Particle Swarm Optimization with Diversive Curiosity - An Endeavor to Enhance Swarm Intelligence

How to manage trade-o between ex- ploitation and exploration in Particle Swarm Opti- mization (PSO) for eciently solving various opti- mization problems is an important issue. In order to prevent premature convergence in PSO search, this paper proposes a new method, Particle Swarm Optimization with Diversive Curiosity (PSO/DC). A key idea of the proposed method is to introduce a mechanism of diversive curiosity into PSO for pre- venting premature convergence and for managing the exploration-exploitation trade-o. Diversive curios- ity is represented by an internal indicator that detects marginal improvement of a swarm of particles for cer- tain number of iterations, and forces them to contin- ually explore an optimal solution to a given optimiza- tion problem. Applications of the proposed method to a 2-dimensional optimization problem well demon- strate its eectiveness. Our experimental results in- dicate that the performance (100%) by the proposed method is superior in terms of success ratio to that (60%) by the PSO model optimized by EPSO, and basically accord with the finding called "the zone of curiosity" in psychology.

[1]  Hong Zhang,et al.  Designing Particle Swarm Optimization: performance comparison of two temporally cumulative fitness functions in EPSO , 2008 .

[2]  Keiki Takadama,et al.  Exploration and Exploitation Trade-Off in Multiagent Learning , 2001 .

[3]  Hong Zhang,et al.  Evolutionary Particle Swarm Optimization (EPSO) - Estimation of Optimal PSO Parameters by GA , 2007, IMECS.

[4]  Gisbert Schneider,et al.  Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training , 2006, BMC Bioinformatics.

[5]  R Spina,et al.  OPTIMIZATION OF INJECTION MOLDED PARTS BY USING ANN-PSO APPROACH , 2006 .

[6]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[7]  Russell C. Eberhart,et al.  Adaptive particle swarm optimization: detection and response to dynamic systems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[8]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[9]  L. J. Eshelman,et al.  chapter Real-Coded Genetic Algorithms and Interval-Schemata , 1993 .

[10]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[11]  Hong Zhang,et al.  An Extended Hybrid Genetic Algorithm for Exploring a Large Search , 2004 .

[12]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[13]  John H. Smith.  Preparation of Papers for the IAENG International Journal of Computer Science , 2009 .

[14]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[15]  H. Day.  Advances in intrinsic motivation and aesthetics , 1981 .

[16]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[17]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[18]  Imtiaz Ahmad,et al.  Particle swarm optimization for task assignment problem , 2002, Microprocess. Microsystems.

[19]  G. Loewenstein.  The psychology of curiosity: A review and reinterpretation. , 1994 .

[20]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[21]  H. I. Day,et al.  Curiosity and the Interested Explorer. , 1982 .

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

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

[24]  James Kennedy.  In Search of the Essential Particle Swarm , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[25]  Joachim F. Wohlwill,et al.  A Conceptual Analysis of Exploratory Behavior , 1981 .

[26]  Pierre-Yves Oudeyer,et al.  Intrinsic Motivation Systems for Autonomous Mental Development , 2007, IEEE Transactions on Evolutionary Computation.

[27]  Masumi Ishikawa,et al.  Improving the Performance of Particle Swarm Optimization with Diversive Curiosity , 2008 .

[28]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[29]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[30]  M Ishikawa,et al.  Evolutionary Particle Swarm Optimization: A Metaoptimization Method with GA for Estimating Optimal PSO Models , 2008 .

[31]  Renbin Xiao,et al.  Two hybrid compaction algorithms for the layout optimization problem , 2007, Biosyst..