Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
Ant Colony Optimization
A Swarm Approach for Emission Sources Localization
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Adaptive elitist-population based genetic algorithm for multimodal function optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations
Robotics and Autonomous Systems
Glowworm swarm optimisation: a new method for optimising multi-modal functions
International Journal of Computational Intelligence Studies
Robot algorithms for localization of multiple emission sources
ACM Computing Surveys (CSUR)
The improvement of glowworm swarm optimization for continuous optimization problems
Expert Systems with Applications: An International Journal
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This paper presents a novel glowworm metaphor based distributed algorithm that enables a minimalist mobile robot swarm to effectively split into subgroups, exhibit simultaneous taxis towards, and rendezvous at multiple source locations. The locations of interest could represent radiation sources such as nuclear and hazardous aerosol spills spread within an unknown environment. The glowworm algorithm is based on a glowworm swarm optimization (GSO) technique that finds multiple optima of multimodal functions. The algorithm is in the same spirit as the ant-colony optimization (ACO) and particle swarm optimization (PSO) algorithms, but with several significant differences. A key feature of the GSO algorithm is the use of an adaptive local-decision domain, which is used effectively to detect the multiple optimum locations of the multimodal function. We conduct sound source localization experiments, using a set of four wheeled robots (christened Glowworms), to validate the glowworm approach to the problem of multiple source localization. We also examine the behavior of the glowworm algorithm in the presence of uncertainty due to perceptional noise. A comparison with a gradient based approach reveals the superiority of the glowworm algorithm in coping with uncertainty.