ABSTRACT
Cooperative co-evolution (CC) is a powerful evolutionary computation framework for solving large scale global optimization (LSGO) problems via the strategy of "divide-and-conquer", but its efficiency highly relies on the decomposition result. Existing decomposition algorithms either cannot obtain correct decomposition results or require a large number of fitness evaluations (FEs). To alleviate these limitations, this paper proposes a new decomposition algorithm named historical interdependency based differential grouping (HIDG). HIDG detects interdependency from the perspective of vectors. By utilizing historical interdependency information, it develops a novel criterion which can directly deduce the interdependencies among some vectors without consuming extra FEs. Coupled with an existing vector-based decomposition framework, HIDG further significantly reduces the total number of FEs for decomposition. Experiments on two sets of LSGO benchmark functions verified the effectiveness and efficiency of HIDG.
- R. E. Bellman. 1957. Dynamic Programming, ser. Dover Books on Mathematics. Princeton University Press. Google ScholarDigital Library
- M. A. Potter and K. A. De Jong. 1994. A cooperative coevolutionary approach to function optimization. In Proceedings of the Third Conference on Parallel Problem Solving from Nature, 2, 249--257. Google ScholarDigital Library
- S. Mahdavi, M. E. Shiri and S. Rahnamayan. 2015. Metaheuristics in large-scale global continues optimization: a survey. Information Sciences, 295, 407--428. Google ScholarDigital Library
- M. N. Omidvar, X. Li, Y. Mei, and X. Yao. 2014. Cooperative co-evolution with differential grouping for large scale optimization. IEEE Transactions on Evolutionary Computation, 18(3), 378--393.Google ScholarCross Ref
- Y. Mei, M. N. Omidvar, X. Li, and X. Yao. 2016. A competitive divide-and-conquer algorithm for unconstrained large-scale black-box optimization. Acm Transactions on Mathematical Software, 42(2), 13. Google ScholarDigital Library
- Y. Sun, M. Kirley, and S. K. Halgamuge. 2015. Extended differential grouping for large scale global optimization with direct and indirect variable interactions. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'15). ACM, Madrid, Spain 26(2), 313--320. Google ScholarDigital Library
- M. N. Omidvar, M. Yang, Y. Mei, X. Li, and X. Yao. 2017. DG2: a faster and more accurate differential grouping for large-scale black-box optimization. IEEE Transactions on Evolutionary Computation, 21(6), 929--942.Google ScholarDigital Library
- Z. Ren, A. Chen, L. Wang, Y. Liang, and B. Pang. 2017. An efficient vector-growth decomposition algorithm for cooperative coevolution in solving large scale problems. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'2017). ACM, Berlin. Germany. 41--42. Google ScholarDigital Library
- X. M. Hu, F. L. He, W. N. Chen, and J. Zhang. 2016. Cooperation coevolution with fast interdependency identification for large scale optimization. Information Sciences an International Journal, 381(C), 142--160. Google ScholarDigital Library
- Y. Sun, M. Kirley, and S. K. Halgamuge. 2017. A Recursive Decomposition Method for Large Scale Continuous Optimization. IEEE Transactions on Evolutionary Computation, 99, 1--1.Google Scholar
- K. Tang, X. Li, P. N. Suganthan, Y. Zhang, and T. Weise. 2009, Benchmark functions for the CEC'2010 special session and competition on largescale global optimization. Technical Report. Nature Inspired Computation and Applications Laboratory, USTC, China, (1), 1--23.Google Scholar
- X. Li, K. Tang, M. N. Omidvar, Y. Zhang, and K. Qin. 2013. Benchmark functions for the CEC'2013 special session and competition on large scale global optimization, Technical Report. Evolutionary Computation and Machine Learning Group, RMIT University, Australia, 1--10.Google Scholar
Index Terms
- A historical interdependency based differential grouping algorithm for large scale global optimization
Recommendations
An efficient vector-growth decomposition algorithm for cooperative coevolution in solving large scale problems
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference CompanionBy taking the idea of divide-and-conquer, cooperative coevolution provides a powerful architecture for large scale optimization problems, but its efficiency depends heavily on the decomposition strategy. Existing decomposition algorithms either cannot ...
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 ...
Investigating the effects of population size and the number of subcomponents on the performance of SHADE algorithm with random adaptive grouping for LSGO problems
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionLarge-scale global optimization (LSGO) problems are known as hard problems for many evolutionary algorithms (EAs). LSGO problems are usually computationally costly, thus an experimental analysis for choosing an appropriate algorithm and its parameter ...
Comments