An adaptive coevolutionary Differential Evolution algorithm for large-scale optimization

In this paper, we propose a new algorithm, named JACC-G, for large scale optimization problems. The motivation is to improve our previous work on grouping and adaptive weighting based cooperative coevolution algorithm, DECC-G [1], which uses random grouping strategy to divide the objective vector into subcomponents, and solve each of them in a cyclical fashion. The adaptive weighting mechanism is used to adjust all the subcomponents together at the end of each cycle. In the new JACC-G algorithm: (1) A most recent and efficient Differential Evolution (DE) variant, JADE [2], is employed as the subcomponent optimizer to seek for a better performance; (2) The adaptive weighting is time-consuming and expected to work only in the first few cycles, so a detection module is added to prevent applying it arbitrarily; (3) JADE is also used to optimize the weight vector in adaptive weighting process instead of using a basic DE in previous DECC-G. The efficacy of the proposed JACC-G algorithm is evaluated on two sets of widely used benchmark functions up to 1000 dimensions.

[1]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

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

[3]  Francisco Herrera,et al.  MA-SW-Chains: Memetic algorithm based on local search chains for large scale continuous global optimization , 2010, IEEE Congress on Evolutionary Computation.

[4]  Miguel Rocha,et al.  A Comparison of Algorithms for the Optimization of Fermentation Processes , 2006, 2006 IEEE International Conference on Evolutionary Computation.

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

[6]  Xin Yao,et al.  Making a Difference to Differential Evolution , 2008, Advances in Metaheuristics for Hard Optimization.

[7]  D. Sofge,et al.  A blended population approach to cooperative coevolution for decomposition of complex problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[8]  Amit Konar,et al.  Differential Evolution with Local Neighborhood , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[9]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[10]  Jingqiao Zhang,et al.  Evolutionary optimization of transition probability matrices for credit decision-making , 2010, Eur. J. Oper. Res..

[11]  Jouni Lampinen,et al.  A Fuzzy Adaptive Differential Evolution Algorithm , 2005, Soft Comput..

[12]  Thomas Stützle,et al.  A Racing Algorithm for Configuring Metaheuristics , 2002, GECCO.

[13]  Arthur C. Sanderson,et al.  Differential evolution for discrete optimization: An experimental study on Combinatorial Auction problems , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[14]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[15]  Tapabrata Ray,et al.  A cooperative coevolutionary algorithm with Correlation based Adaptive Variable Partitioning , 2009, 2009 IEEE Congress on Evolutionary Computation.

[16]  A. Kai Qin,et al.  Self-adaptive differential evolution algorithm for numerical optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[17]  Arthur C. Sanderson,et al.  JADE: Self-adaptive differential evolution with fast and reliable convergence performance , 2007, 2007 IEEE Congress on Evolutionary Computation.

[18]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[19]  Mitchell A. Potter,et al.  The design and analysis of a computational model of cooperative coevolution , 1997 .

[20]  Carlos A. Coello Coello,et al.  A comparative study of differential evolution variants for global optimization , 2006, GECCO.

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

[22]  X. Yao,et al.  Scaling up fast evolutionary programming with cooperative coevolution , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[23]  Uday K. Chakraborty,et al.  Advances in Differential Evolution , 2010 .

[24]  Marcus Gallagher,et al.  Statistical Racing Techniques for Improved Empirical Evaluation of Evolutionary Algorithms , 2004, PPSN.

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

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

[27]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.

[28]  N. Iyer,et al.  MONACO - Multi-Objective National Airspace Collaborative Optimization , 2007, 2007 IEEE Aerospace Conference.

[29]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[30]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[31]  Hongfei Teng,et al.  Cooperative Co-evolutionary Differential Evolution for Function Optimization , 2005, ICNC.

[32]  Xiaodong Li,et al.  Cooperative Co-evolution for large scale optimization through more frequent random grouping , 2010, IEEE Congress on Evolutionary Computation.