An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization
Contemporary real-world optimization benchmarks are subject to many constraints and are often high-dimensional problems. Typically, such problems are expensive in terms of computational time and cost. Conventional constraint-based solvers that are used to tackle such problems require a considerable...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/142645 |
| _version_ | 1826116321865105408 |
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| author | Noor H. Awad Mostafa Z. Ali Mallipeddi, Rammohan Suganthan, Ponnuthurai Nagaratnam |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Noor H. Awad Mostafa Z. Ali Mallipeddi, Rammohan Suganthan, Ponnuthurai Nagaratnam |
| author_sort | Noor H. Awad |
| collection | NTU |
| description | Contemporary real-world optimization benchmarks are subject to many constraints and are often high-dimensional problems. Typically, such problems are expensive in terms of computational time and cost. Conventional constraint-based solvers that are used to tackle such problems require a considerable high budget of function evaluations. Such budget is not affordable in practice. In most cases, this number is considered the termination criterion in which the optimization process is stopped and then the best solution is marked. The algorithm might not converge even after consuming the pre-defined number of function evaluations, and hence it does not guarantee an optimal solution is found. Motivated by this consideration, this paper introduces an effective surrogate model to assist the differential evolution algorithm to generate competitive solutions during the search process. The proposed surrogate model uses a new adaptation scheme to adapt the theta parameter in the well-known Kriging model. This variable determines the correlation between the parameters of the optimization problem being solved. For that reason, an accurate surrogate model is crucial to have a noticeable enhancement during the search. The statistical information exploited from a covariance matrix is used to build the correlation matrix to adapt the theta variable instead of using a fixed value during the search. Hence, the surrogate model evolves over the generations to better model the basin of the search, as the population evolves. The model is implemented in the popular L-SHADE algorithm. Two benchmark sets: bound-constrained problems and real-world optimization problems are used to validate the performance of the proposed algorithm, namely iDEaSm. Also, two engineering design problems are solved: welded beam and pressure vessel. The performance of the proposed work is compared with other state-of-the-art algorithms and the simulation results indicate that the new technique can improve the performance to generate better statistical significance solutions. |
| first_indexed | 2024-10-01T04:09:25Z |
| format | Journal Article |
| id | ntu-10356/142645 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:09:25Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1426452020-06-26T02:35:24Z An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization Noor H. Awad Mostafa Z. Ali Mallipeddi, Rammohan Suganthan, Ponnuthurai Nagaratnam School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Evolutionary Algorithm Differential Evolution Contemporary real-world optimization benchmarks are subject to many constraints and are often high-dimensional problems. Typically, such problems are expensive in terms of computational time and cost. Conventional constraint-based solvers that are used to tackle such problems require a considerable high budget of function evaluations. Such budget is not affordable in practice. In most cases, this number is considered the termination criterion in which the optimization process is stopped and then the best solution is marked. The algorithm might not converge even after consuming the pre-defined number of function evaluations, and hence it does not guarantee an optimal solution is found. Motivated by this consideration, this paper introduces an effective surrogate model to assist the differential evolution algorithm to generate competitive solutions during the search process. The proposed surrogate model uses a new adaptation scheme to adapt the theta parameter in the well-known Kriging model. This variable determines the correlation between the parameters of the optimization problem being solved. For that reason, an accurate surrogate model is crucial to have a noticeable enhancement during the search. The statistical information exploited from a covariance matrix is used to build the correlation matrix to adapt the theta variable instead of using a fixed value during the search. Hence, the surrogate model evolves over the generations to better model the basin of the search, as the population evolves. The model is implemented in the popular L-SHADE algorithm. Two benchmark sets: bound-constrained problems and real-world optimization problems are used to validate the performance of the proposed algorithm, namely iDEaSm. Also, two engineering design problems are solved: welded beam and pressure vessel. The performance of the proposed work is compared with other state-of-the-art algorithms and the simulation results indicate that the new technique can improve the performance to generate better statistical significance solutions. 2020-06-26T02:35:24Z 2020-06-26T02:35:24Z 2018 Journal Article Noor H. Awad., Mostafa Z. Ali., Mallipeddi, R., & Suganthan, P. N. (2018). An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization. Information Sciences, 451-452, 326-347. doi:10.1016/j.ins.2018.04.024 0020-0255 https://hdl.handle.net/10356/142645 10.1016/j.ins.2018.04.024 2-s2.0-85045428554 451-452 326 347 en Information Sciences © 2018 Elsevier Inc. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Evolutionary Algorithm Differential Evolution Noor H. Awad Mostafa Z. Ali Mallipeddi, Rammohan Suganthan, Ponnuthurai Nagaratnam An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization |
| title | An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization |
| title_full | An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization |
| title_fullStr | An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization |
| title_full_unstemmed | An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization |
| title_short | An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization |
| title_sort | improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization |
| topic | Engineering::Electrical and electronic engineering Evolutionary Algorithm Differential Evolution |
| url | https://hdl.handle.net/10356/142645 |
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