Cooperative Coevolution for Large-Scale Optimization Based on Kernel Fuzzy Clustering and Variable Trust Region Methods

Large-scale optimization arises in a variety of scientific and engineering applications. In this paper, a particle swarm optimization (PSO) approach with dynamic neighborhood that is based on kernel fuzzy clustering and variable trust region methods (called FT-DNPSO) is proposed for large-scale optimization. The cooperative coevolution incorporated with a kernel fuzzy C-means clustering strategy is introduced to divide high-dimensional problems in to subproblems, and explore their search spaces. Furthermore, the independent variable ranges change adaptably by using the variable trust region learning method, which expedites the convergence process and explores in the effective space. In addition, the dynamic neighborhood topology assists the PSO algorithm in cooperating with neighbor particles and avoids the problem of premature convergence. Simulation results substantiate the effectiveness of the proposed algorithm to solve large-scale optimization problems with many well-known benchmark functions.

[1]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[2]  J. D. Schaffer,et al.  Real-Coded Genetic Algorithms and Interval-Schemata , 1992, FOGA.

[3]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[4]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[5]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[6]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[7]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[8]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[9]  Andries Petrus Engelbrecht,et al.  Using neighbourhoods with the guaranteed convergence PSO , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

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

[11]  K. Uosaki,et al.  Improvement of Particle Swarm Optimization for High-Dimensional Space , 2006, 2006 SICE-ICASE International Joint Conference.

[12]  J. Kennedy,et al.  Neighborhood topologies in fully informed and best-of-neighborhood particle swarms , 2003, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[13]  Jing J. Liang,et al.  Dynamic Multi-Swarm Particle Swarm Optimizer with a Novel Constraint-Handling Mechanism , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[14]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[15]  Mrinal Kanti Naskar,et al.  A position based algorithm for topology management in Mobile Ad-Hoc Networks enduring limited transmission failures , 2007 .

[16]  Hassan M. Emara,et al.  Clubs-based Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[17]  Wen-Chih Peng,et al.  Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Veysel Gazi,et al.  Particle swarm optimization with dynamic neighborhood topology: Three neighborhood strategies and preliminary results , 2008, 2008 IEEE Swarm Intelligence Symposium.

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

[20]  Andries Petrus Engelbrecht,et al.  Particle swarm optimization with spatially meaningful neighbours , 2008, 2008 IEEE Swarm Intelligence Symposium.

[21]  J. Hazra,et al.  Congestion management using multiobjective particle swarm optimization , 2007, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[22]  Yuren Zhou,et al.  Runtime Analysis of an Ant Colony Optimization Algorithm for TSP Instances , 2009, IEEE Transactions on Evolutionary Computation.

[23]  Lin Xin,et al.  Application of neighbourhood topology particle swarm optimization to cylinder linear induction motor design , 2009, 2009 IEEE International Conference on Automation and Logistics.

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

[25]  Gary G. Yen,et al.  Dynamic Multiple Swarms in Multiobjective Particle Swarm Optimization , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[26]  Min Han,et al.  Particle swarm optimization using dynamic neighborhood topology for large scale optimization , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[27]  A. J. Yuste,et al.  Knowledge Acquisition in Fuzzy-Rule-Based Systems With Particle-Swarm Optimization , 2010, IEEE Transactions on Fuzzy Systems.

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

[29]  Ponnuthurai N. Suganthan,et al.  Differential evolution with ensemble of constraint handling techniques for solving CEC 2010 benchmark problems , 2010, IEEE Congress on Evolutionary Computation.

[30]  Francisco Herrera,et al.  Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power , 2010, Inf. Sci..

[31]  Ponnuthurai N. Suganthan,et al.  Dynamic multi-swarm particle swarm optimizer with sub-regional harmony search , 2010, IEEE Congress on Evolutionary Computation.

[32]  Shu-Kai S. Fan,et al.  Dynamic multi-swarm particle swarm optimizer using parallel PC cluster systems for global optimization of large-scale multimodal functions , 2010 .

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

[34]  P. Subbaraj,et al.  Parallel particle swarm optimization with modified stochastic acceleration factors for solving large scale economic dispatch problem , 2010 .

[35]  Rong-Jong Wai,et al.  On-Line Supervisory Control Design for Maglev Transportation System via Total Sliding-Mode Approach and Particle Swarm Optimization , 2010, IEEE Transactions on Automatic Control.

[36]  Thomas Stützle,et al.  An incremental particle swarm for large-scale continuous optimization problems: an example of tuning-in-the-loop (re)design of optimization algorithms , 2011, Soft Comput..

[37]  Ponnuthurai N. Suganthan,et al.  Self-adaptive differential evolution with multi-trajectory search for large-scale optimization , 2011, Soft Comput..

[38]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[39]  José Mario Martínez,et al.  Outer Trust-Region Method for Constrained Optimization , 2011, J. Optim. Theory Appl..

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

[41]  C. L. Philip Chen,et al.  A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[42]  Enrique Alba,et al.  Restart particle swarm optimization with velocity modulation: a scalability test , 2011, Soft Comput..

[43]  Yuhui Shi,et al.  Population diversity based study on search information propagation in particle swarm optimization , 2012, 2012 IEEE Congress on Evolutionary Computation.

[44]  Bernard De Baets,et al.  Zadeh’s Extension Principle for Continuous Functions of Non-Interactive Variables: A Parallel Optimization Approach , 2012, IEEE Transactions on Fuzzy Systems.

[45]  Mingyue Ding,et al.  Phase Angle-Encoded and Quantum-Behaved Particle Swarm Optimization Applied to Three-Dimensional Route Planning for UAV , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[46]  H.-Y. Zhang,et al.  Multicriteria Decision-Making Approach Based on Atanassov's Intuitionistic Fuzzy Sets With Incomplete Certain Information on Weights , 2013, IEEE Transactions on Fuzzy Systems.