Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior

Nature-inspired optimization algorithms, notably evolutionary algorithms (EAs), have been widely used to solve various scientific and engineering problems because of to their simplicity and flexibility. Here we report a novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior. The framework is mainly based on the producer-scrounger model, which assumes that group members search either for ldquofindingrdquo (producer) or for ldquojoiningrdquo (scrounger) opportunities. Based on this framework, concepts from animal searching behavior, e.g., animal scanning mechanisms, are employed metaphorically to design optimum searching strategies for solving continuous optimization problems. When tested against benchmark functions, in low and high dimensions, the GSO algorithm has competitive performance to other EAs in terms of accuracy and convergence speed, especially on high-dimensional multimodal problems. The GSO algorithm is also applied to train artificial neural networks. The promising results on three real-world benchmark problems show the applicability of GSO for problem solving.

[1]  Xin Yao,et al.  A new evolutionary system for evolving artificial neural networks , 1997, IEEE Trans. Neural Networks.

[2]  Xin Yao,et al.  A constructive algorithm for training cooperative neural network ensembles , 2003, IEEE Trans. Neural Networks.

[3]  Q. Henry Wu,et al.  A Novel Group Search Optimizer Inspired by Animal Behavioural Ecology , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[4]  Chandrika Kamath,et al.  Inducing oblique decision trees with evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[5]  L. Giraldeau,et al.  Food exploitation: searching for the optimal joining policy. , 1999, Trends in ecology & evolution.

[6]  Brian Birge,et al.  PSOt - a particle swarm optimization toolbox for use with Matlab , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[7]  G. Theraulaz,et al.  Inspiration for optimization from social insect behaviour , 2000, Nature.

[8]  Thomas Caraco,et al.  Time Budgeting and Group Size: A Test of Theory , 1979 .

[9]  Johan A. K. Suykens,et al.  Benchmarking Least Squares Support Vector Machine Classifiers , 2004, Machine Learning.

[10]  D. Harper.  Competitive foraging in mallards: “Ideal free’ ducks , 1982, Animal Behaviour.

[11]  Bernard Zenko,et al.  Is Combining Classifiers with Stacking Better than Selecting the Best One? , 2004, Machine Learning.

[12]  Xin Yao,et al.  Scaling Up Evolutionary Programming Algorithms , 1998, Evolutionary Programming.

[13]  Richard E. Strauss,et al.  Discrimination and classification of foraging paths produced by search-tactic models , 2004 .

[14]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[15]  L. Giraldeau,et al.  Exchangeable producer and scrounger roles in a captive flock of feral pigeons: a case for the skill pool effect , 1986, Animal Behaviour.

[16]  B. Roitberg.  Searching Behavior: the Behavioral Ecology of Finding Resources , 1992 .

[17]  D. Mustard.  Numerical Integration over the n-Dimensional Spherical Shell , 1964 .

[18]  Douglas C. Montgomery,et al.  Statistical Quality Control , 2008 .

[19]  X. Yao,et al.  Scaling up fast evolutionary programming with cooperative coevolution , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[20]  H. Basford,et al.  Optimal eye movement strategies in visual search , 2005 .

[21]  Marco Laumanns,et al.  A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study , 1998, PPSN.

[22]  Xin Yao,et al.  Evolutionary ensembles with negative correlation learning , 2000, IEEE Trans. Evol. Comput..

[23]  E. Cantu-Paz,et al.  An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[24]  Xin Yao,et al.  Fast Evolution Strategies , 1997, Evolutionary Programming.

[25]  Jacob Barhen,et al.  TRUST: A deterministic algorithm for global optimization , 1997 .

[26]  Shiro Usui,et al.  Mutation-based genetic neural network , 2005, IEEE Transactions on Neural Networks.

[27]  D. Wolpert,et al.  No Free Lunch Theorems for Search , 1995 .

[28]  Edmund J. Collins,et al.  Optimality Models in Behavioral Biology , 2001, SIAM Rev..

[29]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[30]  Tapabrata Ray,et al.  Society and civilization: An optimization algorithm based on the simulation of social behavior , 2003, IEEE Trans. Evol. Comput..

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

[32]  R. Sibly,et al.  Producers and scroungers: A general model and its application to captive flocks of house sparrows , 1981, Animal Behaviour.

[33]  I. Couzin,et al.  Effective leadership and decision-making in animal groups on the move , 2005, Nature.

[34]  Petros Koumoutsakos,et al.  Optimization based on bacterial chemotaxis , 2002, IEEE Trans. Evol. Comput..

[35]  S. Liversedge,et al.  Saccadic eye movements and cognition , 2000, Trends in Cognitive Sciences.

[36]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[37]  Hans-Paul Schwefel,et al.  Evolution and Optimum Seeking: The Sixth Generation , 1993 .

[38]  Ulf Grenander,et al.  A stochastic nonlinear model for coordinated bird flocks , 1990 .

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

[40]  Jean B. Lasserre,et al.  Global Optimization with Polynomials and the Problem of Moments , 2000, SIAM J. Optim..

[41]  A. Dixon.  AN EXPERIMENTAL STUDY OF THE SEARCHING BEHAVIOUR OF THE PREDATORY COCCINELLID BEETLE ADALIA DECEMPUNCTATA (L.) , 1959 .

[42]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[43]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[44]  César Hervás-Martínez,et al.  Cooperative coevolution of artificial neural network ensembles for pattern classification , 2005, IEEE Transactions on Evolutionary Computation.

[45]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1998 .

[46]  G DietterichThomas.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees , 2000 .

[47]  César Hervás-Martínez,et al.  COVNET: a cooperative coevolutionary model for evolving artificial neural networks , 2003, IEEE Trans. Neural Networks.

[48]  W. J. O'brien,et al.  A new view of the predation cycle of a planktivorous fish, white crappie (Pomoxis annularis) , 1986 .

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

[50]  H. Stanley,et al.  Optimizing the success of random searches , 1999, Nature.

[51]  Volker Nissen,et al.  Evolutionary Algorithms in Management Applications , 1995 .

[52]  H. Jane Brockmann,et al.  Kleptoparasitism in birds , 1979, Animal Behaviour.

[53]  H. Ronald Pulliam,et al.  Chapter 3 – SOCIAL ORGANIZATION IN THE NONREPRODUCTIVE SEASON , 1982 .

[54]  Martin Fodslette Meiller.  A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1993 .

[55]  C. Clark,et al.  Foraging and Flocking Strategies: Information in an Uncertain Environment , 1984, The American Naturalist.

[56]  David B. Fogel,et al.  The Advantages of Evolutionary Computation , 1997, BCEC.

[57]  Christopher R. Houck,et al.  A Genetic Algorithm for Function Optimization: A Matlab Implementation , 2001 .

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

[59]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[60]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[61]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[62]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..