Glowworm swarm optimisation: a new method for optimising multi-modal functions

This paper presents an exposition of a new method of swarm intelligence based algorithm for optimising multi-modal functions. The main objective of using this method is to ensure capture of all local maxima of the function. The application of this method is in the area of multiple signal source location or identification of odour sources and hazardous spills. The method is based upon a dynamic decision domain for each agent in the swarm that decides its direction of movement by the strength of the signal picked up from its neighbours. This is somewhat similar to the luciferin induced glow of a glowworm which is used to attract mates or prey. The brighter the glow more is the attraction. The method is memory-less and gradient free and does not require the knowledge of any global information. Moreover, the method is amenable to robotic implementation. Several illustrative examples are given to show the effectiveness of the method in comparison to existing swarm intelligence algorithms.

[1]  Debasish Ghose,et al.  A Glowworm Swarm Optimization Based Multi-robot System for Signal Source Localization , 2009 .

[2]  R. Fierro,et al.  Cooperative hybrid control of robotic sensors for perimeter detection and tracking , 2005, Proceedings of the 2005, American Control Conference, 2005..

[3]  Kalyanmoy Deb,et al.  Comparison of multi-modal optimization algorithms based on evolutionary algorithms , 2006, GECCO.

[4]  Debasish Ghose,et al.  Rendezvous of Glowworm-Inspired Robot Swarms at Multiple Source Locations: A Sound Source Based Real-Robot Implementation , 2006, ANTS Workshop.

[5]  Debasish Ghose,et al.  Detection of multiple source locations using a glowworm metaphor with applications to collective robotics , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[6]  Xiaodong Li,et al.  Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization , 2004, GECCO.

[7]  Frans van den Bergh,et al.  A NICHING PARTICLE SWARM OPTIMIZER , 2002 .

[8]  P. Melin,et al.  Parallel Evolutionary Computing using a cluster for Mathematical Function Optimization , 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.

[9]  Michael V. Jakuba,et al.  Stochastic mapping for chemical plume source localization with application to autonomous hydrothermal vent discovery , 2007 .

[10]  Debasish Ghose,et al.  Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations , 2008, Robotics Auton. Syst..

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

[12]  J. Dréo,et al.  Continuous interacting ant colony algorithm based on dense heterarchy , 2004, Future Gener. Comput. Syst..

[13]  Samir W. Mahfoud.  Crowding and Preselection Revisited , 1992, PPSN.

[14]  Debasish Ghose,et al.  Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications , 2006, Multiagent Grid Syst..

[15]  Debasish Ghose,et al.  Formations of minimalist mobile robots using local-templates and spatially distributed interactions , 2005, Robotics Auton. Syst..

[16]  David E. Goldberg,et al.  Probabilistic Crowding: Deterministic Crowding with Probabilistic Replacement , 1999 .

[17]  Ching-Shyang Chen,et al.  Approximation of multivariate functions and evaluation of particular solutions using Chebyshev polynomial and trigonometric basis functions , 2006 .

[18]  Aimo A. Törn,et al.  Global Optimization , 1999, Science.

[19]  W. Cedeno,et al.  On the use of niching for dynamic landscapes , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[20]  Michael J. Shaw,et al.  A Double-Layered Learning Approach to Acquiring Rules for Classification: Integrating Genetic Algorithms with Similarity-Based Learning , 1994, INFORMS J. Comput..

[21]  Michael J. Shaw,et al.  Genetic algorithms with dynamic niche sharing for multimodal function optimization , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[22]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[23]  Manoj Kumar Tiwari,et al.  Incorporating kin selection in simulated annealing algorithm and its performance evaluation , 2004, Eur. J. Oper. Res..

[24]  Debasish Ghose,et al.  Chasing Multiple Mobile Signal Sources: A Glowworm Swarm Optimization Approach , 2007, IICAI.

[25]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[26]  Alain Pétrowski,et al.  A clearing procedure as a niching method for genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[27]  K N Krishnanand,et al.  Glowworm Swarm Optimization : A Multimodal Function Optimization Paradigm With Applications To Multiple Signal Source Localization Tasks , 2007 .

[28]  Michael N. Vrahatis,et al.  On the computation of all global minimizers through particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[29]  William M. Spears,et al.  Swarms for chemical plume tracing , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[30]  Samir W. Mahfoud.  Niching methods for genetic algorithms , 1996 .

[31]  Debasish Ghose,et al.  Glowworm-inspired robot swarm for simultaneous taxis towards multiple radiation sources , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[32]  Debasish Ghose,et al.  Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions , 2009, Swarm Intelligence.

[33]  Sai-Ming Li,et al.  Forest fire monitoring with multiple small UAVs , 2005, Proceedings of the 2005, American Control Conference, 2005..

[34]  Nadipuram Prasad,et al.  Bio-inspired sensor swarms to detect leaks in pressurized systems , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[35]  J. Kennedy.  Stereotyping: improving particle swarm performance with cluster analysis , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[36]  J. Farrell,et al.  Chemical plume tracing experimental results with a REMUS AUV , 2003, Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492).

[37]  David E. Goldberg,et al.  Implicit Niching in a Learning Classifier System: Nature's Way , 1994, Evolutionary Computation.

[38]  Debasish Ghose,et al.  Multimodal Function Optimization using a Glowworm Metaphor with Applications to Collective Robotics , 2005, IICAI.

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

[40]  Georges R. Harik,et al.  Finding Multimodal Solutions Using Restricted Tournament Selection , 1995, ICGA.

[41]  Debasish Ghose,et al.  Glowworm Swarm Optimization Algorithm for Hazard Sensing in Ubiquitous Environments Using Heterogeneous Agent Swarms , 2008, Soft Computing Applications in Industry.