A novel cooperative coevolution for large scale global optimization

For large scale global optimization problems, the efficiency and effectiveness of evolutionary algorithms (EAs) will be much reduced with the dimension increasing. In this paper, a novel evolutionary algorithm is proposed in order to improve the performance of EAs. In the proposed algorithm, on one hand, a variable grouping strategy is introduced. It can group all variables into several subcomponents, while the variables in each subcomponent are non-separable. In this way, a large scale problem can be decomposed into several small scale problems. On the other hand, a filled function with one parameter is integrated into EAs, which can help algorithm to escape from the current local optimal solution and find a better one. The simulations are made on the standard benchmark suite in CEC'2013, and the proposed algorithm is compared with several well performed algorithms. The results indicate the proposed algorithm is more efficient and effective.

[1]  Kenneth A. De Jong,et al.  A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.

[2]  Xin Yao,et al.  Multilevel cooperative coevolution for large scale optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[3]  Hongfei Lin,et al.  A New Filled Function Method with Two Parameters for Global Optimization , 2013, Journal of Optimization Theory and Applications.

[4]  Xiaodong Li,et al.  Cooperative Coevolution With Route Distance Grouping for Large-Scale Capacitated Arc Routing Problems , 2014, IEEE Transactions on Evolutionary Computation.

[5]  Yuping Wang,et al.  A New Filled Function Method with Two Parameters for Global Optimization , 2014, J. Optim. Theory Appl..

[6]  Yuping Wang,et al.  Smoothing and auxiliary functions based cooperative coevolution for global optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

[7]  Zhenyu Yang,et al.  Large-Scale Global Optimization Using Cooperative Coevolution with Variable Interaction Learning , 2010, PPSN.

[8]  Antonio LaTorre,et al.  Large scale global optimization: Experimental results with MOS-based hybrid algorithms , 2013, 2013 IEEE Congress on Evolutionary Computation.

[9]  Xiaodong Li,et al.  Cooperative Co-evolution with delta grouping for large scale non-separable function optimization , 2010, IEEE Congress on Evolutionary Computation.

[10]  Xiaodong Li,et al.  Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization , 2014, IEEE Transactions on Evolutionary Computation.

[11]  Xin Yao,et al.  Large scale evolutionary optimization using cooperative coevolution , 2008, Inf. Sci..

[12]  Ponnuthurai Nagaratnam Suganthan,et al.  Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization , 2008 .

[13]  Xiaodong Li,et al.  Cooperatively Coevolving Particle Swarms for Large Scale Optimization , 2012, IEEE Transactions on Evolutionary Computation.

[14]  Xin Yao,et al.  Self-adaptive differential evolution with neighborhood search , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[15]  Chun Chen,et al.  Multiple trajectory search for Large Scale Global Optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[16]  Xian Liu.  Finding Global Minima with a Computable Filled Function , 2001, J. Glob. Optim..

[17]  Randolph Rach,et al.  Near-field and far-field approximations by the Adomian and asymptotic decomposition methods , 2011, Appl. Math. Comput..

[18]  Liansheng Zhang,et al.  New filled functions for nonsmooth global optimization , 2009 .