An Investigation of Some Properties of an Ant Algorithm (original) (raw)
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Abstract In this paper we discuss the experimental evaluation of an improved configuration of our recent framework ACODA (Ant Colony Optimization on a Distributed Architecture) for solving the Traveling Salesman Problem (TSP). ACODA is a novel multi-agent system architecture for distributed Ant Colony Optimization in a decentralized environment. This new configuration improves the execution time by allowing each software agent of ACODA to manage a part of the TSP map rather than a single map node. Experimental results ...
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Ant Colony Extended (ACE) is a novel algorithm belonging to the general Ant Colony Optimisation (ACO) framework. Two specific features of ACE are: the division of tasks between two kinds of ants, namely patrollers and foragers, and the implementation of a regulation policy to control the number of each kind of ant during the searching process. In addition, ACE does not employ the construction graph usually employed by classical ACO algorithms. Instead, the search is performed using a state space exploration approach. This paper studies the performance of ACE in the context of the Travelling Salesman Problem (TSP), a classical combinatorial optimisation problem. The results are compared with the results of two well known ACO algorithms: ACS and MMAS. ACE shows better performance than ACS and MMAS in almost every TSP tested instance.
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Swarm Intelligence(SI) is the emergent collective intelligence of groups of simple agents. Economy is an example of SI. Simulating an economy using Ant Colony algorithms would allow prediction and control of fluctuations in the complex emergent behavior of the simulated system. Such a simulation is far beyond SI's capabilities, which is still in its infancy. This paper presents a distributed approach implementing Ant Colony Optimization(ACO). We present our agent based architecture of ACO and initial experimental results on the Travelling Salesman Problem. The innovation of our work consists of: i)representing network nodes as software agents, ii) representing software agents as software objects that are passed as messages between the nodes according to ACO rules.
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Abstract This paper presents a configurable distributed architecture for Ant Colony Optimization. We represent the problem environment as a distributed multi-agent system and we reduce ant management to messages that are asynchronously exchanged between agents. The experimental setup allows the deployment of the system on computer clusters, as well as on ordinary computer networks. We present experimental results that we obtained by utilizing our system to solve nontrivial instances of the Traveling Salesman Problem. ...
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Ant Colony Optimization algorithms (ACO) are meta-heuristic algorithms inspired from the cooperative behavior of real ants that could be used to achieve complex computations and have been proven to be very efficient to many different discrete optimization problems. One such problem is the Travelling Salesman Problem (TSP) which belongs to the class of NP-hard problems, which means that there is no exact algorithm to solve it in polynomial time. The importance of this problem appears in many application areas such as telecommunications, electronics, logistics, transportation, astronomy, industry and scheduling problem. Many algorithms had been proposed to solve TSP each with its own merits and demerits. In this paper, we propose a theoretical overview of TSP and ACO and also an implementation of how ACO can be used to solve TSP.
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An Ants System is an artificial system based on the behavior of real ant colonies, which is used to solve combinatorial problems. This is a distributed algorithm composed by a set of cooperating agents called ants which cooperate among them to find good solutions to ...