Computationally expensive constrained problems via surrogate-assisted dynamic population evolutionary optimization
Abstract This paper proposes a surrogate-assisted dynamic population optimization algorithm (SDPOA) for the purpose of solving computationally expensive constrained optimization problems, in which the population is dynamically updated based on the real-time iteration information to achieve targeted...
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2025-01-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01745-0 |
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| author | Zan Yang Chen Jiang Jiansheng Liu |
| author_facet | Zan Yang Chen Jiang Jiansheng Liu |
| author_sort | Zan Yang |
| collection | DOAJ |
| description | Abstract This paper proposes a surrogate-assisted dynamic population optimization algorithm (SDPOA) for the purpose of solving computationally expensive constrained optimization problems, in which the population is dynamically updated based on the real-time iteration information to achieve targeted searches for solutions with different qualities. Specifically, the population is dynamically constructed by simultaneously considering the real-time feasibility, convergence, and diversity information of all the previously evaluated solutions. The evolution strategies adapted to dynamic populations are designed to arrange targeted search resources for individuals with different potentials. Specifically, for mutation, targeted base solution selection for the top 2 and other center points is designed for emphasizing the exploitation in promising regions; for selection, the search sources arranged on the best and other population individuals are adaptively adjusted with the iteration progresses; for constraint handling, the diversity of infeasible solutions is integrated into the original constraint-domination principle to avoid the locality of only using constraint violation to rank infeasible solutions. For accelerating the convergence, the sparse local search is designed based on update state of the current best solution in which two excellent but non adjacent individuals are used to provide valuable guidance information for local search. Therefore, SDPOA strikes a balance between feasibility, diversity, and convergence. Empirical studies demonstrate that the SDPOA achieves the best performance among all the compared state-of-the-art algorithms, and the SDPOA can obtain new structures with smaller compliance in the design of polyline-based core sandwich structures. |
| format | Article |
| id | doaj-art-e0e5b6884d1a4c118410a4c7f04e28d3 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-e0e5b6884d1a4c118410a4c7f04e28d32025-02-09T13:01:22ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-01-0111213410.1007/s40747-024-01745-0Computationally expensive constrained problems via surrogate-assisted dynamic population evolutionary optimizationZan Yang0Chen Jiang1Jiansheng Liu2School of Advanced Manufacturing, Nanchang UniversityGuangdong Intelligent Robotics InstituteSchool of Advanced Manufacturing, Nanchang UniversityAbstract This paper proposes a surrogate-assisted dynamic population optimization algorithm (SDPOA) for the purpose of solving computationally expensive constrained optimization problems, in which the population is dynamically updated based on the real-time iteration information to achieve targeted searches for solutions with different qualities. Specifically, the population is dynamically constructed by simultaneously considering the real-time feasibility, convergence, and diversity information of all the previously evaluated solutions. The evolution strategies adapted to dynamic populations are designed to arrange targeted search resources for individuals with different potentials. Specifically, for mutation, targeted base solution selection for the top 2 and other center points is designed for emphasizing the exploitation in promising regions; for selection, the search sources arranged on the best and other population individuals are adaptively adjusted with the iteration progresses; for constraint handling, the diversity of infeasible solutions is integrated into the original constraint-domination principle to avoid the locality of only using constraint violation to rank infeasible solutions. For accelerating the convergence, the sparse local search is designed based on update state of the current best solution in which two excellent but non adjacent individuals are used to provide valuable guidance information for local search. Therefore, SDPOA strikes a balance between feasibility, diversity, and convergence. Empirical studies demonstrate that the SDPOA achieves the best performance among all the compared state-of-the-art algorithms, and the SDPOA can obtain new structures with smaller compliance in the design of polyline-based core sandwich structures.https://doi.org/10.1007/s40747-024-01745-0Expensive constrained optimizationRadial basis functionDynamic populationSandwich structures |
| spellingShingle | Zan Yang Chen Jiang Jiansheng Liu Computationally expensive constrained problems via surrogate-assisted dynamic population evolutionary optimization Complex & Intelligent Systems Expensive constrained optimization Radial basis function Dynamic population Sandwich structures |
| title | Computationally expensive constrained problems via surrogate-assisted dynamic population evolutionary optimization |
| title_full | Computationally expensive constrained problems via surrogate-assisted dynamic population evolutionary optimization |
| title_fullStr | Computationally expensive constrained problems via surrogate-assisted dynamic population evolutionary optimization |
| title_full_unstemmed | Computationally expensive constrained problems via surrogate-assisted dynamic population evolutionary optimization |
| title_short | Computationally expensive constrained problems via surrogate-assisted dynamic population evolutionary optimization |
| title_sort | computationally expensive constrained problems via surrogate assisted dynamic population evolutionary optimization |
| topic | Expensive constrained optimization Radial basis function Dynamic population Sandwich structures |
| url | https://doi.org/10.1007/s40747-024-01745-0 |
| work_keys_str_mv | AT zanyang computationallyexpensiveconstrainedproblemsviasurrogateassisteddynamicpopulationevolutionaryoptimization AT chenjiang computationallyexpensiveconstrainedproblemsviasurrogateassisteddynamicpopulationevolutionaryoptimization AT jianshengliu computationallyexpensiveconstrainedproblemsviasurrogateassisteddynamicpopulationevolutionaryoptimization |