The self-organizing exploratory pattern of the argentine ant
TLDR
A minimal model shows how the exploratory pattern may be generated by the individual workers' simple trail-laying and -following behavior, illustrating how complex collective structures in insect colonies may be based on self-organization.Abstract:
Workers of the Argentine ant, Iridomyrmex humilis,start to explore a chemically unmarked territory randomly. As the exploratory front advances, other explorers are recruited and a trail extends from it to the nest. Whereas recruitment trails are generally constructed between two points, these exploratory trails have no fixed destination, and strongly resemble the foraging patterns of army ants. A minimal model shows how the exploratory pattern may be generated by the individual workers' simple trail-laying and -following behavior, illustrating how complex collective structures in insect colonies may be based on self-organization.read more
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Ant Colony Optimization
TL;DR: Ant colony optimization (ACO) is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals as discussed by the authors In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization.
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Metaheuristics in combinatorial optimization: Overview and conceptual comparison
Christian Blum,Andrea Roli +1 more
TL;DR: A survey of the nowadays most important metaheuristics from a conceptual point of view and introduces a framework, that is called the I&D frame, in order to put different intensification and diversification components into relation with each other.
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Ant algorithms for discrete optimization
TL;DR: An overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and the ant colony optimization (ACO) metaheuristic is presented.
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Ant Colony Optimization
TL;DR: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species as discussed by the authors.
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Ant colony optimization: artificial ants as a computational intelligence technique
TL;DR: The introduction of ant colony optimization (ACO) is discussed and all ACO algorithms share the same idea and the ACO is formalized into a meta-heuristics for combinatorial problems.
References
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Journal ArticleDOI
The Chemical Basis of Morphogenesis
TL;DR: In this article, it is suggested that a system of chemical substances, called morphogens, reacting together and diffusing through a tissue, is adequate to account for the main phenomena of morphogenesis.
Journal ArticleDOI
The Chemical Basis of Morphogenesis
TL;DR: A possible mechanism by which the genes of a zygote may determine the anatomical structure of the resulting organism is discussed, suggesting that certain well-known physical laws are sufficient to account for many of the facts.
Journal ArticleDOI
Initiation of slime mold aggregation viewed as an instability.
Evelyn Fox Keller,Lee A. Segel +1 more
TL;DR: A mathematical formulation of the general interaction of amoebae, as mediated by acrasin is presented, and a detailed analysis of the aggregation process is provided.
Book
Order out of chaos : man's new dialogue with nature.
Ilya Prigogine,Isabelle Stengers +1 more
TL;DR: Ilya Prigogine, winner of the Nobel Prize in 1977 for his work on the thermodynamics of non-equilibrium systems, makes his ideas accessible to a wide audience in this book, which has engendered massive debate in Europe and America as discussed by the authors.