Decomposition of large-scale constrained problems using a genetic-based search

In this paper, a traditional genetic algorithm with an integer representation is developed to search suitable variable arrangements for decomposition problems into a Cooperative-Coevolution framework for large-scale constrained optimization problems. The function evaluation is based on the Variable Interaction Identification for Constrained Optimization Problems (VIIC), that detects interactions among variables in large-scale problems. The new algorithm is capable of getting variable arrangements where the number of subgroups, as well as variables in each subgroup, are not fixed as in traditional decomposition methods. The proposed method is compared against VIIC with neighborhood strategy (VIICN). Those algorithms are tested on a novel benchmark for large scale constrained problems with 100, 500 and 1000 dimensions. The numerical results indicate that the proposed method outperforms VIICN, with the computational time is greatly reduced, which makes DVIIC a viable method for solving large-scale constrained optimization problems.

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

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

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

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

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

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

[7]  Ruhul A. Sarker,et al.  Using Hybrid Dependency Identification with a Memetic Algorithm for Large Scale Optimization Problems , 2012, SEAL.

[8]  Efren Mezura-Montes,et al.  Towards an improvement of variable interaction identification for large-scale constrained problems , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

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

[10]  Devavrat Shah,et al.  Fast Distributed Algorithms for Computing Separable Functions , 2005, IEEE Transactions on Information Theory.

[11]  Ruhul A. Sarker,et al.  Dependency Identification technique for large scale optimization problems , 2012, 2012 IEEE Congress on Evolutionary Computation.

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

[13]  Karsten Weicker,et al.  On the improvement of coevolutionary optimizers by learning variable interdependencies , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

[15]  Daryl Essam,et al.  Decomposition-based evolutionary algorithm for large scale constrained problems , 2015, Inf. Sci..