article |
| id | doaj.art-58d94af8d829492eb22ff25eae57abd7 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T16:28:36Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-58d94af8d829492eb22ff25eae57abd72023-11-20T13:01:27ZengMDPI AGEntropy1099-43002020-09-01229100410.3390/e22091004A-DVM: A Self-Adaptive Variable Matrix Decision Variable Selection Scheme for Multimodal ProblemsMarco Antonio Florenzano Mollinetti0Bernardo Bentes Gatto1Mário Tasso Ribeiro Serra Neto2Takahito Kuno3School of Systems and Information Engineering, University of Tsukuba, Tsukuba 305-8577, JapanCenter for Artificial Intelligence Research (C-AIR), University of Tsukuba, Tsukuba 305-8577, JapanDepartment of Computer Science, University of Porto, 4050-290 Porto, PortugalSchool of Systems and Information Engineering, University of Tsukuba, Tsukuba 305-8577, JapanArtificial Bee Colony (ABC) is a Swarm Intelligence optimization algorithm well known for its versatility. The selection of decision variables to update is purely stochastic, incurring several issues to the local search capability of the ABC. To address these issues, a self-adaptive decision variable selection mechanism is proposed with the goal of balancing the degree of exploration and exploitation throughout the execution of the algorithm. This selection, named Adaptive Decision Variable Matrix (A-DVM), represents both stochastic and deterministic parameter selection in a binary matrix and regulates the extent of how much each selection is employed based on the estimation of the sparsity of the solutions in the search space. The influence of the proposed approach to performance and robustness of the original algorithm is validated by experimenting on 15 highly multimodal benchmark optimization problems. Numerical comparison on those problems is made against the ABC and their variants and prominent population-based algorithms (e.g., Particle Swarm Optimization and Differential Evolution). Results show an improvement in the performance of the algorithms with the A-DVM in the most challenging instances.https://www.mdpi.com/1099-4300/22/9/1004artificial bee colonyswarm intelligencemultimodal problems |
| spellingShingle | Marco Antonio Florenzano Mollinetti Bernardo Bentes Gatto Mário Tasso Ribeiro Serra Neto Takahito Kuno A-DVM: A Self-Adaptive Variable Matrix Decision Variable Selection Scheme for Multimodal Problems Entropy artificial bee colony swarm intelligence multimodal problems |
| title | A-DVM: A Self-Adaptive Variable Matrix Decision Variable Selection Scheme for Multimodal Problems |
| title_full | A-DVM: A Self-Adaptive Variable Matrix Decision Variable Selection Scheme for Multimodal Problems |
| title_fullStr | A-DVM: A Self-Adaptive Variable Matrix Decision Variable Selection Scheme for Multimodal Problems |
| title_full_unstemmed | A-DVM: A Self-Adaptive Variable Matrix Decision Variable Selection Scheme for Multimodal Problems |
| title_short | A-DVM: A Self-Adaptive Variable Matrix Decision Variable Selection Scheme for Multimodal Problems |
| title_sort | a dvm a self adaptive variable matrix decision variable selection scheme for multimodal problems |
| topic | artificial bee colony swarm intelligence multimodal problems |
| url | https://www.mdpi.com/1099-4300/22/9/1004 |
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