Predatory Search Strategy Based on Swarm Intelligence for Continuous Optimization Problems

We propose an approach to solve continuous variable optimization problems. The approach is based on the integration of predatory search strategy (PSS) and swarm intelligence technique. The integration is further based on two newly defined concepts proposed for the PSS, namely, “restriction” and “neighborhood,” and takes the particle swarm optimization (PSO) algorithm as the local optimizer. The PSS is for the switch of exploitation and exploration (in particular by the adjustment of neighborhood), while the swarm intelligence technique is for searching the neighborhood. The proposed approach is thus named PSS-PSO. Five benchmarks are taken as test functions (including both unimodal and multimodal ones) to examine the effectiveness of the PSS-PSO with the seven well-known algorithms. The result of the test shows that the proposed approach PSS-PSO is superior to all the seven algorithms.

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

[2]  Ernesto Costa,et al.  SAPPO: A Simple, Adaptable, Predator Prey Optimiser , 2003, EPIA.

[3]  Ernesto Costa,et al.  An Empirical Comparison of Particle Swarm and Predator Prey Optimisation , 2002, AICS.

[4]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[5]  Kiyoshi Nakamuta,et al.  Mechanism of the switchover from extensive to area-concentrated search behaviour of the ladybird beetle, Coccinella septempunctata bruckii , 1985 .

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

[7]  Witold Pedrycz,et al.  Identifying core sets of discriminatory features using particle swarm optimization , 2009, Expert Syst. Appl..

[8]  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.

[9]  DingweiWANG.  Colony location algorithm for assignment problems , 2004 .

[10]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[11]  INFORMATION-BASED ALGORITHMIC DESIGN OF A NEURAL NETWORK CLASSIFIER , 2014 .

[12]  Ponnuthurai Nagaratnam Suganthan,et al.  Two-lbests based multi-objective particle swarm optimizer , 2011 .

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

[14]  Chong Liu,et al.  Predatory search algorithm with restriction of solution distance , 2005, Biological Cybernetics.

[15]  Jeng-Shyang Pan,et al.  An improved vector particle swarm optimization for constrained optimization problems , 2011, Inf. Sci..

[16]  J. Brickmann.  B. Mandelbrot: The Fractal Geometry of Nature, Freeman and Co., San Francisco 1982. 460 Seiten, Preis: £ 22,75. , 1985 .

[17]  Dingwei Wang,et al.  Particle swarm optimization with a leader and followers , 2008 .

[18]  Sung-Kwun Oh,et al.  Polynomial-based radial basis function neural networks (P-RBF NNs) realized with the aid of particle swarm optimization , 2011, Fuzzy Sets Syst..

[19]  Jing Liu,et al.  A multiagent genetic algorithm for global numerical optimization , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[20]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, ANTS Conference.

[21]  W. J. Bell.  Searching Behavior Patterns in Insects , 1990 .

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

[23]  Maurice Clerc,et al.  Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm , 2009, Swarm Intelligence.

[24]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[25]  G. Odell,et al.  Swarms of Predators Exhibit "Preytaxis" if Individual Predators Use Area-Restricted Search , 1987, The American Naturalist.

[26]  Ricardo de A. Araújo.  Swarm-based translation-invariant morphological prediction method for financial time series forecasting , 2010, Inf. Sci..

[27]  Wenjun Chris Zhang,et al.  An Integrated Road Construction and Resource Planning Approach to the Evacuation of Victims From Single Source to Multiple Destinations , 2010, IEEE Transactions on Intelligent Transportation Systems.

[28]  Honghai Liu,et al.  A Fuzzy Qualitative Framework for Connecting Robot Qualitative and Quantitative Representations , 2008, IEEE Transactions on Fuzzy Systems.

[29]  Prof. Dr. Eberhard Curio.  The Ethology of Predation , 1976, Zoophysiology and Ecology.

[30]  Witold Pedrycz,et al.  Fuzzy vector quantization with the particle swarm optimization: A study in fuzzy granulation-degranulation information processing , 2007, Signal Process..

[31]  Robert G. Reynolds,et al.  Learning the parameters for a gradient-based approach to image segmentation from the results of a region growing approach using cultural algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[32]  Alexandre Linhares,et al.  Synthesizing a predatory search strategy for VLSI layouts , 1999, IEEE Trans. Evol. Comput..

[33]  J. J. Hopfield,et al.  “Neural” computation of decisions in optimization problems , 1985, Biological Cybernetics.

[34]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[35]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[36]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[37]  James Smith,et al.  The Food Searching Behaviour of Two European Thrushes. Ii: the Adaptiveness of the Search Patterns , 1974 .

[38]  Alexandre Linhares.  State-space search strategies gleaned from animal behavior: a traveling salesman experiment , 1998, Biological Cybernetics.

[39]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[40]  James Smith,et al.  The Food Searching Behaviour of Two European Thrushes , 1974 .

[41]  Dingwei Wang,et al.  Colony location algorithm for assignment problems , 2004 .

[42]  Manoj Kumar Tiwari,et al.  Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch , 2008, IEEE Transactions on Evolutionary Computation.

[43]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[44]  Kazuo Furuta,et al.  Evacuation Planning Based on the Contraflow Technique With Consideration of Evacuation Priorities and Traffic Setup Time , 2013, IEEE Transactions on Intelligent Transportation Systems.

[45]  M. Clerc,et al.  Particle Swarm Optimization , 2006 .

[46]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..