A Complementary Cyber Swarm Algorithm

A recent study Yin et al., 2010 showed that combining particle swarm optimization PSO with the strategies of scatter search SS and path relinking PR produces a Cyber Swarm Algorithm that creates a more effective form of PSO than methods that do not incorporate such mechanisms. This paper proposes a Complementary Cyber Swarm Algorithm C/CyberSA that performs in the same league as the original Cyber Swarm Algorithm but adopts different sets of ideas from the tabu search TS and the SS/PR template. The C/CyberSA exploits the guidance information and restriction information produced in the history of swarm search and the manipulation of adaptive memory. Responsive strategies using long term memory and path relinking implementations are proposed that make use of critical events encountered in the search. Experimental results with a large set of challenging test functions show that the C/CyberSA outperforms two recently proposed swarm-based methods by finding more optimal solutions while simultaneously using a smaller number of function evaluations. The C/CyberSA approach further produces improvements comparable to those obtained by the original CyberSA in relation to the Standard PSO 2007 method Clerc, 2008.

[1]  Levent Yilmaz,et al.  An Information Foraging Model of Knowledge Creation and Spillover Dynamics in Open Source Science , 2012, Int. J. Agent Technol. Syst..

[2]  Michel Gendreau,et al.  Path relinking for the vehicle routing problem , 2004, J. Heuristics.

[3]  Panos M. Pardalos,et al.  Global optimization by continuous grasp , 2007, Optim. Lett..

[4]  Peng-Yeng Yin,et al.  Cyber Swarm optimization for general keyboard arrangement problem , 2011 .

[5]  Milind Tambe,et al.  Introducing Multiagent Systems to Undergraduates through Games and Chocolate , 2012 .

[6]  Deborah Richards,et al.  Multi-Agent Systems for Education and Interactive Entertainment: Design, Use and Experience , 2010 .

[7]  Fred W. Glover,et al.  An Experimental Evaluation of a Scatter Search for the Linear Ordering Problem , 2001, J. Glob. Optim..

[8]  Fred W. Glover,et al.  Multistart Tabu Search and Diversification Strategies for the Quadratic Assignment Problem , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[9]  D. M. Deighton,et al.  Computers in Operations Research , 1977, Aust. Comput. J..

[10]  Yoshikazu Fukuyama,et al.  A hybrid particle swarm optimization for distribution state estimation , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[11]  Manish Arora,et al.  Design of Multi Agent System for Resource Allocation and Monitoring , 2011, Int. J. Agent Technol. Syst..

[12]  Patrick Siarry,et al.  A hybrid method combining continuous tabu search and Nelder-Mead simplex algorithms for the global optimization of multiminima functions , 2005, Eur. J. Oper. Res..

[13]  Goran Trajkovski,et al.  Developments in Intelligent Agent Technologies and Multi-Agent Systems: Concepts and Applications , 2010 .

[14]  Koichi Kurumatani,et al.  New Approach to Smooth Traffic Flow with Route Information Sharing , 2009, Multi-Agent Systems for Traffic and Transportation Engineering.

[15]  Mohd Sharifuddin Ahmad,et al.  A Collaborative Framework for Multiagent Systems , 2011, Int. J. Agent Technol. Syst..

[16]  Fred W. Glover,et al.  Cyber Swarm Algorithms - Improving particle swarm optimization using adaptive memory strategies , 2010, Eur. J. Oper. Res..

[17]  Francisco Gortázar,et al.  Path relinking for large-scale global optimization , 2011, Soft Comput..

[18]  Mingyuan Zhang Theoretical and Practical Frameworks for Agent-Based Systems , 2012 .

[19]  Amir Nakib,et al.  A New Multiagent Algorithm for Dynamic Continuous Optimization , 2010, Int. J. Appl. Metaheuristic Comput..

[20]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[21]  Graçaliz Pereira Dimuro,et al.  A Minimal Dynamical MAS Organization Model , 2009, Handbook of Research on Multi-Agent Systems.

[22]  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).

[23]  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).

[24]  Hong Lin Architectural Design of Multi-Agent Systems: Technologies and Techniques , 2007 .

[25]  Abraham Duarte,et al.  Adaptive Memory Programming for Global Optimization , .

[26]  Fred W. Glover,et al.  Neighborhood analysis: a case study on curriculum-based course timetabling , 2011, J. Heuristics.

[27]  Fred W. Glover,et al.  ' s personal copy Continuous Optimization Finding local optima of high-dimensional functions using direct search methods , 2008 .

[28]  Fred Glover,et al.  Tabu Search and Adaptive Memory Programming — Advances, Applications and Challenges , 1997 .

[29]  Fred W. Glover,et al.  A Template for Scatter Search and Path Relinking , 1997, Artificial Evolution.

[30]  Wei Kong,et al.  Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data , 2008, Comput. Biol. Chem..

[31]  Yu-Xuan Wang,et al.  Hybrid particle swarm optimizer with tabu strategy for global numerical optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[32]  Fred W. Glover,et al.  Ejection Chains, Reference Structures and Alternating Path Methods for Traveling Salesman Problems , 1996, Discret. Appl. Math..

[33]  Virginia. Virginia Dignum . Dignum,et al.  Handbook of Research on Multi-Agent Systems - Semantics and Dynamics of Organizational Models , 2009, Handbook of Research on Multi-Agent Systems.

[34]  Patrick Siarry,et al.  Tabu Search applied to global optimization , 2000, Eur. J. Oper. Res..

[35]  Charles Audet,et al.  Analysis of Generalized Pattern Searches , 2000, SIAM J. Optim..

[36]  Fred W. Glover,et al.  Ejection chain and filter-and-fan methods in combinatorial optimization , 2006, 4OR.

[37]  Stefano Balbi,et al.  Agent-Based Modelling of Socio-Ecosystems: A Methodology for the Analysis of Adaptation to Climate Change , 2010, Int. J. Agent Technol. Syst..

[38]  Josefina Santibáñez,et al.  Propositional Logic Syntax Acquisition Using Induction and Self-Organisation , 2009 .

[39]  Kenneth Sörensen,et al.  "Multiple Neighbourhood" Search in Commercial VRP Packages: Evolving Towards Self-Adaptive Methods , 2008, Adaptive and Multilevel Metaheuristics.

[40]  Luís N. Vicente,et al.  A particle swarm pattern search method for bound constrained global optimization , 2007, J. Glob. Optim..

[41]  Evelina Lamma,et al.  Modelling Interactions via Commitments and Expectations , 2009, Handbook of Research on Multi-Agent Systems.

[42]  Ching-Yi Chen,et al.  Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression , 2007, Expert Syst. Appl..

[43]  X. Yao Evolving Artificial Neural Networks , 1999 .

[44]  Rafael Martí,et al.  Experimental Testing of Advanced Scatter Search Designs for Global Optimization of Multimodal Functions , 2005, J. Glob. Optim..

[45]  Vladimiro Miranda,et al.  EPSO: Evolutionary Particle Swarms , 2007, Advances in Evolutionary Computing for System Design.

[46]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

[47]  Ana L. C. Bazzan,et al.  Multi-Agent Systems for Traffic and Transportation Engineering , 2009 .

[48]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[49]  Yasushi Kambayashi,et al.  Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization , 2010 .

[50]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[51]  Giulia Andrighetto,et al.  Norm and Social Compliance: A Computational Study , 2010, Int. J. Agent Technol. Syst..

[52]  Hirotaka Yoshida,et al.  A PARTICLE SWARM OPTIMIZATION FOR REACTIVE POWER AND VOLTAGE CONTROL CONSIDERING VOLTAGE STABILITY , 2000 .

[53]  Rafael Martí,et al.  Scatter Search: Diseño Básico y Estrategias avanzadas , 2002, Inteligencia Artif..

[54]  J. Teng,et al.  A Novel ACS-Based Optimum Switch Relocation Method , 2002, IEEE Power Engineering Review.

[55]  Goran Trajkovski,et al.  Handbook of Research on Agent-Based Societies: Social and Cultural Interactions , 2009 .

[56]  Kazuteru Miyazaki,et al.  Exploitation-Oriented Learning XoL: A New Approach to Machine Learning Based on Trial-and-Error Searches , 2011 .

[57]  Fred W. Glover,et al.  Tabu Search for Nonlinear and Parametric Optimization (with Links to Genetic Algorithms) , 1994, Discret. Appl. Math..

[58]  Atsushi Ishigame,et al.  Particle swarm optimization based on the concept of tabu search , 2007, 2007 IEEE Congress on Evolutionary Computation.

[59]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[60]  Masao Fukushima,et al.  Tabu Search directed by direct search methods for nonlinear global optimization , 2006, Eur. J. Oper. Res..

[61]  Fred W. Glover,et al.  Hybrid scatter tabu search for unconstrained global optimization , 2011, Ann. Oper. Res..

[62]  Thomas Kiel Rasmussen,et al.  Hybrid Particle Swarm Optimiser with breeding and subpopulations , 2001 .

[63]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.