Analysis of Convergence Rate for Improved Generalized Ant Colony Optimization Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1728-1731
Author(s):  
Dai Yuan Zhang ◽  
Hua Zhao
Keyword(s):  
Convergence Rate ◽  
Markov Model ◽  
Theoretical Result ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Difficult Problem ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm

Although theoretical result of convergence for improved generalized ant colony optimization (IGACO) algorithm has been proved in recent years, the convergence speed is also an open and difficult problem. This article, based on the Markov model, tries to explore the analysis of convergence speed for IGACO algorithm. Some experiments have been studied to compare the convergence speed between ant colony optimization (ACO) algorithm and IGACO algorithm.

Research on Application of Ant Colony Optimization

2014 ◽  
Vol 548-549 ◽  
pp. 1213-1216
Author(s):  
Wang Rui ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Slow Convergence ◽  
Simulation Results ◽  
Research On Application

We research on application of ant colony optimization. In order to avoid the stagnation and slow convergence speed of ant colony algorithm, this paper propose the multiple ant colony optimization algorithm based on the equilibrium of distribution. The simulation results show that the optimal algorithm can have better balance in reducing stagnation and improving the convergence.

A Maturity-Based Adaptive Ant Colony Optimization Algorithm

2011 ◽  
Vol 50-51 ◽  
pp. 353-357
Author(s):  
Hai Ning Wang ◽  
Shou Qian Sun ◽  
Bo Liu
Keyword(s):  
Convergence Rate ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Solution Quality ◽  
Local Optima ◽  
The Relationship

In this paper, for the problems of low convergence rate and getting trapped in local optima easily, the average path similarity (APS) was proposed to present the optimization maturity by analyzing the relationship between parameters of local pheromone updating and global pheromone updating, as well as the optimizing capacity and convergence rate. Furthermore, the coefficients of pheromone updating adaptively were adjusted to improve the convergence rate and prevent the algorithm from getting stuck in local optima. The adaptive ACS has been applied to optimize several benchmark TSP instances. The solution quality and convergence rate of the algorithm were compared comprehensively with conventional ACS to verify the validity and the effectiveness.

Adaptive cooperative ant colony optimization algorithm based on reinforcement incentive and its application

2021 ◽  
pp. 1-10
Author(s):  
Weiwei Yu ◽  
Chengwang Xie ◽  
Chao Deng
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Information Exchange ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Neighborhood Search ◽  
Local Optimum ◽  
Ant Colony Optimization Algorithm

Ant colony algorithm has great advantages in solving some NP complete problems, but it also has some problems such as slow search speed, low convergence accuracy and easy to fall into local optimum. In order to balance the contradiction between the convergence accuracy and the convergence speed of ant colony algorithm, this paper first proposes an ant colony algorithm (RIACO) based on the reinforcement excitation theory of Burrus Frederic Skinner. In this algorithm, pheromone is stimulated and its volatilization coefficient is adjusted adaptively according to the iteration times, thus the speed of ant colony search is accelerated. Secondly, based on the characteristics of real ant colony classification and division of labor, this paper proposes an ant colony algorithm based on labor division and cooperation (LCACO). The algorithm divides the ant colony into two different types of ant colony for information exchange and improves the state transition probability formula, so that the two ant colonies can search the optimal path cooperatively, so as to improve the precision of ant colony search. Finally, combining the two improved ant colony algorithms, this paper proposes an adaptive cooperative ant colony optimization algorithm based on reinforcement incentive (SMCAACO). A multi constrained vehicle routing problem (MCVRP) is compared with the classical tabu search algorithm (TS), variable neighborhood search algorithm (VNS) and basic ant colony algorithm (ACO). The experimental results show that, in solving the mcvrp problem, the algorithm proposed in this paper not only has a good performance in the solution results, but also achieves a good balance between the convergence speed and the convergence accuracy.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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