Large-Scale Global Optimization Using Cooperative Coevolution with Variable Interaction Learning
In recent years, Cooperative Coevolution (CC) was proposed as a promising framework for tackling high-dimensional optimization problems. The main idea of CC-based algorithms is to discover which decision variables, i.e, dimensions, of the search space interact. Non-interacting variables can be optimized as separate problems of lower dimensionality. Interacting variables must be grouped together and optimized jointly. Early research in this area started with simple attempts such as one-dimension based and splitting-in-half methods. Later, more efficient algorithms with new grouping strategies, such as DECC-G and MLCC, were proposed. However, those grouping strategies still cannot sufficiently adapt to different group sizes. In this paper, we propose a new CC framework named Cooperative Coevolution with Variable Interaction Learning (CCVIL), which initially considers all variables as independent and puts each of them into a separate group. Iteratively, it discovers their relations and merges the groups accordingly. The efficiency of the newly proposed framework is evaluated on the set of large-scale optimization benchmarks.
KeywordsVariable Interaction Learning Large-Scale Optimization Numerical Optimization Incremental Group Strategy Cooperative Coevolution
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- 3.Husbands, P., Mill, F.: Simulated co-evolution as the mechanism for emergent planning and scheduling. In: 4th Intl. Conf. on Genetic Algorithms, pp. 264–270. Morgan Kaufmann, San Francisco (1991)Google Scholar
- 5.Yong, C.H., Miikkulainen, R.: Cooperative coevolution of multi-agent systems. Technical Report AI01-287, University of Texas at Austin, Austin, TX, USA (2001)Google Scholar
- 7.Tang, K., Li, X., Suganthan, P.N., Yang, Z., Weise, T.: Benchmark functions for the CEC’2010 special session and competition on large scale global optimization. In: TR, NICAL, USTC, Hefei, Anhui, China (2009), http://nical.ustc.edu.cn/cec10ss.php
- 8.Weicker, K., Weicker, N.: On the improvement of coevolutionary optimizers by learning variable interdependencies. In: IEEE CEC, pp. 1627–1632. IEEE Press, Los Alamitos (1999)Google Scholar
- 9.Potter, M.A., De Jong, K.A.: A cooperative coevolutionary approach to function optimization. In: 3rd Conf. on Parallel Problem Solving from Nature, vol. 2, pp. 249–257 (1994)Google Scholar
- 10.Aickelin, U.: A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems. In: GECCO Late-Breaking Papers, pp. 1–8Google Scholar
- 11.Yang, Z., Tang, K., Yao, X.: Multilevel cooperative coevolution for large scale optimization. In: IEEE Congress on Evolutionary Computation, pp. 1663–1670. IEEE Press, Los Alamitos (2008)Google Scholar
- 15.Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: IEEE Congress on Evolutionary Computation, vol. 2. IEEE Press, Los Alamitos (2005)Google Scholar
- 16.Streeter, M.J.: Upper bounds on the time and space complexity of optimizing additively separable functions. In: GECCO, pp. 186–197. Springer, Heidelberg (2004)Google Scholar