A minimum population search hybrid for large scale global optimization

Large-scale global optimization is a challenging task which is embedded in many scientific and engineering applications. Among large scale problems, multimodal functions present an exceptional challenge because of the need to promote exploration. In this paper we present a hybrid heuristic specifically designed for optimizing large scale multimodal functions. The hybrid is based on the unbiased exploration ability of Minimum Population Search. Minimum Population Search is a recently developed metaheuristic able to efficiently optimize multimodal functions. However, MPS lacks techniques for exploiting search gradients. To overcome this limitation, we combine its exploration power with the intense local search of the CMA-ES algorithm. The proposed algorithm is evaluated on the test functions provided by the LSGO competition of IEEE Congress of Evolutionary Computation (CEC 2013).

[1]  Stephen Chen,et al.  MINIMUM POPULATION SEARCH - A SCALABLE METAHEURISTIC FOR MULTI-MODAL , 2015 .

[2]  Josien P. W. Pluim,et al.  Evaluation of Optimization Methods for Nonrigid Medical Image Registration Using Mutual Information and B-Splines , 2007, IEEE Transactions on Image Processing.

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

[4]  Wei Chu,et al.  A new evolutionary search strategy for global optimization of high-dimensional problems , 2011, Inf. Sci..

[5]  Francisco Herrera,et al.  Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems , 2011, Soft Comput..

[6]  Antonio LaTorre,et al.  Multiple Offspring Sampling in Large Scale Global Optimization , 2012, 2012 IEEE Congress on Evolutionary Computation.

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

[8]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[9]  Shahryar Rahnamayan,et al.  Metaheuristics in large-scale global continues optimization: A survey , 2015, Inf. Sci..

[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]  Richard Bellman,et al.  9. Dynamic Programming , 1997 .

[12]  Antonio Bolufé Röhler,et al.  Extending Minimum Population Search towards large scale global optimization , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[13]  Antonio Bolufé Röhler,et al.  Measuring the curse of dimensionality and its effects on particle swarm optimization and differential evolution , 2014, Applied Intelligence.

[14]  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.

[15]  Antonio LaTorre,et al.  A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test , 2011, Soft Comput..

[16]  Xiaodong Li,et al.  Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale , 2009 .

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

[18]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[19]  James Montgomery,et al.  A simple strategy for maintaining diversity and reducing crowding in differential evolution , 2012, 2012 IEEE Congress on Evolutionary Computation.

[20]  Bin Li,et al.  Two-stage based ensemble optimization for large-scale global optimization , 2010, IEEE Congress on Evolutionary Computation.

[21]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

[22]  Antonio Bolufé Röhler,et al.  Minimum population search - Lessons from building a heuristic technique with two population members , 2013, 2013 IEEE Congress on Evolutionary Computation.

[23]  Alexander Gelbukh,et al.  Empirical Analysis of a Micro-evolutionary Algorithm for Numerical Optimization , 2011 .

[24]  Bruce L. Golden,et al.  Very large-scale vehicle routing: new test problems, algorithms, and results , 2005, Comput. Oper. Res..

[25]  Francisco Luna,et al.  Solving large-scale real-world telecommunication problems using a grid-based genetic algorithm , 2008 .

[26]  Antonio Bolufé Röhler,et al.  Multi-swarm hybrid for multi-modal optimization , 2012, 2012 IEEE Congress on Evolutionary Computation.

[27]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[28]  James Montgomery,et al.  A Simple Strategy to Maintain Diversity and Reduce Crowding in Particle Swarm Optimization , 2011, Australasian Conference on Artificial Intelligence.

[29]  El-Ghazali Talbi,et al.  A Taxonomy of Hybrid Metaheuristics , 2002, J. Heuristics.