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UAV Path Planning Based on an Improved Ant Colony Algorithm

Annual Meeting of the IEEE Industry Applications Society, 2021
In order to improve operational efficiency and survival probability, the optimal path of an UAV should be designed before the UAV performs a mission. The basic ant colony algorithm is easy to fall into the local optimum, and slow convergence speed.
Liu Huan, Zhang Ning, Li Qiang
semanticscholar   +1 more source

An Ant Colony Verification Algorithm

Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 2007
Buchi automata are widely used as a modeling formalism in formal verification. The emptiness check procedure is used to carry on the model checking of a model M. of a system, against an LTL formula Phi, that expresses the desidered properties the system should satisfy.
R.achid Rebiha, Giovanni L. Ciampaglia
openaire   +1 more source

Intelligent Learning Ant Colony Algorithm

Applied Mechanics and Materials, 2011
Ant colony algorithm is effective algorithm for NP-hard problems, but it also tends to mature early as other evolutionary algorithms. One improvement method of ant colony algorithm is studied in this paper. Intelligent learning ant colony algorithm with the pheromone difference and positive-negative learning mechanism is brought forward to solve TSP ...
Jian Hua Ma, Fa Zhong Tian
openaire   +1 more source

Applying ant colony algorithm to identify ecological security patterns in megacities

Environmental Modelling & Software, 2019
Ecological security patterns composed of ecological sources and corridors provide an effective approach to conserving natural ecosystems. Although the direction of ecological corridors has been identified in previous studies, the precise range remains ...
Jian Peng   +6 more
semanticscholar   +1 more source

Adaptive ant colony optimization algorithm

2014 International Conference on Mechatronics and Control (ICMC), 2014
An adaptive ant colony algorithm is proposed to overcome the premature convergence problem in the conventional ant colony algorithm. The adaptive ant colony is composed of three groups of ants: ordinary ants, abnormal ants and random ants. Each ordinary ant searches the path with the high concentration pheromone at the high probability, each abnormal ...
Gu Ping   +4 more
openaire   +1 more source

Application of Ant Colony Algorithm

Applied Mechanics and Materials, 2014
This paper mainly considers the application of the ant colony in our life. The principle of ant colony optimization, improves the performance of ant colony algorithm, and the global searching ability of the algorithm. We introduce a new adaptive factor in order to avoid falling into local optimal solution.
Rui Wang, Zai Tang Wang
openaire   +1 more source

Continuous Ant Colony Algorithm

Advanced Materials Research, 2011
Ant Colony Algorithm is a new bionics optimization algorithm from mimic the swarm intelligence of ant colony behavior. And it is a very good combination optimization method. To extend the ant colony algorithm, and to improve the searching performance, from the connections of continuous optimization and searching process of ant colony algorithm, one new
openaire   +1 more source

An Improved Ant Colony Algorithm

2008 International Conference on MultiMedia and Information Technology, 2008
Artificial ant colony algorithm is new in the evolution computing. The primary study shows it is a better algorithm with robust based population, but it has some shortcomings such as its slow computing speed, and it is easy to fall in local peak in large scale problem. To overcome these deficiencies, an improved ant colony algorithm is designed through
Xin Zhang, Yu-zhong Zhou, Ping Fang
openaire   +1 more source

MC-ANT: A Multi-Colony Ant Algorithm

2010
In this paper we propose an ant colony optimization variant where several independent colonies try to simultaneously solve the same problem. The approach includes a migration mechanism that ensures the exchange of information between colonies and a mutation operator that aims to adjust the parameter settings during the optimization.
Leonor Melo   +2 more
openaire   +1 more source

MLACO: A multi-label feature selection algorithm based on ant colony optimization

Knowledge-Based Systems, 2020
Nowadays, with emerge the multi-label datasets, the multi-label learning processes attracted interest and increasingly applied to different fields.
Mohsen Paniri   +2 more
semanticscholar   +1 more source

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