Glowworm Swarm Optimization (GSO) Algorithm for Optimization Problems: A State-of-the-Art Review


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Glowworm Swarm Optimization (GSO) algorithm is a derivative-free, meta-heuristic algorithm and mimicking the glow behavior of glowworms which can efficiently capture all the maximum multimodal function. Nevertheless, there are several weaknesses to locate the global optimum solution for instance low calculation accuracy, simply falling into the local optimum, convergence rate of success and slow speed to converge. This paper reviews the exposition of a new method of swarm intelligence in solving optimization problems using GSO. Recently the GSO algorithm was used simultaneously to find solutions of multimodal function optimization problem in various fields in today industry such as science, engineering, network and robotic. From the paper review, we could conclude that the basic GSO algorithm, GSO with modification or improvement and GSO with hybridization are considered by previous researchers in order to solve the optimization problem. However, based on the literature review, many researchers applied basic GSO algorithm in their research rather than others.



Edited by:

Wei Deng and Qi Luo




N. Zainal et al., "Glowworm Swarm Optimization (GSO) Algorithm for Optimization Problems: A State-of-the-Art Review", Applied Mechanics and Materials, Vol. 421, pp. 507-511, 2013

Online since:

September 2013




[1] Zhang, J. L., Zhou, G., & Zhou, Y. Q. (2010). A new artificial glowworm swarm optimization algorithm based on chaos method. Quantitative Logic and Soft Computing 2010, 683-693.


[2] Krishnanand, K., & Ghose, D. (2009). A glowworm swarm optimization based multi-robot system for signal source localization. Design and Control of Intelligent Robotic Systems, 49-68.


[3] Yang, Y., Zhou, Y., & Gong, Q. (2010). Hybrid artificial glowworm swarm optimization algorithm for solving system of nonlinear equations. Journal of Computational Information Systems, 6(10), 3431-3438.

[4] Krishnanand, K. N., & Ghose, D. (2006, June). Theoretical foundations for multiple rendezvous of glowworm-inspired mobile agents with variable local-decision domains. In American Control Conference, 2006 (p.6-pp). IEEE.


[5] Wu, B., Qian, C., Ni, W., & Fan, S. (2012). The improvement of glowworm swarm optimization for continuous optimization problems. Expert Systems with Applications.


[6] Oramus, P. (2010). Improvements to Glowworm Swarm Optimization Algorithm. Computer Science, 11, 7-20.

[7] He, D. X., & Zhu, H. Z. (2011). An improved glowworm swarm optimization algorithm for high-dimensional function optimization. Energy Procedia, 13, 5657-5664.


[8] Zhou, Y., Liu, J., & Zhao, G. Leader Glowworm Swarm Optimization Algorithm for Solving Nonlinear Equations Systems.

[9] Zhao, G., Zhou, Y., & Wang, Y. (2011). Using Complex Method Guidance GSO Swarm Algorithm for Solving High Dimensional Function Optimization Problem. Journal of Convergence Information Technology, 6(11), 352-360.


[10] Huang, K., Zhou, Y., & Wang, Y. (2011). Niching Glowworm Swarm Optimization Algorithm with Mating Behavior.

[11] Krishnanand, K. N., & Ghose, K. V. R. D. A Network Robot System for Multiple Odor Source Localization using Glowworm Swarm Optimization Algorithm.

[12] Krishnanand, K., & Ghose, D. (2009). Glowworm swarm optimization for searching higher dimensional spaces. Innovations in Swarm Intelligence, 61-75.


[13] Krishnanand, K. N., Amruth, P., Guruprasad, M. H., Bidargaddi, S. V., & Ghose, D. (2006, May). Glowworm-inspired robot swarm for simultaneous taxis towards multiple radiation sources. In Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on (pp.958-963.


[14] Kaipa, K., Puttappa, A., Hegde, G., Bidargaddi, S., & Ghose, D. (2006).

[15] Krishnanand, K., & Ghose, D. (2008). Glowworm swarm optimization algorithm for hazard sensing in ubiquitous environments using heterogeneous agent swarms. Soft Computing Applications in Industry, 165-187.


[16] Krishnanand, K. N., & Ghose, D. (2008). Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations. Robotics and Autonomous Systems, 56(7), 549-569.


[17] Qua, L., Hea, D., & Wua, J. (2011). Hybrid Coevolutionary Glowworm Swarm Optimization Algorithm for Fixed Point Equation.

[18] Yuli, Z., Xiaoping, M., & Yanzi, M. (2011, July). Localization of multiple odor sources using modified glowworm swarm optimization with collective robots. In Control Conference (CCC), 2011 30th Chinese (pp.1899-1904). IEEE.