A Hybrid Discrete Particle Swarm Optimization with

2006

This paper presents a competitive Particle Swarm Optimization algorithm for the Traveling Salesman Problem, where the velocity operator is based upon local search and path-relinking procedures. The paper proposes two versions of the algorithm, each of them utilizing a distinct local search method. The proposed heuristics are compared with other Particle Swarm Optimization algorithms presented previously for the same problem. The results are also compared with three effective algorithms for the TSP. A computational experiment with benchmark instances is reported. The results show that the method proposed in this paper finds high quality solutions and is comparable with the effective approaches presented for the TSP.

In this paper we consider two widely used Swarm Intelligence (SI) inspired heuristic approaches Ant Colony Optimization and Improved Particle Swarm Optimization to solve classical optimization problem called Travelling Salesman Problem (TSP) that cannot be solved conventionally because it is NP hard problem. If one tries to solve TSP using conventional approach it will take years to find optimal solution. Therefore, Heuristic algorithm is the feasible solution to such problem. Interest of researchers has been attracted by Ant Colony Optimization (ACO) and Improved Particle Swarm Optimization (PSO) algorithms because of their simple, effective and efficient nature in solving real world optimization problems. The comparative analyses based on Performance have been done by using ACO and Improved PSO respectively in solving TSP in this paper. The comparative results are shown and it is devised that Improved PSO is better approach to solve the traveling salesman problem.

Adaptive and Natural Computing Algorithms, 2005

The classical travelling salesman problem (TSP) is to determine a tour in a weighted graph (that is, a cycle that visits every vertex exactly once) such that the sum of the weights of the edges in this tour is minimal. Hybrid methods, based on nature inspired heuristics, have shown their ability to provide high quality solutions for the TSP. The success of a hybrid algorithm is due to its tradeoff between the exploration and exploitation abilities in search space. This work presents a new hybrid model, based on Particle Swarm Optimization and Fast Local Search, with concepts of Genetic Algorithms, for the blind TSP A detailed description of the model is provided, emphasizing its hybrid features. The control parameters were carefully adjusted and the implemented system was tested with instances from 76 to 2103 cities. For instances up to 439 cities, the best results were less than 1% in excess ofthe known optima. In the average, for all instances, results are 2.538% in excess. Simularion results indicated that the proposed hybrid model pe@orms robustly. These results encourage further research and improvement of the hybrid model to tackle with hard combinatorial problems.

2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), 2014

A multi-colony ant colony optimization (ACO) algorithm consists of several colonies of ants. Each colony uses a separate pheromone table in an attempt to maximize the search area explored. Over the years, multi-colony ACO algorithms have been successfully applied on different optimization problems with stationary environments. In this paper, we investigate their performance in dynamic environments. Two types of algorithms are proposed: homogeneous and heterogeneous approaches, where colonies share the same properties and colonies have their own (different) properties, respectively. Experimental results on the dynamic travelling salesman problem show that multi-colony ACO algorithms have promising performance in dynamic environments when compared with single colony ACO algorithms.

International Journal of Computer Applications, 2012

Dynamic travelling salesman problem (DTSP) is one of the optimization issues which it is not solvable with classical methods. To solve this problem, various solutions in the literature can be seen that each has advantages and disadvantages. Genetic Algorithm (GA) and Ant Colony Optimization (ACO) have been good to solve the DTSP. In this paper, we highlight a new algorithm by combining genetic and ACO which gives us a better solution for DTSP. In hybrid algorithm, suitability of algorithm and travelled distance for DTSP has been considered. Obtained results suggest that Hybrid algorithm does not establish easily in the local optimum and possesses a good speed in convergence for comprehensive answer.

International Journal of Intelligent Systems and Applications in Engineering, 2019

Nowadays, the systems that are inspired by biological structures have gained importance and attracted the attention of researchers. The Multiple Travelling Salesman Problem (MTSP) is an extended version of the TSP. The aim in the MTSP is to find the tours for m salesmen, who all start and end at the depot, such that each intermediate node is visited exactly once and the total cost of visiting nodes is minimized. The Particle Swarm Optimization (PSO) algorithm which is a meta-heuristic algorithm based on the social behaviour of birds. In this article, 2 algorithms based on PSO, called APSO and HAPSO, were proposed to solve the MTSP. The APSO algorithm is based on the PSO and 2-opt algorithms, the path-relink and swap operators. While the HAPSO algorithm is based on the GRASP, PSO and 2-opt algorithms, the path-relink and swap operators. In the experiments, 5 TSP instances are used and the algorithms are compared with the GA and ACO algorithms. According to the results, the HAPSO algorithm has the better performance than the other algorithms on the most instances. Moreover the HAPSO algorithm produces more stable results than the APSO algorithm and the performance of the HAPSO algorithm is better in all the MTSP instances. Therefore, the HAPSO algorithm is more robust than the APSO algorithm.

This paper presents a Discre Optimization (DPSO) technique to so Travelling Salesman Problem (TSP) represented as a graph where nodes re edges represent paths between them. A p graph addresses a more realistic problem connected graph since in real life pat between certain cities. A novel approach t obtain results in this situation. Convergen optimal solution for such a Graph based T Keywords -Travelling salesman swarm optimization.

Journal of Intelligent Systems

This paper proposes a Multi-Agent based Particle Swarm Optimization (PSO) Framework for the Traveling salesman problem (MAPSOFT). The framework is a deployment of the recently proposed intelligent multi-agent based PSO model by the authors. MAPSOFT is made up of groups of agents that interact with one another in a coordinated search effort within their environment and the solution space. A discrete version of the original multi-agent model is presented and applied to the Travelling Salesman Problem. Based on the simulation results obtained, it was observed that agents retrospectively decide on their next moves based on consistent better fitness values obtained from present and prospective neighborhoods, and by reflecting back to previous behaviors and sticking to historically better results. These overall attributes help enhance the conventional PSO by providing more intelligence and autonomy within the swarm and thus contributed to the emergence of good results for the studied prob...

Dynamic travelling salesman problem (DTSP) is one of the optimization issues which it is not solvable with classical methods. To solve this problem, various solutions in the literature can be seen that each has advantages and disadvantages. Genetic Algorithm (GA) and Ant Colony Optimization (ACO) have been good to solve the DTSP. In this paper, we highlight a new algorithm by combining genetic and ACO which gives us a better solution for DTSP. In hybrid algorithm, suitability of algorithm and travelled distance for DTSP has been considered. Obtained results suggest that Hybrid algorithm does not establish easily in the local optimum and possesses a good speed in convergence for comprehensive answer.

International Conference on Artificial Intelligence, 2006

This paper describes a new metaheuristic technique to solve the travelling salesman problem based on particle swarm using guided local search. The main contribution of the paper is that is develops a particle swarm-based strategy to solve combinatorial optimization problems in general, and the TSP in particular. It first establishes the steps to be followed in the new method and then goes on to test the results obtained on a range of symmetrical problems set by different authors in the field. The method's results are then compared with results from a number of well known non-guided local search techniques, hoighlighting the fact that it is an enhancement on earlier techniques. Finally, it studies the contribution of particle swarm to the local search process search, where there is also an improvement over results from a simple local search. Experimental results suggest that the new method provided good results for symmetric TSP problems.

International Journal of Managing Public Sector Information and Communication Technologies, 2012

Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we're going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015

Ant colony optimization (ACO) algorithms have proved to be able to adapt for solving dynamic optimization problems (DOPs). The integration of local search algorithms has also proved to significantly improve the output of ACO algorithms. However, almost all previous works consider stationary environments. In this paper, the MAX-MIN Ant System, one of the best ACO variations, is integrated with the unstringing and stringing (US) local search operator for the dynamic travelling salesman problem (DTSP). The best solution constructed by ACO is passed to the US operator for local search improvements. The proposed memetic algorithm aims to combine the adaptation capabilities of ACO for DOPs and the superior performance of the US operator on the static travelling salesman problem in order to tackle the DTSP. The experiments show that the MAX-MIN Ant System is able to provide good initial solutions to US and the proposed algorithm outperforms other peer ACObased memetic algorithms on different DTSPs.

Applied Sciences

This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Optimization (ACO) technique for TSP due to its versatile and easily adaptable nature. However, additional potential improvement in the arrangement way decrease is yet possible in this approach. In this paper BWAS based incorporated arrangement as a high level type of ACO to upgrade the exhibition of the TSP arrangement is proposed. In addition, a novel approach, based on hybrid Particle Swarm Optimization (PSO) and ACO (BWAS) has also been introduced in this work. The presentation measurements of arrangement quality and assembly time have been utilized in this work and proposed algorithm is tried against various standard test sets to examine the upgrade in search capacity. The outcomes for TSP arrangement show that initial trail setup for the best particle can result in shortening the accumulated process of ...

Studies in Computational Intelligence, 2013

Ant colony optimization (ACO) algorithms have proved to be powerful methods to address dynamic optimization problems (DOPs). However, once the population converges to a solution and a dynamic change occurs, it is difficult for the population to adapt to the new environment since high levels of pheromone will be generated to a single trail and force the ants to follow it even after a dynamic change. A good solution is to maintain the diversity via transferring knowledge from previous environments to the pheromone trails using immigrants. In this chapter, we investigate ACO algorithms with different immigrants schemes for two types of dynamic travelling salesman problems (DTSPs) with traffic factor, i.e., under random and cyclic dynamic changes. The experimental results based on different DTSP test cases show that the investigated algorithms outperform other peer ACO algorithms and that different immigrants schemes are beneficial on different environmental cases S. Yang and X. Yao (Eds.): Evolutionary Computation for DOPs, SCI 490, pp. 317-341.

IEEE transactions on cybernetics, 2016

For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address DTSPs. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric DTSPs. The experimental results show the efficiency of the proposed memetic algorithm for addressing DTSPs in comparison with other state-of-the-art algorithms.