Research on intelligent parking path planning based on improved safe adaptive ant colony algorithm
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This paper proposes an improved safe adaptive ant colony algorithm for underground parking lot path planning problems. Firstly, by enhancing the heuristic function and introducing the distance to the target node and the adaptive enhancement coefficient, ants are inclined to move towards the target node to avoid falling into local optima. Secondly, the adoption of Gaussian distribution for initializing pheromones and a limited pheromone update strategy enhances the search efficiency of the algorithm. Finally, a comprehensive scoring strategy is designed to select the optimal path. Simulation results from three different scale environment maps demonstrate that this algorithm outperforms traditional A star algorithm in target orientation and surpasses traditional and improved ant colony algorithms in terms of turning times and time. With efficiency and practicality, it can be effectively applied to the problem of underground parking lot path planning, providing an effective solution to improve urban traffic efficiency and alleviate parking difficultiesKeywords:
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Improving the intelligent bionic ant colony model will require multidisciplinary research,and so its development will promote progress in related subjects.The ant colony model,a new intelligent bionic model which mimics the behavior of an ant colony,has progressed substantially in the last ten years.However,there has been no systematic study of this field in China.To encourage more research this paper gives a detailed introduction to several major ant colony models according to their underlying bionic principles and simulation methods.The latest developments in this field are also described.Then,typical applications of ant colony models are summarized,and new areas where they can be used are presented.Finally,comparisons are made between the ant colony algorithm,the particle swarm optimization algorithm,the immune algorithm,and the evolutionary algorithm.The similarities and differences between these algorithms are pointed out.This comprehensive introduction should promote research on ant colony algorithms in China.
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Multi-level ant system - a new approach through the new pheromone update for ant colony optimization
Ant Colony Optimization (ACO) is a meta-heuristic approach inspired by the study of the behavior of real ant colonies when finding the shortest path from their nest to food source. ACO has been used for solving approximately NP-hard problems and its elite effects has been proved by the experiments. Currently, two famous ACO algorithms are Ant Colony System (ACS) and Max-Min Ant System (MMAS) proposed by M.Dorigo and T.Stutzle. In this paper, we introduce the idea about Multi-level Ant System (MLAS) and its application as an improved version of Max-Min Ant System through a novel pheromone updating scheme. We applied the new algorithm to the well-known combinatorial optimization problems such as Traveling Salesman Problem, in which we compared the results of the new algorithm with that of MMAS algorithms. Experimental results based on the standard test data showed that MLAS algorithm is more effective than MMAS in term of both the average and the best solution.
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Many studies have focused in designing a set of good DNA sequences as it is one of the crucial tools in improving the reliability and efficiency of DNA computing. In this paper, an improved model of Ant Colony System is developed in optimizing DNA sequences design. The proposed model suggests that each artificial ant represents a possible solution of the DNA sequences design problem. This differs from the previous Ant Colony System approached where a number of artificial ants are required to represent a possible solution. In the implementation, four objective measures and two constraint measures are employed to obtain a good set of DNA sequences. The performance of the proposed model is evaluated by comparing the result with existing Ant Colony System model and other published sequence design method. The experimental result shows that the proposed Ant Colony System model outperformed the existing Ant Colony System model.
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Because the repair time is not taken into account, ant colony algorithm can not find the optimum path in a traffic network after earthquake. To solve the problem above, an improved ant colony algorithm is proposed, which brings the rush repair time into the constraints of path planning. When the rush repair is not completed, the corresponding path is not connected. In order to pass the path, it must wait until the path is connected. Simulate the traffic network diagram before and after the earthquake and carry out simulation experiments, the results show that compared with ant colony algorithm, the algorithm can effectively plan the path, has strong optimization ability and less time-consuming.
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An improved ant colony algorithm was put forward,which is more close to the real ant colony information system,in order to solve the VRP in grain logistics.The problem of delay due to the deficiency in cooperation is avoided,through the simulation contrast with the traditional ant colony algorithm in TSP, which approves the effectiveness of the improved ant colony algorithm.
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In this paper the actual research status of ant colony system,which has in recent years aroused wide interests,is summarily reviewed.Several types of modified ant colony system algorithmare are briefly introduced,such as ant colony system (ACS),max-min ant system (MMAS),ant colony algorithm with mutation features,ant colony algorithm integrated with genetic algorithm and so on.And as examples,based on several applications in the power system,this paper investigates some situations combined with applications.Also some hardware realizations of ant as single agent are roughly brought forth.
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An improved ant colony system was presented,which aimed at the drawback of ant colony system in network load space sharing.The algorithm used multiple ant colony to tag in same network and ectohormone of different ant colony restrain each other however which of same ant colony promote.This algorithm carry out the load space sharing by decreasing the number of ant colony's ectohormone in shortest path.The feasibility of the algorithm is proved by experiment and the results are given.
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This paper gives concept of covering path planning, its mathematics description as well as its designing method about full covering path planning. The paper also describes several kinds of performance functions of covering path motion. According to this, we design a full covering path planner based on sequence of elementary motion with synthetical performance function.
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This research conducts a comparative analysis of four Ant Colony Optimization (ACO) variants -- Ant System (AS), Rank-Based Ant System (ASRank), Max-Min Ant System (MMAS), and Ant Colony System (ACS) -- for solving the Traveling Salesman Problem (TSP). Our findings demonstrate that algorithm performance is significantly influenced by problem scale and instance type. ACS excels in smaller TSP instances due to its rapid convergence, while PACS proves more adaptable for medium-sized problems. MMAS consistently achieves competitive results across all scales, particularly for larger instances, due to its ability to avoid local optima. ASRank, however, struggles to match the performance of the other algorithms. This research provides insights into the strengths and weaknesses of these ACO variants, guiding the selection of the most suitable algorithm for specific TSP applications.
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Rank (graph theory)
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