Research on the Algorithm Optimization of Improved Ant Colony Algorithm- LSACA

Yunheng Liu 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.3

The ant colony algorithm is an algorithm which is used to find the optimal path. As a kind of bionic evolutionary algorithm, the ant colony algorithm is inspired by the real ant colony foraging mechanisms. Firstly, this paper introduces the basic model of the ant colony algorithm. Then, aiming at the shortcomings of the ant colony algorithm, we propose a new probability formula of the optimal path and the new formula of the pheromone update. In addition, we combine the traditional ant colony algorithm with the local search algorithm and propose the improved ant colony algorithm. It is the LSACA algorithm. In the experimental analysis, we set and analyze the parameters of the algorithm. Then, we compare with the traditional algorithm to prove the feasibility and the effectiveness of the algorithm.

Research on the Hybrid ant Colony Algorithm based on Genetic Algorithm

Xu Yan 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.3

Since the ant colony algorithm is proposed, it has achieved the remarkable achievements in many fields. With the development of the times, the traditional ant colony algorithm exposes its limitations for solving the questions. In this paper, we improve the ant colony algorithm. And we combine the ant colony algorithm with the genetic algorithm. Then, we propose the GAPSPAC algorithm. The algorithm combines the advantages of the genetic algorithm and the ant colony algorithm. And it overcomes the disadvantages to improve the efficiency of solving the questions. In the last experiment, we can see the algorithm has the better problem solving ability and the stability.

Research on Ant Colony Algorithm Optimization Neural Network Weights Blind Equalization Algorithm

Yanxiang Geng,Liyi Zhang,Yunshan Sun,Yao Zhang,Nan Yang,Jiawei Wu 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.2

The project of ant colony algorithm optimization neural network combining blind equalization algorithm is proposed. The better initial weights of neural networks are provided because of the randomness, ergodicity and positive feedback of the ant colony algorithm. And then, a combination of optimal weights are found through BP algorithm, which is fast local search speed. Thus blind equalization performance is improved. Computer simulation show that, the novel blind equalization algorithm speeds up the convergence rate, reduces the remaining steady-state error and bit error rate, which is compared with the Neural Network Blind Equalization Algorithm(NNBE) and Genetic Algorithm optimization Neural Network Blind Equalization Algorithm(GA-NNBE) .

Study on Thinking Evolution based ant Colony Algorithm in Typical Production Scheduling Application

Xianmin Wei,Peng Zhang 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.6

Aiming at solving the NP-hard workshop production scheduling problems, proposed one kind based on mind evolutionary algorithm. The algorithm in the traditional ant colony algorithm is established, and the combination of evolutionary thought and local optimization idea overcomes the basic ant colony algorithm is easy to fall into local optimal defects, the improved state transition rules, defining a pheromone range, improve the pheromone update strategy, and the increase of neighborhood search. Experimental results show that, for a typical production scheduling problems, based on mind evolutionary ant colony algorithm can obtain the optimal solution in theory, optimal solution, the solution and average three indicators are better than the basic ant colony algorithm, showed good performance.

The Combination of Extension of Ant Colony Algorithm and Other Intelligent Algorithms

Ma Li,Li Qianting,Ma Meiqiong,Meng Jun,Bai Jiyun 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.12

The extension of ant colony algorithm was proposed by Dorigo, the founder of ant colony algorithm, which is the latest ant colony algorithm for solving a continuous space optimization problem. Considering the blindness of man-made choice of initial solution and initial parameters of the algorithm, and according to the algorithm converging slowly and easily falling into local optimum, this paper has provided improvement strategy for this optimization. It has introduced quantum computing and genetic algorithm, chaos optimization to carry out combination and comparison, and it has carried out improvement on the weight internally solved by memory in the algorithm. The effectiveness of various combined algorithms was determined through the optimization of numerous multi-dimensional continuous functions.

Optimization Design Based On Self-Adapted Ant Colony and Genetic Mix Algorithm for Parameters of PID Controller

Wang Xiao-Yu 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.7

This paper presents a method of optimized PID parameter self-adapted ant colony algorithm with aberrance gene, based on ant colony algorithm. This method overcomes genetic algorithm’s defects of repeated iteration, slower solving efficiency, ordinary ant colony algorithm’s defects of slow convergence speed, easy to get stagnate, and low ability of full search. For a given system, the results of simulation experiments which compare to the result of Z-N optimization and evolution of genetic algorithm optimization and evolution of ant colony system optimization, it has more excellent performance in finding best solution and convergence, the PID parameters also have optimality, system possesses dynamic controlling and performance. The experiments show that this method has its practical value on controlling other objection and process.

An Improved Quantum Ant Colony Optimization Algorithm for Solving Complex Function Problems

Changai Chen,Yanwen Xu 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.11

In order to improve the slow convergence speed and avoid falling into the local optimum in ant colony optimization algorithm, an improved quantum ant colony optimization (IMAQACO) algorithm based on combing quantum evolutionary algorithm with ant colony optimization algorithm is proposed for solving complex function problems in this paper. In the IMAQACO algorithm, the quantum state vectors are used to represent the pheromone, the adaptively dynamical updating strategy is used to control pheromone evaporation factor, the quantum rotation gate is used to realize the ant movement and change the convergence tend of quantum probability amplitude, quantum non-gate is used to realize ant location variation, so the IMAQACO algorithm has better global search ability and population diversity than ACO algorithm. In order to test the optimization performance of IMAQACO algorithm, several benchmark functions are selected in here. The tested results indicate that the IMAQACO can effectively improve the convergence speed and avoid falling into the local optimum, and has a stronger global optimization ability and higher convergence speed in solving complex function problems.

Innovative Teaching Via Sustainable Vocational Education with an Improved Ant Colony Algorithm

Yan Xia 대한전자공학회 2023 IEIE Transactions on Smart Processing & Computing Vol.12 No.5

Although students’ test scores provide an important reference for teaching and learning, research scholars still need to objectively analyze the scores. Under the current situation where English performance of vocational education students does not achieve satisfactory results, this research uses a clustering algorithm to improve on the ant colony optimization algorithm. This ant colony clustering analysis algorithm is improved by incorporating two optimization strategies, and the test scores of vocational education students are introduced as the original data for cluster analysis. The optimal number of ant colonies is nine, when the three error values of the two ant colony algorithms are minimized. The convergence values of the three ant colony algorithms are smallest when there are 200 training cycles or when the training batch size is 1000, resulting in upgraded ant colony clustering algorithm convergence values of 0.498 and 1.523, respectively. The performance of the student evaluation model combined with the ant colony clustering optimization algorithm improved, followed by CF, FOA, and BP. KNN had the worst performance. Data mining on student performance can be done via research that can provide specialized advice on students' issues.

Ant Colony System: An Approach for the Design of a Single Database to Establish Any Node-to-node Connectivity for Robot Path Planning in a Robot Colony

보안공학연구지원센터(IJHIT) 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.3

In Ant Colony System, the agents or the ants travel in search of foods by following one another that is they show the swarming behavior along the searching path of the food source and return path towards the hive. In the artificial ant system or the robot system, the same operation of the robot can be observed by the suitable design of the travelling path. The path design and the travelling technique are already proposed in my two previous works. Those designs include different types of algorithms for different opeartions. Kruskel’s algorithm is applied to find the shortest path in between any two nodes, one is the source and another is the destination node. But the disadvantage of this design is the longer simulation time as three algorithms is required to travel from one node to another node and naturally several numbers of the databases are required to hold all the information about the entire robot colony. In this paper, a single database is proposed that can hold all the connectivity and which can be driven with a single algorithm. The proposed method is compared with the other previous techniques of finding the shortest path and the result set represented here showing the efficiency of the proposed method.

A Novel Hybrid Optimization Algorithm and its Application in Solving Complex Problem

Hao Jia 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.2

Ant colony optimization (ACO) algorithm is a new heuristic algorithm which has been demonstrated a successful technology and applied to solving complex optimization problems. But the ACO exists the low solving precision and premature convergence problem, particle swarm optimization (PSO) algorithm is introduced to improve performance of the ACO algorithm. A novel hybrid optimization (HPSACO) algorithm based on combining collaborative strategy, particle swarm optimization and ant colony optimization is proposed for the traveling salesman problems in this paper. The HPSACO algorithm makes use of the exploration capability of the PSO algorithm and stochastic capability of the ACO algorithm. The main idea of the HPSACO algorithm uses the rapidity of the PSO algorithm to obtain a series of initializing optimal solutions for dynamically adjusting the initial pheromone distribution of the ACO algorithm. Then the parallel search ability of the he ACO algorithm are used to obtain the optimal solution of solving problem. Finally, various scale TSP are selected to verify the effectiveness and efficiency of the proposed HPSACO algorithm. The simulation results show that the proposed HPSACO algorithm takes on the better search precision, the faster convergence speed and avoids the stagnation phenomena.