Abstract
Glowworm swarm optimization (GSO) algorithm is a novel algorithm based on swarm intelligence and inspired from light emission behavior of glowworms to attract a peer or prey in nature. The main application of this algorithm is to capture all local optima of multimodal function. GSO algorithm has shown some such weaknesses in global search as low accuracy computation and easy to fall into local optimum. In order to overcome above disadvantages of GSO, this paper presented an improved GSO algorithm, which called parallel hybrid mutation glowworm swarm optimization (PHMGSO) algorithm. Experimental results show that PHMGSO has higher calculation accuracy and convergence faster speed compared to standard GSO and PSO algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Krishnanand, K.N., Ghose, D.: Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings of IEEE Swarm Intelligence Symposium, pp. 84–91 (2005)
Krishnanand, K.N., Ghose, D.: Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal fuctions. Swarm Intell 3, 87–124 (2009)
Zhang, J.-l., Zhou, G., Zhou, Y.-Q.: A new artificial glowworm swarm optimization algorithm based on chaos method. In: Cao, B.-y., Wang, G.-j., Chen, S.-l., Guo, S.-z. (eds.) Quantitative Logic and Soft Computing 2010. Advances in Intelligent Systems and Computing, vol. 82, pp. 683–693. Springer, Heidelberg (2010)
Yanmin, L., Ben, N.: A Novel PSO Model Based on Simulating Human Socia Communication Behaviorl. Discrere Dynamics Nature Society, Artilcle ID797373, 21pages (2012)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Networks, vol. 4, pp. 1942–1948 (1995)
Krishnanand, K.N., Ghose, D.: A glowworm swarm optimization based multi-robot system for signal source localization. In: Liu, D., Wang, L., Tan, K.C. (eds.) Design and Control of Intelligent Robotic Systems. SCI, vol. 177, pp. 49–68. Springer, Heidelberg (2009)
Krishnanand, K.N., Ghose, D.: Theoretical foundations for redezvous of glowworm inspired agent swarms at multiple lcations. Robotics and Autonomous Systems 56(7), 549–569 (2007)
Zhe, O., Zhou, Y.: Self-adaptive step glowworm swarm optimization algorithm. Journal of Computer Applications 31(7), 115–118 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tang, Z., Zhou, Y., Chen, X. (2013). An Improved Glowworm Swarm Optimization Algorithm Based on Parallel Hybrid Mutation. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-39482-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39481-2
Online ISBN: 978-3-642-39482-9
eBook Packages: Computer ScienceComputer Science (R0)