Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 26, Issue 5, Pages 1144-1156Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2022.3153933
Keywords
Statistics; Sociology; Optimization; Probabilistic logic; Computational modeling; Codes; Mathematical models; Clustering; continuous optimization; estimation of distribution algorithms (EDAs); mapping; nonlinear constraints; seeding
Funding
- La Caixa Foundation Fellowship
- Basque Government through the BERC 2022-2025
- Elkartek Programs [KK-2020/00049]
- Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa Excellence Accreditation [SEV-2017-0718]
- Spanish Ministry of Science [PID2019-106453GAI00/AEI/10.13039/501100011033]
- Basque Government consolidated Groups 2019-2021 [IT1244-19]
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This article introduces a new algorithm (EDA++) equipped with mechanisms to handle nonlinear constraints by adopting the framework of estimation of distribution algorithms (EDAs). The study shows that the feasibility of the final solutions is guaranteed and the quality of the solutions in terms of objective values is improved by seeding an initial population of feasible solutions to the algorithm.
Handling nonlinear constraints in continuous optimization is challenging, and finding a feasible solution is usually a difficult task. In the past few decades, various techniques have been developed to deal with linear and nonlinear constraints. However, reaching feasible solutions has been a challenging task for most of these methods. In this article, we adopt the framework of estimation of distribution algorithms (EDAs) and propose a new algorithm (EDA++) equipped with some mechanisms to deal with nonlinear constraints. These mechanisms are associated with different stages of the EDA, including seeding, learning, and mapping. It is shown that, besides increasing the quality of the solutions in terms of objective values, the feasibility of the final solutions is guaranteed if an initial population of feasible solutions is seeded to the algorithm. The EDA with the proposed mechanisms is applied to two suites of benchmark problems for constrained continuous optimization and its performance is compared with some state-of-the-art algorithms and constraint-handling methods. Conducted experiments confirm the speed, robustness, and efficiency of the proposed algorithm in tackling various problems with linear and nonlinear constraints.
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