This paper presents an improved ant colony algorithm for the path planning of the omnidirectional mobile vehicle. The purpose of the improved ant colony algorithm is to design an appropriate route to connect the start...
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This paper presents an improved ant colony algorithm for the path planning of the omnidirectional mobile vehicle. The purpose of the improved ant colony algorithm is to design an appropriate route to connect the starting point and ending point of the environment with obstacles. ant colony algorithm, which is used to solve the path planning problem, is improved according to the characteristics of the omnidirectional mobile vehicle. And in the improved algorithm, the nonuniform distribution of the initial pheromone and the selection strategy with direction play a very positive role in the path search. The coverage and updating strategy of pheromone is introduced to avoid repeated search reducing the effect of the number of ants on the performance of the algorithm. In addition, the pheromone evaporation coefficient is segmented and adjusted, which can effectively balance the convergence speed and search ability. Finally, this paper provides a theoretical basis for the improved ant colony algorithm by strict mathematical derivation, and some numerical simulations are also given to illustrate the effectiveness of the theoretical results.
Airport gate assignment is core task for airport ground operations. Due to the fact that the departure and arrival time of flights may be influenced by many random factors, the airport gate assignment scheme may encou...
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Airport gate assignment is core task for airport ground operations. Due to the fact that the departure and arrival time of flights may be influenced by many random factors, the airport gate assignment scheme may encounter gate conflict and many other problems. This paper aims at finding a robust solution for airport gate assignment problem. A mixed integer model is proposed to formulate the problem, and colony algorithm is designed to solve this model. Simulation result shows that, in consideration of robustness, the ability of antidisturbance for airport gate assignment scheme has much improved.
ant colony optimization (ACO) algorithms have been successfully applied to identify classification rules in data mining. This paper proposes a new ant colony optimization algorithm, named hm antMiner(order), for the h...
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ant colony optimization (ACO) algorithms have been successfully applied to identify classification rules in data mining. This paper proposes a new ant colony optimization algorithm, named hm antMiner(order), for the hierarchical multilabel classification problem in protein function prediction. The proposed algorithm is characterized by an orderly roulette selection strategy that distinguishes the merits of the data attributes through attributes importance ranking in classification model construction. A new pheromone update strategy is introduced to prevent the algorithm from getting trapped in local optima and thus leading to more efficient identification of classification rules. The comparison studies to other closely related algorithms on 16 publicly available datasets reveal the efficiency of the proposed algorithm.
Recently, nature-inspired techniques have become valuable to many intelligent systems in different fields of technology and science. Among these techniques, ant Systems (AS) have become a valuable technique for intell...
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Recently, nature-inspired techniques have become valuable to many intelligent systems in different fields of technology and science. Among these techniques, ant Systems (AS) have become a valuable technique for intelligent systems in different fields. AS is a computational system inspired by the foraging behavior of ants and intended to solve practical optimization problems. In this paper, we introduce the antStar algorithm, which is swarm intelligence based. antStar enhances the optimization and performance of an AS by integrating the AS and A* algorithm. Applying the antStar algorithm to the single-source shortest-path problem has been done to ensure the efficiency of the proposed antStar algorithm. The experimental result of the proposed algorithm illustrated the robustness and accuracy of the antStar algorithm.
ant Colony Optimization (ACO) is a metaheuristic that has recently been applied to scheduling problems. We propose an ACO algorithm for the Single Machine Total Weighted Tardiness Problem and compare it to an existing...
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ant Colony Optimization (ACO) is a metaheuristic that has recently been applied to scheduling problems. We propose an ACO algorithm for the Single Machine Total Weighted Tardiness Problem and compare it to an existing ACO algorithm for the unweighted problem. The proposed algorithm has some novel properties that are of general interest for ACO optimization. A main novelty is that the ants are guided on their way through the decision space by global pheromone information instead of using only local pheromone information. It is also shown that the ACO optimization behaviour can be improved when priority scheduling heuristics are adapted so that they appropriately reflect absolute quality differences between the alternatives before they are used by the ants. Further improvements can be obtained by identifying situations where the ants can perform optimal decisions.
This paper deals with the optimization of hole-making operations in conditions where a hole may need several tools to get completed. The objective of interest in the considered problem is to minimize the summation of ...
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This paper deals with the optimization of hole-making operations in conditions where a hole may need several tools to get completed. The objective of interest in the considered problem is to minimize the summation of tool airtime and tool switch time. This objective is affected by the sequence through which each operation of each hole is done. The problem is formulated as a 0-1 non-linear mathematical model. An ant algorithm is developed to solve the proposed mathematical model. The paper includes an illustrative example which shows the application of the proposed algorithm to optimizing the sequence of hole-making operations in a typical industrial part. The performance of the proposed algorithm is tested through solving six benchmark problems. The computational experience conducted in this research indicates that the proposed method is both effective and efficient. (c) 2007 Elsevier Ltd. All rights reserved.
High-speed railway is one of the most important ways to solve the long-standing travel difficulty problem in China. However, due to the high acquisition and maintenance cost, it is impossible for decision-making depar...
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High-speed railway is one of the most important ways to solve the long-standing travel difficulty problem in China. However, due to the high acquisition and maintenance cost, it is impossible for decision-making departments to purchase enough EMUs to satisfy the explosive travel demand. Therefore, there is an urgent need to study how to utilize EMU more efficiently and reduce costs in the case of completing a given task in train diagram. In this paper, an EMU circulation scheduling model is built based on train diagram constraints, maintenance constraints, and so forth;in the model solving process, an improved ACA algorithm has been designed. A case study is conducted to verify the feasibility of the model. Moreover, contrast tests have been carried out to compare the efficiency between the improved ACA and the traditional approaches. The results reveal that improved ACA method can solve the model with less time and the quality of each representative index is much better, which means that efficiency of the improved ACA method is higher and better scheduling scheme can be obtained.
ant colony optimization (ACO) is an efficient heuristic algorithm for combinatorial optimization problems, such as clustering. Because the search strategy of ACO is similar to those of other well-known heuristics, the...
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ant colony optimization (ACO) is an efficient heuristic algorithm for combinatorial optimization problems, such as clustering. Because the search strategy of ACO is similar to those of other well-known heuristics, the probability of searching particular regions will be increased if better results are found and kept. Although this kind of search strategy may find a better approximate solution, it also has a high probability of losing the potential search directions. To prevent the ACO from losing too many potential search directions at the early iterations, a novel pheromone updating strategy is presented in this paper. In addition to the "original" pheromone table used to keep track of the promising information, a second pheromone table is added to the proposed algorithm to keep track of the unpromising information so as to increase the probability of searching directions worse than the current solutions. Several well-known clustering datasets are used to evaluate the performance of the proposed method in this paper. The experimental results show that the proposed method can provide better results than ACO and other clustering algorithms in terms of quality.
Optimal path planning is an important issue in vehicle routing problem. This paper proposes a new vehicle routing path planning method which adds path weight matrix and save matrix. The method uses a new transition pr...
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Optimal path planning is an important issue in vehicle routing problem. This paper proposes a new vehicle routing path planning method which adds path weight matrix and save matrix. The method uses a new transition probability function adding the angle factor function and visibility function, while setting penalty function in a new pheromone updating model to improve the accuracy of the route searching. Finally, after each cycle, we use 3-opt method to update the optimal solution to optimize the path length. The results of comparison also confirm that this method is better than the traditional ant colony algorithm for vehicle routing path planning method. The result of computer simulation confirms that the method can plan a more rational rescue path focused on the real traffic situation.
When a mine disaster occurs, to lessen disaster losses and improve survival chances of the trapped miners, good escape routes need to be found and used. Based on the improved ant algorithm, we proposed a new escape-ro...
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When a mine disaster occurs, to lessen disaster losses and improve survival chances of the trapped miners, good escape routes need to be found and used. Based on the improved ant algorithm, we proposed a new escape-route planning method of underground mines. At first, six factors which influence escape difficulty are evaluated and a weight calculation model is built to forma weighted graph of the underground tunnels. Then an improved ant algorithm is designed and used to find good escape routes. We proposed a tunnel network zoning method to improve the searching efficiency of the ant algorithm. We use max-min ant system method to optimize the meeting strategy of ants and improve the performance of the ant algorithm. In addition, when a small part of the mine tunnel network changes, the system may fix the optimal routes and avoid starting a new processing procedure. Experiments show that the proposed method can find good escape routes efficiently and can be used in the escape-route planning of large and medium underground coal mines.
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