An Experimental Study of Combining Evolutionary Algorithms with KD-Tree to Solving Dynamic Optimisation Problems

This paper studies the idea of separating the explored and unexplored regions in the search space to improve change detection and optima tracking. When an optimum is found, a simple sampling technique is used to estimate the basin of attraction of that optimum. This estimated basin is marked as an area already explored. Using a special tree-based data structure named KD-Tree to divide the search space, all explored areas can be separated from unexplored areas. Given such a division, the algorithm can focus more on searching for unexplored areas, spending only minimal resource on monitoring explored areas to detect changes in explored regions. The experiments show that the proposed algorithm has competitive performance, especially when change detection is taken into account in the optimisation process. The new algorithm was proved to have less computational complexity in term of identifying the appropriate sub-population/region for each individual. We also carry out investigations to find out why the algorithm performs well. These investigations reveal a positive impact of using the KD-Tree.

[1]  Mohammad Reza Meybodi,et al.  CellularDE: A Cellular Based Differential Evolution for Dynamic Optimization Problems , 2011, ICANNGA.

[2]  John J. Grefenstette,et al.  Genetic Algorithms for Changing Environments , 1992, PPSN.

[3]  Hendrik Richter,et al.  Detecting change in dynamic fitness landscapes , 2009, 2009 IEEE Congress on Evolutionary Computation.

[4]  Dumitru Dumitrescu,et al.  Evolutionary swarm cooperative optimization in dynamic environments , 2009, Natural Computing.

[5]  Mark Wineberg,et al.  The Shifting Balance Genetic Algorithm: improving the GA in a dynamic environment , 1999 .

[6]  Jürgen Branke,et al.  A Multi-population Approach to Dynamic Optimization Problems , 2000 .

[7]  Tim Blackwell,et al.  Particle Swarm Optimization in Dynamic Environments , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[8]  Chen Shi-ming.  Path planning of mobile robot based on improved Particle Swarm Optimization in dynamic environment , 2008 .

[9]  Arvind S. Mohais,et al.  DynDE: a differential evolution for dynamic optimization problems , 2005, 2005 IEEE Congress on Evolutionary Computation.

[10]  Hartmut Schmeck,et al.  Designing evolutionary algorithms for dynamic optimization problems , 2003 .

[11]  Jürgen Branke,et al.  Multiswarms, exclusion, and anti-convergence in dynamic environments , 2006, IEEE Transactions on Evolutionary Computation.

[12]  Xin Yao,et al.  Continuous Dynamic Constrained Optimization—The Challenges , 2012, IEEE Transactions on Evolutionary Computation.

[13]  Changhe Li,et al.  A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments , 2012, IEEE Transactions on Evolutionary Computation.

[14]  Shengxiang Yang,et al.  Evolutionary dynamic optimization: A survey of the state of the art , 2012, Swarm Evol. Comput..

[15]  Trung Thanh Nguyen,et al.  Continuous dynamic optimisation using evolutionary algorithms , 2011 .

[16]  Hussein A. Abbass,et al.  Tackling Dynamic Problems with Multiobjective Evolutionary Algorithms , 2008, Multiobjective Problem Solving from Nature.

[17]  Rasmus K. Ursem,et al.  Multinational GAs: Multimodal Optimization Techniques in Dynamic Environments , 2000, GECCO.

[18]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

[19]  Ronald W. Morrison,et al.  Designing Evolutionary Algorithms for Dynamic Environments , 2004, Natural Computing Series.

[20]  Xin Yao,et al.  Hybridizing Cultural Algorithms and Local Search , 2006, Ideal.

[21]  Mohammad Reza Meybodi,et al.  A New Particle Swarm Optimization Algorithm for Dynamic Environments , 2010, SEMCCO.

[22]  Xin Yao,et al.  An Experimental Study of Hybridizing Cultural Algorithms and Local Search , 2008, Int. J. Neural Syst..

[23]  Jon Louis Bentley,et al.  Data Structures for Range Searching , 1979, CSUR.

[24]  Janez Brest,et al.  Dynamic optimization using Self-Adaptive Differential Evolution , 2009, 2009 IEEE Congress on Evolutionary Computation.

[25]  Bin Li,et al.  Multi-strategy ensemble particle swarm optimization for dynamic optimization , 2008, Inf. Sci..

[26]  Andries Petrus Engelbrecht,et al.  Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments , 2012, Eur. J. Oper. Res..

[27]  Changhe Li,et al.  A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments , 2010, IEEE Transactions on Evolutionary Computation.