ant colonies, and more generally social insect societies, are distributed systems that, in spite of the simplicity of their individuals, present a highly structured social organization. As a result of this organizatio...
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ant colonies, and more generally social insect societies, are distributed systems that, in spite of the simplicity of their individuals, present a highly structured social organization. As a result of this organization, ant colonies can accomplish complex tasks that in some cases far exceed the individual capacities of a single ant. The study of ant colonies behavior and of their self-organizing capacities is interesting for computer scientists because it provides models of distributed organization which are useful to solve difficult optimization and distributed control problems. In this paper we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we show how these models have inspired a number of novel algorithms for the solution of distributed optimization and distributed control problems. (C) 2000 Elsevier Science B.V. All rights reserved.
This article presents 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 introduces the a...
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This article presents 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 introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.
One of the tools in the gamut of global optimization search procedures is ant algorithms, inspired by the behaviour of the well-known insects-ants. Natural ant colonies exhibit ad-hoc decision-making processes in thei...
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One of the tools in the gamut of global optimization search procedures is ant algorithms, inspired by the behaviour of the well-known insects-ants. Natural ant colonies exhibit ad-hoc decision-making processes in their day-to-day living activities, such as foraging and brooding. These processes could be modelled and used as tools to solve many practical scheduling problems that are present in current manufacturing environments. This paper proposes web-based ant colony system algorithm ( WACSA) optimization procedures to solve several real-world manufacturing systems problems. The problems considered are: ( 1) single-machine scheduling optimization considering tool wear;( 2) drilling sequence optimization;and ( 3) single-machine scheduling considering total job changeover cost. Results indicate that WACSA provides an optimal solution quickly. It also shows that the ant algorithm is preferred over existing meta-heuristics, as it provides a high level of scheduling flexibility.
In most ant algorithms, the role of each ant is to build a solution in a constructive way, basing each decision on the greedy force and the trails. However, different roles are possible for each individual ant, rangin...
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In most ant algorithms, the role of each ant is to build a solution in a constructive way, basing each decision on the greedy force and the trails. However, different roles are possible for each individual ant, ranging from a negligible help in the decision process to a refined local search heuristic. In this paper, the importance of the role assigned to each ant is discussed. Three general ant methodologies are presented. Comparative results are analyzed for the well-known graph coloring problem.
In this article there are presented problems of using ant algorithms in diagnosis. It was elaborated a knowledge representation and searching solution mechanism with help of ant algorithms. Running algorithm is shown ...
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ISBN:
(纸本)9789665335870
In this article there are presented problems of using ant algorithms in diagnosis. It was elaborated a knowledge representation and searching solution mechanism with help of ant algorithms. Running algorithm is shown by examplary system diagnosing TV set.
An ant colony algorithm is considered for the problem ofmapping requests onto physical resources of data centers. Results from an experimental study of the properties of the algorithm are presented, and it is compared...
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ant algorithms are optimisation algorithms inspired by the foraging behaviour of real ants in the wild. Introduced in the early 1990s, ant algorithms aim at finding approximate solutions to optimisation problems throu...
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ant algorithms are optimisation algorithms inspired by the foraging behaviour of real ants in the wild. Introduced in the early 1990s, ant algorithms aim at finding approximate solutions to optimisation problems through the use of artificial ants and their indirect communication via synthetic pheromones. The first ant algorithms and their development into the ant Colony Optimisation (ACO) metaheuristic is described herein. An overview of past and present typical applications as well as more specialised and novel applications is given. The use of ant algorithms alongside more traditional machine learning techniques to produce robust, hybrid, optimisation algorithms is addressed, with a look towards future developments in this area of study. (C) 2009 Elsevier Ltd. All rights reserved.
A proof of convergence for ant algorithms is developed. ant algorithms were modeled as branching random processes: the branching random walk and branching Wiener process to derive rates of birth and death of ant paths...
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A proof of convergence for ant algorithms is developed. ant algorithms were modeled as branching random processes: the branching random walk and branching Wiener process to derive rates of birth and death of ant paths. Substitution is then carried out in birth-death processes which proves that a stable distribution is surely reached. This indicates that ant algorithms converge with probability one. This analogy models ant algorithms complexity parameters such as the number of cycles, the degrees of freedom of problem and the number of ants. (C) 2003 Elsevier Inc. All rights reserved.
We describe an ant algorithm for solving constraint problems (Solnon 2002, IEEE Transactions on Evolutionary Computation 6(4): 347-357). We devise a number of variants and carry out experiments. Our preliminary result...
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We describe an ant algorithm for solving constraint problems (Solnon 2002, IEEE Transactions on Evolutionary Computation 6(4): 347-357). We devise a number of variants and carry out experiments. Our preliminary results suggest that the best way to deposit pheromone and the best heuristics for state transitions may differ from current practice.
This paper presents an approach for the efficient parallel/distributed execution of ant algorithms, based on multi-agent systems. A very popular clustering problem, i.e., the spatially sorting of items belonging to a ...
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ISBN:
(纸本)9781479953134
This paper presents an approach for the efficient parallel/distributed execution of ant algorithms, based on multi-agent systems. A very popular clustering problem, i.e., the spatially sorting of items belonging to a number of predefined classes, is taken as a use case. The approach consists in partitioning the problem space to a number of parallel nodes. Data consistency and conflict issues, which may arise when multiple agents concurrently access shared data, are transparently handled using a purposely developed notion of logical time. The developer remains in charge only of defining the behavior of the agents modeling the ants, without coping with issues related to parallel/distributed programming and performance optimization. Experimental results show that the approach is scalable and can be adopted to speed up the ant algorithm execution when the problem size is large, as may be in the case of massive data analysis and clustering.
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