Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm
86
Citation
26
Reference
10
Related Paper
Citation Trend
Abstract:
As a tool to monitor marine environments and to perform dangerous tasks instead of manned vessels, unmanned surface vehicles (USVs) have extensive applications. Because most path planning algorithms have difficulty meeting the mission requirements of USVs, the purpose of this study was to plan a global path with multiple objectives, such as path length, energy consumption, path smoothness, and path safety, for USV in marine environments. A global path planning algorithm based on an improved quantum ant colony algorithm (IQACA) is proposed. The improved quantum ant colony algorithm is an algorithm that benefits from the high efficiency of quantum computing and the optimization ability of the ant colony algorithm. The proposed algorithm can plan a path considering multiple objectives simultaneously. The simulation results show that the proposed algorithm’s obtained minimum was 2.1–6.5% lower than those of the quantum ant colony algorithm (QACA) and ant colony algorithm (ACA), and the number of iterations required to converge to the minimum was 11.2–24.5% lower than those of the QACA and ACA. In addition, the optimized path for the USV was obtained effectively and efficiently.Keywords:
Smoothness
Ant colony
Path length
For the route choice problem with multi-attributes in traffic networks,this paper,based on Ant Colony Algorithm(ACA),obtains the model of getting synthesis optimal path for specified origin and destination.At first,the ant colony is divided into some subant colonies,and different sub-ant colonise have different attributes.Then,each sub-ant colony makes a route choice according to its attribute in iteration.When all sub-ant colonies have finished iteration,we update the pheromone value.Each sub-ant colony not only searches optimal solution with its specified attribute,but also they influences each other.Therefore,the solution would be better for each attribute.In the end,a simulated test is executed.
Ant colony
Cite
Citations (0)
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.
Ant colony
Swarm intelligence
Parallel metaheuristic
Cite
Citations (0)
In this paper, the continuous Ant Colony Optimization Algorithm (ACOR) is used to investigate the optimum operation of complex multi-reservoir systems. The results are compared with those of the well-known Genetic Algorithm (GA). For this purpose, GA and ACOR are used to solve the long-term operation of a three-reservoir system in Karkheh Basin, southwestern Iran. The solution must determine monthly releases from the three reservoirs and their optimum allocations among the four agricultural demand areas. Meanwhile, a minimum discharge must be maintained within the river reaches for environmental concerns. Review of past research shows that only a few applications of Ant Colony have been generally made in water resources system problems; however, up to the time of initiating this paper, we found no other application of the ACOR in this area. Therefore, unlike GA, application of Ant-Colony-based algorithms in water resources systems has not been thoroughly evaluated and deserves serious study. In this paper, the ACOR is stuided as the most recent Ant-Colony-based algorithm and its application in a multi-reservoir system is evaluated. The results indicate that with when the number of decision variables increases, a longer computational time is required and the optimum solutions found are inferior. Therefore, the ACOR would be unable to solve complex water resources problems unless some modifications are considered. To overcome a part of these drawbacks, a number of techniques are introduced in this paper that considerably improve the quality of the method by decreasing the required computation time and by enhancing optimum solutions found.
Ant colony
Cite
Citations (5)
Ant colony
Implementation
Cite
Citations (3)
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.
Ant colony
Sequence (biology)
Cite
Citations (14)
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.
Ant colony
Bacterial colony
Cite
Citations (0)
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.
Ant colony
Formicoidea
Realization (probability)
Parallel metaheuristic
Cite
Citations (3)
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.
Ant colony
Formicoidea
Cite
Citations (2)
This paper describes GRAF-ANT (Graphical Framework for Ant Colony Optimization), an object-oriented C# framework for developing ant colony systems that we have developed. While developing this framework, abstractions that are necessary for ant colony optimization algorithms were analyzed, as well as the features that their implementing classes should have. During creation of these classes, several problems were solved: implementation of individual ants and ant colonies, connection between visualization and problem spaces, creation of a multithread application in which multiple ant colonies can communicate, creation of a problem independent graphical user interface (GUI), establishing an opportunity for hybridization of ACO (Ant colony optimization). Effects of this hybridization to different variations of ant colony systems is analyzed. The use of the GRAF-ANT and its suitability is illustrated by few instances of the Traveling Salesman Problem (TSP). We also present a concept of escaping ACO stagnation in local optima, named suspicious path destruction, that is also a part of GRAF-ANT.
Ant colony
Extremal optimization
Cite
Citations (15)
For improving global convergence ability and training speed,an ant colony process neural network model was proposed.Making use of distributed computing and strong robustness of ant colony algorithm,ant colony algorithm was applied in feedforward process neural network training.Topology structure of ant colony process neural network was given,and discussed on training mechanism of ant colony process neural network,analyzed the calculation features.And ant colony process neural network had been used in the annual GDP forecast of Heilongjiang province,verified the effectiveness of ant colony process neural network.
Ant colony
Robustness
Feedforward neural network
Cite
Citations (0)