Ant Colony Approach for Multiple Pickup and Multiple Dropoff

The Multiple Travelling Salesman Problem, popularly known as MTSP is an NP-hard problem. MTSP is a well-known combinatorial optimization problem in which more than one salesmen visit all cities only once and return to the depot. In our problem, we apply the MTSP algorithm to multiple drivers picking and dropping packets at multiple locations and the drivers not returning to the starting location. There are no exact solutions for solving this combinatorial problem that can guarantee to find the optimal route within a reasonable time. A meta-heuristic algorithm, Ant Colony Optimization (ACO) is used as a base for our solution construction for different variations of the problem such as handling multiple pickups and multiple drop-offs using a single driver, multiple drivers, drivers starting at different times, and drivers available for different times. The goal is to maximize the number of goods delivered while minimizing distance (or time) within some threshold limits. The results are compared to existing algorithms like Brute-force approach and Nearest Neighbor algorithms. Our results show that the proposed ant colony algorithm achieves better results or at worst identical results to the Brute-force approach.Computer Scienc

Multi-colony ant systems for multi-hose routing

This article is available open access through the publisher’s website at the link below. Copyright @ 2012 International Journal of Computer Applications.Ant System (AS) is a general purpose heuristic algorithm inspired by the foraging behaviour of real ant colonies. AS and its improved versions have been successfully applied to difficult combinatorial optimization problems such as travelling salesman problem, quadratic assignment problem and job shop scheduling. In this paper, two versions of multi-colony ant systems that are extensions to the AS are proposed for the multi-hose routing. In both versions, each colony of ants searches for an optimum path between two end points (or commodities). While each colony searches for optimum paths, they try to maximum use of other colonies paths (sharing paths, or bundling) for easy handling of multiple paths. The first version uses a single pheromone matrix for all colonies and the second version uses different pheromone matrices for each colony and a modified random propositional rule to attract ants toward foreign pheromones. The tessellated format of the obstacles was used in the algorithm instead of the original shapes of the obstacles. As a result of using this format, the algorithm can handle freeform obstacles and speed up the algorithm when checking the collision detections. The experimental results show that there is no significant difference in the quality of the solutions produced by two versions and the first version takes less computation time. Further first version needs low computer memory and one parameter lesser than of the second version

Anti-pheromone as a tool for better exploration of search space

Many animals use chemical substances known as pheromones to induce behavioural changes in other members of the same species. The use of pheromones by ants in particular has lead to the development of a number of computational analogues of ant colony behaviour including Ant Colony Optimisation. Although many animals use a range of pheromones in their communication, ant algorithms have typically focused on the use of just one, a substance that encourages succeeding generations of (artificial) ants to follow the same path as previous generations. Ant algorithms for multi-objective optimisation and those employing multiple colonies have made use of more than one pheromone, but the interactions between these different pheromones are largely simple extensions of single criterion, single colony ant algorithms. This paper investigates an alternative form of interaction between normal pheromone and anti-pheromone. Three variations of Ant Colony System that apply the anti-pheromone concept in different ways are described and tested against benchmark travelling salesman problems. The results indicate that the use of anti-pheromone can lead to improved performance. However, if anti-pheromone is allowed too great an influence on ants' decisions, poorer performance may result

Parallel ACO with a Ring Neighborhood for Dynamic TSP

The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. In the dynamic traveling salesman problem the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their Neighborhoods. The algorithm is tested with success on several large data sets.Comment: 8 pages, 1 figure; accepted J. Information Technology Researc

A parallel implementation of ant colony optimization

Ant Colony Optimization is a relatively new class of meta-heuristic search techniques for optimization problems. As it is a population-based technique that examines numerous solution options at each step of the algorithm, there are a variety of parallelization opportunities. In this paper, several parallel decomposition strategies are examined. These techniques are applied to a specific problem, namely the travelling salesman problem, with encouraging speedup and efficiency results.Full Tex

A memetic ant colony optimization algorithm for the dynamic travelling salesman problem

Copyright @ Springer-Verlag 2010.Ant colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02