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This paper proposes an ant colony optimization algorithm of adaptive premium penalty. The proposed algorithm is based on the MAX-MIN Ant System and introduces a new adaptive discriminative premium penalty pheromone renewal strategy. The proposed strategy is based on the fact that a better path has much better sections and a worse path has much worse sections. By comparing the sections of the best...
This paper presents a method to improve the search rate of Max-Min Ant System for the traveling salesman problem. The proposed method gives deviations from the initial pheromone trails by using a set of local optimal solutions calculated in advance. Max-Min Ant System has demonstrated impressive performance, but the rate of search is relatively low. Considering the generic purpose of stochastic search...
MAX-MIN Ant System (MMAS) is an ant colony optimization (ACO) algorithm that was originally designed to start with a very explorative search phase and then to make a slow transition to an intensive exploitation of the best solutions found during the search. This design leads to a rather slow initial convergence of the algorithm, and, hence, to poor results if the algorithm does not run for sufficient...
The speed of the surface mount equipment is a key factor which influences the efficiency of surface mount process. This paper proposes a new optimization method based on the Max-Min Ant System, which is suitable for surface mount. In the method, the problem of finding the optimal route is converted to that of solving a traveling salesman problem, and a corresponding mathematical model is established...
A novel max-min ant system algorithm is proposed for the problem of setting pheromone trail value which is caused by uncertainty of the objective function value. The upper and lower bounds of pheromone are determined according to the range of objective function value by a random sampling. And the update quantity of pheromone is determined. As result of this process is not using the objective function...
Ant colony algorithm is an efficient intelligent algorithm to solve NP hard problem. This paper presents a parallel computing solution based on General Purpose GPU (GPGPU) to solve traveling salesman problem (TSP) with max-min ant system (MMAS). The experimental result shows it is more efficient than pure CPU computing.
This paper presents a modified multi-colony ant algorithm, based upon a pheromone arithmetic crossover and a repulsive operator. Iteration of this algorithm can avoid some stagnating states of basic ant colony optimization. An important mechanism of this algorithm is the reinitialization of such stagnating states (worst performing ant colonies), which is accomplished through application of the pheromone...
On the base of researches on Max-Min Ant System (MMAS), a new algorithm named multiple nestspsila cooperation ACO based Union-Intersection operations (UI-MNCACO) is proposed to resolve the narrow Traveling Salesman Problem (TSP). In UI-MNCACO, we find out the edges contained in the shortest Hamiltonian circuit by the cooperation of elitist ants, the cooperation is completed by the union and intersection...
This paper proposes a novel ant colony optimization named MMAS-MDS algorithm (max-min ant system extended by multidimensional scaling) for solving the traveling salesman problem more effectively in the congested transportation systems. Global heuristic information related to time-distance is put into the probabilistic selection rule of the ant tour construction. It provides global guides for promising...
In ant colony optimization methods, including ant system and max-min ant system, each ant stochastically generates its candidate solution, in a given iteration, based on the same pheromone tau and heuristic eta information as every other ant. In this paper, we propose a variation in which if an ant generates a particular candidate solution St-1 in iteration t - 1, then the solution components of S...
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