Grasiele R. Duarte
ABSTRACT
Optimization problems with a high number of variables are known as Large-Scale Optimization Problems (LSOPs) and tend to be complex to solve. Additional strategies can be applied in Evolutionary Algorithms (EAs) to solve LSOPs. Decomposition Methods (DMs) decompose the problem domain into groups, then solve them separately. This work implements an adaptive hybrid Island Model based on stigmergy to solve LSOPs using different DMs. The DMs are compared during their execution to identify the most suitable ones to solve the problem. This study concerns the assessment of the DMs' behavior during their execution because in general, works in the literature compare them only based on the quality of the obtained solutions.
- Enrique Alba. 2005. Parallel Metaheuristics: A New Class of Algorithms. Wiley.Google Scholar
Cross Ref
- Marco Dorigo, Eric Bonabeau, and Guy Theraulaz. 2000. Ant algorithms and stigmergy. Future Generation Computer Systems 16, 8 (2000), 851--871.Google ScholarDigital Library
- Grasiele Regina Duarte, Afonso Celso de Castro Lemonge, Leonardo Goliatt da Fonseca, and Beatriz Souza Leite Pires de Lima. 2021. An Island Model based on Stigmergy to solve optimization problems. Natural Computing 20, 3 (2021), 413--441. Google ScholarDigital Library
- Jun-Rong Jian, Zhi-Hui Zhan, and Jun Zhang. 2020. Large-scale evolutionary optimization: a survey and experimental comparative study. International Journal of Machine Learning and Cybernetics 11, 3 (2020), 729--745. Google ScholarCross Ref
- Xiaodong Li, Ke Tang, Mohammad N. Omidvar, Zhenyu Yang, and Kai Qin. 2013. Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization.Google Scholar
- Xiaodong Li and Xin Yao. 2012. Cooperatively Coevolving Particle Swarms for Large Scale Optimization. IEEE Transactions on Evolutionary Computation 16, 2 (2012), 210--224. Google ScholarDigital Library
- Yi Mei, Mohammad Nabi Omidvar, Xiaodong Li, and Xin Yao. 2016. A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization. ACM Trans. Math. Softw. 42, 2, Article 13 (jun 2016), 24 pages. Google ScholarDigital Library
- Daniel Molina, Antonio LaTorre, and Francisco Herrera. 2018. SHADE with Iterative Local Search for Large-Scale Global Optimization. In 2018 IEEE Congress on Evolutionary Computation (CEC). 1--8. Google ScholarDigital Library
- Mohammad Nabi Omidvar, Xiaodong Li, Yi Mei, and Xin Yao. 2014. Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization. IEEE Transactions on Evolutionary Computation 18, 3 (2014), 378--393. Google ScholarCross Ref
- Oscar Pacheco-Del-Moral and Carlos A. Coello Coello. 2020. A SHADE-Based Algorithm for Large Scale Global Optimization. In Parallel Problem Solving from Nature - PPSN XVI. Google ScholarDigital Library
- An Song, Wei-Neng Chen, Peng-Ting Luo, Yue-Jiao Gong, and Jun Zhang. 2017. Overlapped cooperative co-evolution for large scale optimization. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 3689--3694. Google ScholarDigital Library
- Rainer Storn and Kenneth Price. 1997. Differential Evolution - A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization 11, 4 (1997), 341--359. Google ScholarDigital Library
- F. van den Bergh and A.P. Engelbrecht. 2004. A Cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 8, 3 (2004), 225--239. Google ScholarDigital Library
- Ming Yang, Mohammad Nabi Omidvar, Changhe Li, Xiaodong Li, Zhihua Cai, Borhan Kazimipour, and Xin Yao. 2017. Efficient Resource Allocation in Cooperative Co-Evolution for Large-Scale Global Optimization. IEEE Transactions on Evolutionary Computation 21, 4 (2017), 493--505. Google ScholarDigital Library
- Zhenyu Yang, Ke Tang, and Xin Yao. 2008. Large scale evolutionary optimization using cooperative coevolution. Information Sciences 178, 15 (2008), 2985--2999. Google ScholarDigital Library
- Zhenyu Yang, Ke Tang, and Xin Yao. 2008. Multilevel cooperative coevolution for large scale optimization. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). 1663--1670. Google ScholarCross Ref
Index Terms
- Dynamic evaluation of decomposition methods for large-scale optimization problems using an island model
Recommendations
Decentralizing and coevolving differential evolution for large-scale global optimization problems
This paper presents a novel decentralizing and coevolving differential evolution (DCDE) algorithm to address the issue of scaling up differential evolution (DE) algorithms to solve large-scale global optimization (LSGO) problems. As most evolutionary ...
Elite Opposition-Based Differential Evolution for Solving Large-Scale Optimization Problems and Its Implementation on GPU
PDCAT '12: Proceedings of the 2012 13th International Conference on Parallel and Distributed Computing, Applications and TechnologiesRecently, the interests of solving large-scale optimization problems have increased in the field of evolutionary algorithms. This paper presents a novel differential evolution, namely EOBDE, to solve these kinds of problems by using elite opposition-...
Island-cellular model differential evolution for large-scale global optimization
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference CompanionThe Island-Cellular Model (ICM) is an important population distribution approach for Evolutionary Algorithms (EAs). This hybrid approach combines the Island Model (IM) and Cellular Model (CM) in a two-layer hierarchical model. Although the ICM has been ...
Comments