Stigmergic Optimization - download pdf or read online

Nonfiction 3

By Ajith Abraham

ISBN-10: 3540346899

ISBN-13: 9783540346890

First studied in social bugs like ants, oblique self-organizing interactions - often called "stigmergy" - happen whilst one person modifies the surroundings and one other consequently responds to the hot atmosphere. the results of self-organizing habit expand to robotics and past. This booklet explores the applying of stigmergy for numerous optimization difficulties. the amount contains 12 chapters together with an introductory bankruptcy conveying the basic definitions, inspirations and study demanding situations.

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65. Gagn´e C, Price WL, Gravel M (2002) Comparing an ACO algorithm with other heuristics for the single machine scheduling problem with sequencedependent setup times. Journal of Operation Research Society, 53, 895-906. 66. Gambardella LM, Dorigo M (1995) Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem. In Proceedings of ML-95, Twelfth International Conference on Machine Learning, Tahoe City, CA, A. Prieditis and S. ), Morgan Kaufmann, 252-260. 67. Gambardella LM, Dorigo M (1996) Solving Symmetric and Asymmetric TSPs by Ant Colonies.

Regarding robots communication – the main issue in this work and a fundamental aspect of multi-robots architectures – there are two possibilities: direct and indirect. Analogously to the biological counterpart, the direct communication consists of sending and receiving messages that are intended to convey information. The indirect version, also known as stigmergy, occurs through the environment and is neither time nor individual specific. Balch and Arking [3] discussed both methods and concluded that the use of direct or intentional communication may be avoided when similar information can be transmitted by modifications imposed to the environment.

6. A nectar forager exploits the flower sites according to their quality while the distance and direction to the sites is communicated to it through waggle dances performed by fellow foragers on the dance floor. In our algorithm, we map the quality of paths onto the quality of nodes for utilizing the bee principle. Consequently, we formulate the quality of a node, for reaching a destination, as a function of proportional quality of only those neighbors that possibly lie in the path toward the destination.

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Stigmergic Optimization by Ajith Abraham

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