A Multi-Surrogate Assisted Multi-Tasking Optimization Algorithm for High-Dimensional Expensive Problems
Surrogate-assisted evolutionary algorithms (SAEAs) are widely used in the field of high-dimensional expensive optimization. However, real-world problems are usually complex and characterized by a variety of features. Therefore, it is very challenging to choose the most appropriate surrogate. It has...
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MDPI AG
2024-12-01
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| author | Hongyu Li Lei Chen Jian Zhang Muxi Li |
| author_facet | Hongyu Li Lei Chen Jian Zhang Muxi Li |
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| description | Surrogate-assisted evolutionary algorithms (SAEAs) are widely used in the field of high-dimensional expensive optimization. However, real-world problems are usually complex and characterized by a variety of features. Therefore, it is very challenging to choose the most appropriate surrogate. It has been shown that multiple surrogates can characterize the fitness landscape more accurately than a single surrogate. In this work, a multi-surrogate-assisted multi-tasking optimization algorithm (MSAMT) is proposed that solves high-dimensional problems by simultaneously optimizing multiple surrogates as related tasks using the generalized multi-factorial evolutionary algorithm. In the MSAMT, all exactly evaluated samples are initially grouped to form a collection of clusters. Subsequently, the search space can be divided into several areas based on the clusters, and surrogates are constructed in each region that are capable of completely describing the entire fitness landscape as a way to improve the exploration capability of the algorithm. Near the current optimal solution, a novel ensemble surrogate is adopted to achieve local search in speeding up the convergence process. In the framework of a multi-tasking optimization algorithm, several surrogates are optimized simultaneously as related tasks. As a result, several optimal solutions spread throughout disjoint regions can be found for real function evaluation. Fourteen 10- to 100-dimensional test functions and a spatial truss design problem were used to compare the proposed approach with several recently proposed SAEAs. The results show that the proposed MSAMT performs better than the comparison algorithms in most test functions and real engineering problems. |
| format | Article |
| id | doaj-art-1bce5996fdd445c1aae0ae6ea592a35d |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Algorithms |
| spelling | doaj-art-1bce5996fdd445c1aae0ae6ea592a35d2025-01-24T13:17:25ZengMDPI AGAlgorithms1999-48932024-12-01181410.3390/a18010004A Multi-Surrogate Assisted Multi-Tasking Optimization Algorithm for High-Dimensional Expensive ProblemsHongyu Li0Lei Chen1Jian Zhang2Muxi Li3Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang 212013, ChinaFaculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang 212013, ChinaOcean Institute, Northwestern Polytechnical University, Taicang 215400, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin 300354, ChinaSurrogate-assisted evolutionary algorithms (SAEAs) are widely used in the field of high-dimensional expensive optimization. However, real-world problems are usually complex and characterized by a variety of features. Therefore, it is very challenging to choose the most appropriate surrogate. It has been shown that multiple surrogates can characterize the fitness landscape more accurately than a single surrogate. In this work, a multi-surrogate-assisted multi-tasking optimization algorithm (MSAMT) is proposed that solves high-dimensional problems by simultaneously optimizing multiple surrogates as related tasks using the generalized multi-factorial evolutionary algorithm. In the MSAMT, all exactly evaluated samples are initially grouped to form a collection of clusters. Subsequently, the search space can be divided into several areas based on the clusters, and surrogates are constructed in each region that are capable of completely describing the entire fitness landscape as a way to improve the exploration capability of the algorithm. Near the current optimal solution, a novel ensemble surrogate is adopted to achieve local search in speeding up the convergence process. In the framework of a multi-tasking optimization algorithm, several surrogates are optimized simultaneously as related tasks. As a result, several optimal solutions spread throughout disjoint regions can be found for real function evaluation. Fourteen 10- to 100-dimensional test functions and a spatial truss design problem were used to compare the proposed approach with several recently proposed SAEAs. The results show that the proposed MSAMT performs better than the comparison algorithms in most test functions and real engineering problems.https://www.mdpi.com/1999-4893/18/1/4multi-tasking optimization algorithmalgorithm designdynamic ensemble of surrogatesdecision space partitionexpensive optimization problem |
| spellingShingle | Hongyu Li Lei Chen Jian Zhang Muxi Li A Multi-Surrogate Assisted Multi-Tasking Optimization Algorithm for High-Dimensional Expensive Problems Algorithms multi-tasking optimization algorithm algorithm design dynamic ensemble of surrogates decision space partition expensive optimization problem |
| title | A Multi-Surrogate Assisted Multi-Tasking Optimization Algorithm for High-Dimensional Expensive Problems |
| title_full | A Multi-Surrogate Assisted Multi-Tasking Optimization Algorithm for High-Dimensional Expensive Problems |
| title_fullStr | A Multi-Surrogate Assisted Multi-Tasking Optimization Algorithm for High-Dimensional Expensive Problems |
| title_full_unstemmed | A Multi-Surrogate Assisted Multi-Tasking Optimization Algorithm for High-Dimensional Expensive Problems |
| title_short | A Multi-Surrogate Assisted Multi-Tasking Optimization Algorithm for High-Dimensional Expensive Problems |
| title_sort | multi surrogate assisted multi tasking optimization algorithm for high dimensional expensive problems |
| topic | multi-tasking optimization algorithm algorithm design dynamic ensemble of surrogates decision space partition expensive optimization problem |
| url | https://www.mdpi.com/1999-4893/18/1/4 |
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