The imperialist competitive algorithm (ICA) is a new heuristic algorithm proposed for continuous optimization problems. The research about its application on solving the traveling salesman problem (TSP) is still very limited. Aiming to explore its ability on solving TSP, we present a discrete imperialist competitive algorithm in this paper. The proposed algorithm modifies the original rules of the assimilation and introduces the 2-opt algorithm into the revolution process. To examine its performance, we tested the proposed algorithm on 10 small-scale and 2 large-scale standard benchmark instances from the TSPLIB and compared the experimental results with that obtained by two other ICA-based algorithms and six other existing algorithms. The proposed algorithm shows excellent performance in the experiments and comparisons.

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Application of Imperialist Competitive Algorithm on Solving the Traveling Salesman Problem

A social network is a social structure, which is organized by the relationships or interactions between individuals or groups. Humans link the physical network with social network, and the services in the social world are based on data and analysis, which directly influence decision making in the physical network. In this paper, we focus on a routing optimization algorithm, which solves a well-known and popular problem. Ant colony algorithm is proposed to solve this problem effectively, but random selection strategy of the traditional algorithm causes evolution speed to be slow. Meanwhile, positive feedback and distributed computing model make the algorithm quickly converge. Therefore, how to improve convergence speed and search ability of algorithm is the focus of the current research. The paper proposes the improved scheme. Considering the difficulty about searching for next better city, new parameters are introduced to improve probability of selection, and delay convergence speed of algorithm. To avoid the shortest path being submerged, and improve sensitive speed of finding the shortest path, it updates pheromone regulation formula. The results show that the improved algorithm can effectively improve convergence speed and search ability for achieving higher accuracy and optimal results.

An Improved Routing Optimization Algorithm Based on Travelling Salesman Problem for Social Networks

Ant colony algorithm is a simulated evolutionary algorithm with the characteristics of positive feedback and distributed computation. It simulate the process of ants foraging to search the optimal solution. But the algorithm fall into local optimum easily and the convergence speed is very slow. After analyzing the disadvantages of ant colony algorithm, we put forward an improved hybrid ant colony algorithm. For each generation of ant colony perform crossover and mutation operations, and accept new individuals with a specified probability according to the Metropolis criterion of simulation annealing algorithm. Through series of simulation experiments' results, it can be found that the proposed algorithm is good at stability and optimization capacity.

An improved hybrid ant colony algorithm and its application in solving TSP

The collective intelligent behavior demonstrated by a swamp of simple natural agents often called Swarm Intelligence (SI) is already an emerging area of research. SI has been successfully adapted and applied to numerous problems in the field of combinatorial optimisation. Yet there is space for improvement in these heuristics and in the resulting algorithms as existing SI models still suffer from deficiencies in solution accuracy, speed of convergence and premature convergence to non-optimal solutions. Researchers attempt to hybridize such basic algorithms in order to overcome the weaknesses when the algorithms are applied individually. Among them, Particle Swarm Optimisation and Ant Colony Optimisation are often used by researchers due to their simplicity, effectiveness and efficiency in problem solving. This paper presents a socially optimised hybrid version of Particle Swarm Optimisation and Ant Colony Optimisation that adapts social behaviours existing in swarm population. Experimental results show that the objective of reducing the delay in convergence while maintaining an acceptable solution quality by incorporating social interactions in to Swarm Intelligence models is successful when solving Travelling Salesman Problems.

Social interaction optimised Swarm Intelligence technique for Travelling Salesman Problem

On a new polynomial algorithm for solving NPC problems

The Traveling Salesman Problem(TSP) is the oldest and most extensively studied combinatorial optimization problem.For the traveling salesman problem,Hybrid Ant colony and Particle swarm Algorithm(HAPA) is proposed.The HAPA divides the ant colony into several ant sub colonies,then optimizes parameters of the ant sub colonies as particles by the particle swarm optimization algorithm,and introduces the operation of swapping the pheromone in each ant sub colony.Results show that the HAPA has more advantages than the traditional algorithm and the similar algorithm in solving the traveling salesman problem.

Ding Jiadong

Papers

Hybrid ant colony and particle swarm algorithm for solving TSP