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A Steady-State Algorithm for Solving Expensive Multiobjective Optimization Problems With Nonparallelizable Evaluations

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Názov: A Steady-State Algorithm for Solving Expensive Multiobjective Optimization Problems With Nonparallelizable Evaluations
Autori: Kamrul Hasan Rahi, Hemant Kumar Singh, Tapabrata Ray
Zdroj: IEEE Transactions on Evolutionary Computation. 27:1544-1558
Informácie o vydavateľovi: Institute of Electrical and Electronics Engineers (IEEE), 2023.
Rok vydania: 2023
Predmety: 46 Information and Computing Sciences, 4602 Artificial Intelligence, anzsrc-for: 46 Information and Computing Sciences, 0202 electrical engineering, electronic engineering, information engineering, anzsrc-for: 0801 Artificial Intelligence and Image Processing, 02 engineering and technology, anzsrc-for: 4602 Artificial Intelligence, anzsrc-for: 0906 Electrical and Electronic Engineering, anzsrc-for: 0806 Information Systems
Popis: Expensive multiobjective optimization problems (EMOPs) refer to those wherein evaluation of each candidate solution incurs a significant cost. To solve such problems within a limited number of solution evaluations, surrogate-assisted evolutionary algorithms (SAEAs) are often used. However, existing SAEAs typically operate in a generational framework wherein multiple solutions are identified for evaluation in each generation. There exist relatively few proposals in steady-state framework, wherein only a single solution is evaluated in each iteration. The development of such algorithms is crucial to efficiently solve EMOPs for which the evaluation of candidate designs cannot be parallelized. Furthermore, regardless of the framework used, the performance of current SAEAs tends to degrade when the Pareto front (PF) of the problem has irregularities, such as extremely concave/convex segments, even for 2/3-objective problems. To contextualize the motivation of this study, the performance of a few state-of-the-art SAEAs is first demonstrated on some such selected problems. Then, to address the above research gaps, we propose a surrogate-assisted steady-state EA (SASSEA), which incorporates a number of novel elements, including: 1) effective use of model uncertainty information to aid the search, including the use of the probabilistic dominance and Mahalanobis distance; 2) two-step infill identification using nondominance (ND) and distance-based selection; and 3) a shadow ND mechanism to avoid repeated selection and evaluation of dominated solutions. The efficacy of the proposed approach is demonstrated through extensive benchmarking on a range of test problems. It shows competitive performance relative to many state-of-the-art SAEAs, including both steady-state and generational approaches.
Druh dokumentu: Article
ISSN: 1941-0026
1089-778X
DOI: 10.1109/tevc.2022.3219062
Rights: IEEE Copyright
CC BY
Prístupové číslo: edsair.doi.dedup.....73db86d47e4da8106ae90e9a505b1712
Databáza: OpenAIRE
Popis
Abstrakt:Expensive multiobjective optimization problems (EMOPs) refer to those wherein evaluation of each candidate solution incurs a significant cost. To solve such problems within a limited number of solution evaluations, surrogate-assisted evolutionary algorithms (SAEAs) are often used. However, existing SAEAs typically operate in a generational framework wherein multiple solutions are identified for evaluation in each generation. There exist relatively few proposals in steady-state framework, wherein only a single solution is evaluated in each iteration. The development of such algorithms is crucial to efficiently solve EMOPs for which the evaluation of candidate designs cannot be parallelized. Furthermore, regardless of the framework used, the performance of current SAEAs tends to degrade when the Pareto front (PF) of the problem has irregularities, such as extremely concave/convex segments, even for 2/3-objective problems. To contextualize the motivation of this study, the performance of a few state-of-the-art SAEAs is first demonstrated on some such selected problems. Then, to address the above research gaps, we propose a surrogate-assisted steady-state EA (SASSEA), which incorporates a number of novel elements, including: 1) effective use of model uncertainty information to aid the search, including the use of the probabilistic dominance and Mahalanobis distance; 2) two-step infill identification using nondominance (ND) and distance-based selection; and 3) a shadow ND mechanism to avoid repeated selection and evaluation of dominated solutions. The efficacy of the proposed approach is demonstrated through extensive benchmarking on a range of test problems. It shows competitive performance relative to many state-of-the-art SAEAs, including both steady-state and generational approaches.
ISSN:19410026
1089778X
DOI:10.1109/tevc.2022.3219062