Using hyper populated ant colonies for solving the TSP

Vietnam Journal of Computer Science, Mar 2016

The paper discusses the application of hyper populated ant colonies to the well-known traveling salesman problem (TSP). The ant colony optimization (ACO) approach offers reasonably good quality solutions for the TSP, but it suffers from its inherent non-determinism and as a consequence the processing time is unpredictable. The paper tries to mitigate the problem by a substantial increase in the number of used ants. This approach is called ant hyper population and it could be obtained by increasing the number of ants in a single colony assigning more than one colony to solve the same task or both. In all cases the level of non-determinism decreases and thus the number iterations could be reduced. Parallel implementation of the ACO makes it possible to reduce drastically the processing time. The paper compares two ways of implementation of the parallelism using the sockets or the RMI—remote method invocation mechanisms. The paper concentrates on the classical static version of the TSP, but preliminary experiments indicate that such an approach could be even more useful for dynamic TPSs.

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Using hyper populated ant colonies for solving the TSP

Vietnam J Comput Sci Using hyper populated ant colonies for solving the TSP Andrzej Siemi n´ski 0 0 Faculty of Computer Science and Management, Wroclaw University of Technology , Wroclaw , Poland The paper discusses the application of hyper populated ant colonies to the well-known traveling salesman problem (TSP). The ant colony optimization (ACO) approach offers reasonably good quality solutions for the TSP, but it suffers from its inherent non-determinism and as a consequence the processing time is unpredictable. The paper tries to mitigate the problem by a substantial increase in the number of used ants. This approach is called ant hyper population and it could be obtained by increasing the number of ants in a single colony assigning more than one colony to solve the same task or both. In all cases the level of nondeterminism decreases and thus the number iterations could be reduced. Parallel implementation of the ACO makes it possible to reduce drastically the processing time. The paper compares two ways of implementation of the parallelism using the sockets or the RMI-remote method invocation mechanisms. The paper concentrates on the classical static version of the TSP, but preliminary experiments indicate that such an approach could be even more useful for dynamic TPSs. Ant colony optimization; Traveling salesmen problem; Parallelization strategies for ACO; Ant colony community (ACC) 1 Introduction The aim of the paper is to discuss the problem of optimizing the performance of the ant colony optimization (ACO) used for the traveling salesman problem (TSP). Metaheuristics such as the ACO solve a complex problem by iteratively improving candidate solutions. They do not guarantee the selection of an optimum or even a satisfactory near-optimal solution. In the case of the NP-hard or even NP-complete problems they are often the only available choice that we have. Usually the quality of solutions is measured by exclusively by the length of the selected route. The ACO works in a nondeterministic way. Usually a predefined number of iterations are executed and the best found solution is selected. The quality of solutions improves with time, but the process is not uniform. A high quality solution could be found after a few thousands of iterations, but it is not uncommon that we can have it after just a few dozens of them. Therefore, it is so hard to tell when to stop the operation of the ACO. There is a great incentive to prolong the execution time. The reported experiment results look more impressive. This is certainly possible in an university environment. It may not be the case for the real life applications. The time needed to execute a great number of iterations may simply not be available. Moreover, the longer we run the ACO, the solution updates are the less and less frequent. This demerit is even more acute for the dynamic TSP. In that case the ACO may not be able to catch up with the changing environment. The paper addresses the problem using a drastically increased number of ants. In what follows such ACOs are called hyper populated Ant Colonies. They come in two flavors. In the first one the ant number increases in just one colony and in the second we have a number of cooperating colonies—the so-called ant colony community (ACC). In both cases the results converge faster so the number of iterations could be limited. The main contribution of the paper is a detailed presentations of a model for the ACC and verification of its efficiency. The Socket mechanism was used to implement the model and its advantages over the competing RMI mechanism are discussed. The complexity of interactions between individual ants or even ant colonies make a theoretical analysis extremely hard even with a number of simplifying assumptions. Therefore, this paper demonstrates empirically the performance and convergence aspects of the proposed model. The paper is organized as follows. The second Section briefly introduces the TSP. The next one is devoted to the ACO—a metaheuristics commonly used to solve it. This section presents its general operational principles and the role played by its parameters. Attempts to optimize their values are also mentioned. It ends with the discussion of the stopping conditions. In the fourth Section we provide experiment results that justify the increase in the number of used ants. The prolonged execution time resulting from increasing of ant population could be mitigated by the parallel implementation of the ACO. The fifth Section contains the taxonomy of parallel ACOs and introduces the coarse-grained ACC proposed in the paper. The conducted experiments, their results with the criteria used to evaluate them are presented in the 6th Section. The paper concludes with the resume of research work done so far and the plans for future investigation. 2 Traveling salesman problem specification The TSP could be stated in a remarkably simple way: given a list of cities and the distances sepa (...truncated)


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Andrzej Siemiński. Using hyper populated ant colonies for solving the TSP, Vietnam Journal of Computer Science, 2016, pp. 103-117, Volume 3, Issue 2, DOI: 10.1007/s40595-016-0059-z