HABCO: A Robust Agent on Hybrid Ant-Bee Colony Optimization

The purpose of this research is to generate a robust agent by combining bee colony optimization (BCO) and ELU-Ants for solving traveling salesman problem (TSP), called HABCO. The robust agents, called ant-bees, firstly are grouped into three types scout, follower, recruiter at each stages. Then, the bad agents are high probably discarded, while the good agents are high probably duplicated in earlier steps. This first two steps mimic BCO algorithm. However, constructing tours such as choosing nodes, and updating pheromone are built by ELU-Ants method.To evaluate the performance of the proposed algorithm, HABCO is performed on several benchmark datasets and compared to ACS and BCO. The experimental results show that HABCO achieves the better solution, either with or without 2opt

Using 2-Opt based evolution strategy for travelling salesman problem

Harmony search algorithm that matches the (µ+ 1) evolution strategy, is a heuristic method simulated by the process of music improvisation. In this paper, a harmony search algorithm is directly used for the travelling salesman problem. Instead of conventional selection operators such as roulette wheel, the transformation of real number values of harmony search algorithm to order index of vertex representation and improvement of solutions are obtained by using the 2-Opt local search algorithm. Then, the obtained algorithm is tested on two different parameter groups of TSPLIB. The proposed method is compared with classical 2-Opt which randomly started at each step and best known solutions of test instances from TSPLIB. It is seen that the proposed algorithm offers valuable solutions

Parallelization of Ant System for GPU under the PRAM Model

We study the parallelized ant system algorithm solving the traveling salesman problem on n cities. First, following the series of recent results for the graphics processing unit, we show that they translate to the PRAM (parallel random access machine) model. In addition, we develop a novel pheromone matrix update method under the PRAM CREW (concurrent-read exclusive-write) model and translate it to the graphics processing unit without atomic instructions. As a consequence, we give new asymptotic bounds for the parallel ant system, resulting in step complexities O(n łg łg n) on CRCW (concurrent-read concurrent-write) and O(n łg n) on CREW variants of PRAM using n2 processors in both cases. Finally, we present an experimental comparison with the currently known pheromone matrix update methods on the graphics processing unit and obtain encouraging results

Parallelized neural network system for solving Euclidean traveling salesman problem

We investigate a parallelized divide-and-conquer approach based on a self-organizing map (SOM) in order to solve the Euclidean Traveling Salesman Problem (TSP). Our approach consists of dividing cities into municipalities, evolving the most appropriate solution from each municipality so as to find the best overall solution and, finally, joining neighborhood municipalities by using a blend operator to identify the final solution. We evaluate the performance of parallelized approach over standard TSP test problems (TSPLIB) to show that our approach gives a better answer in terms of quality and time rather than the sequential evolutionary SOM

Advanced Harmony Search with Ant Colony Optimization for Solving the Traveling Salesman Problem

We propose a novel heuristic algorithm based on the methods of advanced Harmony Search and Ant Colony Optimization (AHS-ACO) to effectively solve the Traveling Salesman Problem (TSP). The TSP, in general, is well known as an NP-complete problem, whose computational complexity increases exponentially by increasing the number of cities. In our algorithm, Ant Colony Optimization (ACO) is used to search the local optimum in the solution space, followed by the use of the Harmony Search to escape the local optimum determined by the ACO and to move towards a global optimum. Experiments were performed to validate the efficiency of our algorithm through a comparison with other algorithms and the optimum solutions presented in the TSPLIB. The results indicate that our algorithm is capable of generating the optimum solution for most instances in the TSPLIB; moreover, our algorithm found better solutions in two cases (kroB100 and pr144) when compared with the optimum solution presented in the TSPLIB