An improved ant colony algorithm based on artificial potential field and quantum evolution theory

Zhai, Longzhen; Feng, Shaohong

doi:10.3233/jifs-212220

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…At the same time, the application scope of fusion algorithm is not wide. Aiming at the blindness and relatively slow convergence speed in path planning search, Zhai et al [37] proposed an improved ACO algorithm based on artificial potential field and quantum evolution theory. Nagendranth et al [38] proposed a new algorithm based on type II fuzzy-based clustering with improved ACO algorithm based routing protocol for secure data transmission in manet.…”

Section: Improved Aco Algorithmmentioning

confidence: 99%

An optimized resource scheduling algorithm based on GA and ACO algorithm

Yin

Peng

et al. 2023

Preprint

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With the development of Internet of things (IoT) technology, fog computing has gradually come into people’s vision because of the characteristics of real-time and low delay. Providing computing and storage services at the edge of the network, fog computing can meet the needs of users in delay-sensitive applications as a highly virtualized platform. Resource scheduling plays an important role in fog computing, because it can meet the requirements of customers and improve the quality of service. However, if largescale service requests can not solve the resource scheduling problem effectively, it will reduce resource utilization and user satisfaction. Focus on the above problem, we establish a resource scheduling model named normalization processing, which can achieve the goal of the lowest total cost by finding the optimal pheromone. The optimal resource scheduling results can be obtained by changing the pheromone concentration of ants during the simulated foraging process. At the same time, we propose a new resource scheduling algorithm named new genetic ant colony optimization (NGACO) algorithm, which can update pheromone generation by roulette algorithm. The experimental results show that the resource scheduling performance of NGACO algorithm is 14.7%, 25%, 12.8% lower than that of ACO algorithm in makespan, economic cost, total cost respectively, and load balancing is 34.7% higher than ACO algorithm.

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Section: Improved Aco Algorithmmentioning

confidence: 99%

An optimized resource scheduling algorithm based on GA and ACO algorithm

Yin

Peng

et al. 2023

Preprint

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“…Quantum ant colony algorithm (QACA) is a new optimization algorithm that combines quantum computing with the ant colony algorithm (ACA) 22 . It has been successfully applied in path planning problems, travelling salesman problems, 0-1 knapsack problems, and other problems 23,24,25 . In this paper, a distribution network fault location method based on improved quantum ant colony algorithm is proposed.…”

Section: Introductionmentioning

confidence: 99%

Fault Location of Distribution Network Based on the Quantum Ant Colony Algorithm

Wang

et al. 2023

Preprint

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With the continuous expansion of the distribution network and the access of distributed generation, the problem of distribution networks with distributed generation (DG) fault location has become complex and difficult to solve. In order to locate faults accurately and quickly, in this paper, the quantum ant algorithm is applied to solve this problem for the first time. Firstly, we establish a reasonable mathematical model. To greatly avoid the adverse effects of information loss, we propose an information self-correction method. Secondly, a hierarchical positioning model is proposed to shorten the positioning time. Then considering the requirements of quickness and accuracy for fault location, the algorithm is improved. We update the quantum rotation gate mechanism and dynamically adjust the rotation angle. In addition, we add the mutation mechanism to expand the population diversity. Finally, the effectiveness and performance of the improved quantum ant colony algorithm are reflected by comparing with other algorithms. Simulation results demonstrate that the improved quantum ant colony algorithm has substantial global optimization ability, high fault location accuracy and fault tolerance in different cases, and the hierarchical positioning model can shorten the algorithm location time.

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“…The research idea is to evenly decompose the motion region into a large number of sub-regions [15], [16], and search for the sub-regions set that satisfies the objective function in the passable regions to achieve the optimal path planning. Scholars from various countries focus on improving the search-path time, searchpath integrity and search-path optimality, and their research results are mainly divided into heuristic algorithms,(such as A* algorithm [17], D* algorithm [18]) evolutionary algorithms(such as ant colony algorithm [19], particle swarm algorithm [20], genetic algorithm [21]) and potential field algorithms (vector field [22], artificial potential field [23]). Among them, the evolutionary algorithm has the characteristics of self-organization, self-adaptation and selflearning.…”

Section: Introductionmentioning

confidence: 99%

An Air Target Course Prediction Method Based on Sub-Regions Divide and Conquer With Double Variable Weight

Yang

Li³

2022

IEEE Access

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The number and density of airspace decompositions in course prediction are often fixed, resulting in large evaluation errors or low search efficiency of sub-regions, which makes it difficult to take into account the accuracy and real-time performance of course prediction. Aiming at this problem, a model based on the dynamic divide and conquer in sub-regions has been built. Firstly, a new airspace decomposition rule is proposed to adaptively decompose according to the complexity of the environment. Secondly, a state-distance bivariate weight function is constructed. The weights of course prediction index system are dynamically adjusted to improve the evaluation reliability of the selection possibilities of subregions. Then, to realize a comprehensive course prediction, the quality function of the course sequence is constructed from length, tortuosity, and selection possibility. Finally, a heuristic function based on possibility factor and distance factor and a pheromone update rule based on positive and negative feedback mechanism are constructed to improve the convergence speed and quality of course prediction. Compared with the airspace uniform decomposition strategy, course evaluation quality function, and ant colony algorithm experiment, the proposed method has a faster convergence speed and a higher sequence quality.

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An improved ant colony algorithm based on artificial potential field and quantum evolution theory

Cited by 7 publications

References 25 publications

An optimized resource scheduling algorithm based on GA and ACO algorithm

An optimized resource scheduling algorithm based on GA and ACO algorithm

Fault Location of Distribution Network Based on the Quantum Ant Colony Algorithm

An Air Target Course Prediction Method Based on Sub-Regions Divide and Conquer With Double Variable Weight

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