SHADE with Iterative Local Search for Large-Scale Global Optimization

Global optimization is a very important topic in research due to its wide applications in many real-world problems in science and engineering. Among optimization problems, dimensionality is one of the most crucial issues that increases the difficulty of the optimization process. Thus, Large-Scale Global Optimization, optimization with a great number of variables, arises as a field that is getting an increasing interest. In this paper, we propose a new hybrid algorithm especially designed to tackle this type of optimization problems. The proposal combines, in a iterative way, a modern Differential Evolution algorithm with one local search method chosen from a set of different search methods. The selection of the local search method is dynamic and takes into account the improvement obtained by each of them in the previous intensification phase, to identify the most adequate in each case for the problem. Experiments are carried out using the CEC'2013 Large-Scale Global Optimization benchmark, and the proposal is compared with other state-of-the-art algorithms, showing that the synergy among the different components of our proposal leads to better and more robust results than more complex algorithms. In particular, it improves the results of the current winner of previous Large-Scale Global Optimization competitions, Multiple Offspring Sampling, MOS, obtaining very good results, especially in the most difficult problems.

[1]  Petr Bujok,et al.  L-SHADE with competing strategies applied to CEC2015 learning-based test suite , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[2]  Ponnuthurai N. Suganthan,et al.  Real-parameter evolutionary multimodal optimization - A survey of the state-of-the-art , 2011, Swarm Evol. Comput..

[3]  Jorge Nocedal,et al.  Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization , 1997, TOMS.

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

[5]  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).

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

[7]  Francisco Herrera,et al.  Iterative hybridization of DE with local search for the CEC'2015 special session on large scale global optimization , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

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

[9]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

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

[11]  Francisco Herrera,et al.  A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends , 2015, Int. J. Comput. Intell. Syst..

[12]  Alex S. Fukunaga,et al.  Evaluating the performance of SHADE on CEC 2013 benchmark problems , 2013, 2013 IEEE Congress on Evolutionary Computation.

[13]  Jorge Nocedal,et al.  Remark on “algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization” , 2011, TOMS.

[14]  Antonio LaTorre,et al.  A comprehensive comparison of large scale global optimizers , 2015, Inf. Sci..

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