scholarly journals Biobjective Optimization of Vibration Performance of Steel-Spring Floating Slab Tracks by Four-Pole Parameter Method Coupled with Ant Colony Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Hao Jin ◽  
Weining Liu ◽  
Shunhua Zhou ◽  
Christoph Adam
Keyword(s):  
Ant Colony Optimization ◽  
Damping Ratio ◽  
Optimal Solution ◽  
Ant Colony ◽  
Parameter Method ◽  
Pareto Optimal ◽  
Spring Stiffness ◽  
Steel Spring ◽  
Pole Parameter ◽  
Biobjective Optimization

Steel-spring floating slab tracks are one of the most effective methods to reduce vibrations from underground railways, which has drawn more and more attention in scientific communities. In this paper, the steel-spring floating slab track located inTrack Vibration Abatement and Control Laboratorywas modeled with four-pole parameter method. The influences of the fastener damping ratio, the fastener stiffness, the steel-spring damping ratio, and the steel-spring stiffness were researched for the rail displacement and the foundation acceleration. Results show that the rail displacement and the foundation acceleration will decrease with the increase of the fastener stiffness or the steel-spring damping ratio. However, the rail displacement and the foundation acceleration have the opposite variation tendency for the fastener damping ratio and the steel-spring stiffness. In order to optimize the rail displacement and the foundation acceleration affected by the fastener damping ratio and the steel-spring stiffness at the same time, a multiobjective ant colony optimization (ACO) was employed. Eventually, Pareto optimal frontier of the rail displacement and the foundation acceleration was derived. Furthermore, the desirable values of the fastener damping ratio and the steel-spring stiffness can be obtained according to the corresponding Pareto optimal solution set.

An Improved and Realized Volatility Strategy of the Ant Colony Optimization Algorithm

2013 ◽  
Vol 389 ◽  
pp. 849-853
Author(s):  
Fang Song Cui ◽  
Wei Feng ◽  
Da Zhi Pan ◽  
Guo Zhong Cheng ◽  
Shuang Yang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Ant System ◽  
Ant Colony Optimization Algorithm ◽  
Volatility Model ◽  
Local Optimal Solution ◽  
The Impact ◽  
Improved Algorithm

In order to overcome the shortcomings of precocity and stagnation in ant colony optimization algorithm, an improved algorithm is presented. Considering the impact that the distance between cities on volatility coefficient, this study presents an model of adjusting volatility coefficient called Volatility Model based on ant colony optimization (ACO) and Max-Min ant system. There are simulation experiments about TSP cases in TSPLIB, the results show that the improved algorithm effectively overcomes the shortcoming of easily getting an local optimal solution, and the average solutions are superior to ACO and Max-Min ant system.

Fleet optimization considering overcapacity and load sharing restrictions using genetic algorithms and ant colony optimization

2020 ◽  
Vol 34 (1) ◽  
pp. 104-113
Author(s):  
Fredy Kristjanpoller ◽  
Kevin Michell ◽  
Werner Kristjanpoller ◽  
Adolfo Crespo
Keyword(s):  
Ant Colony Optimization ◽  
Real World ◽  
Life Cycle Cost ◽  
Optimal Solution ◽  
Load Sharing ◽  
Ant Colony ◽  
Added Value ◽  
Fleet Size ◽  
Combined Use ◽  
Computing Performance

AbstractThis paper presents a fleet model explained through a complex configuration of load sharing that considers overcapacity and is based on a life cycle cost (LCC) approach for cost-related decision-making. By analyzing the variables needed to optimize the fleet size, which must be evaluated in combination with the event space method (ESM), the solution to this problem would normally require high computing performance and long computing times. Considering this, the combined use of an integer genetic algorithm (GA) and the ant colony optimization (ACO) method was proposed in order to determine the optimal solution. In order to analyze and highlight the added value of this proposal, several empirical simulations were performed. The results showed the potential strengths of the proposal related to its flexibility and capacity in solving large problems with a near optimal solution for large fleet size and potential real-world applications. Even larger problems can be solved this way than by using the complete enumeration approach and a non-family fleet approach. Thus, this allows for a more real solution to fleet design that also considers overcapacity, availability, and an LCC approach. The simulations showed that the model can be solved in much less time compared with the base model and allows for the resolution of a fleet of at least 64 trucks using GA and 130 using ACO, respectively. Thus, the proposed framework can solve real-world problems, such as the fleet design of mining companies, by offering a more realistic approach.

Application of Ant Colony Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 1217-1220
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Pheromone Concentration ◽  
Local Optimal Solution ◽  
Searching Ability ◽  
Excessive Accumulation

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. With the increase the number of interations, this factor will benefit the ant search the edge with lower pheromone concentration and avoid the excessive accumulation of pheromone.

An Improved Ant Colony Optimization Supervised by PSO

2010 ◽  
Vol 108-111 ◽  
pp. 1354-1359
Author(s):  
Zhi Gang Zhou
Keyword(s):  
Ant Colony Optimization ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Search Space ◽  
Ant Colony ◽  
Ant Colony System ◽  
Control Parameters ◽  
Evaporation Coefficient ◽  
The Core ◽  
Stochastic Selection

Combined with the idea of the particle swarm optimization (PSO) algorithm, the ant colony optimization (ACO) algorithm is presented to solve the well known traveling salesman problem (TSP). The core of this algorithm is using PSO to optimize the control parameters of ACO which consist of heuristic factor, pheromone evaporation coefficient and the threshold of stochastic selection, and applying ant colony system to routing. The new algorithm effectively overcomes the influence of control parameters of ACO and decreases the numbers of useless experiments, aiming to find the balance between exploiting the optimal solution and enlarging the search space.

scholarly journals Software Test Suite Minimization using Ant Colony Optimization

2019 ◽  
Vol 8 (9S) ◽  
pp. 948-952
Keyword(s):  
Ant Colony Optimization ◽  
Software Maintenance ◽  
Software Industry ◽  
Optimal Solution ◽  
Maintenance Phase ◽  
Regression Testing ◽  
Ant Colony ◽  
Test Suite ◽  
High Quality ◽  
Area Of Interest

In the fast pacing technological era, the key to a successful software industry is quick delivery of high quality software to the clients. This high quality is achieved by performing software testing on the product. The high quality product ensures stakeholder’s satisfaction which in turn spreads good word about the software industry making it a success. In this paper, we will focus on the problems faced during regression testing and how the same can be handled. Regression testing is a critical activity done during the software maintenance phase of the software development cycle. However, it has countless underlying issues like effective test case generation and prioritization, etc which need to be dealt with. These issues demand effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of Ant Colony Optimization (ACO) for test suite minimization has been an area of interest for many researchers. This paper presents an implementation of ACO for test suite minimization, showcasing how arbitrary nature of ACO helps choose an optimal solution to the problem.

scholarly journals Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 311 ◽  
Author(s):  
Hai Xue ◽  
Kyung Kim ◽  
Hee Youn
Keyword(s):  
Load Balancing ◽  
Ant Colony Optimization ◽  
Network Performance ◽  
Optimal Path ◽  
Optimal Solution ◽  
Software Defined Networking ◽  
Ant Colony ◽  
Centralized Control ◽  
Trip Time ◽  
Overall Performance

Load Balancing (LB) is one of the most important tasks required to maximize network performance, scalability and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), LB for SDN has become a very important issue. SDN decouples the control plane from the data forwarding plane to implement centralized control of the whole network. LB assigns the network traffic to the resources in such a way that no one resource is overloaded and therefore the overall performance is maximized. The Ant Colony Optimization (ACO) algorithm has been recognized to be effective for LB of SDN among several existing optimization algorithms. The convergence latency and searching optimal solution are the key criteria of ACO. In this paper, a novel dynamic LB scheme that integrates genetic algorithm (GA) with ACO for further enhancing the performance of SDN is proposed. It capitalizes the merit of fast global search of GA and efficient search of an optimal solution of ACO. Computer simulation results show that the proposed scheme substantially improves the Round Robin and ACO algorithm in terms of the rate of searching optimal path, round trip time, and packet loss rate.

scholarly journals A GENERALIZED CONVERGENCE RESULT FOR THE GRAPH-BASED ANT SYSTEM METAHEURISTIC

2003 ◽  
Vol 17 (4) ◽  
pp. 545-569 ◽  
Author(s):  
Walter J. Gutjahr
Keyword(s):  
Ant Colony Optimization ◽  
Wide Class ◽  
Optimal Solution ◽  
Convergence Result ◽  
Ant Colony ◽  
Restrictive Condition ◽  
Optimal Solutions ◽  
Ant System

It is shown that on fairly weak conditions, the current solutions of a metaheuristic following the ant colony optimization paradigm, the graph-based ant system, converge with a probability that can be made arbitrarily close to unity to one element of the set of optimal solutions. The result generalizes a previous result by removing the very restrictive condition that both the optimal solution and its encoding are unique (this generalization makes the proof distinctly more difficult) and by allowing a wide class of implementation variants in the first phase of the algorithm. In this way, the range of application of the convergence result is considerably extended.

scholarly journals Finding the shortest path using the ant colony optimization

2017 ◽  
Vol 7 (1.1) ◽  
pp. 392
Author(s):  
K Yella Swamy ◽  
Saranya Gogineni ◽  
Yaswanth Gunturu ◽  
Deepchand Gudapati ◽  
Ramu Tirumalasetti
Keyword(s):  
Ant Colony Optimization ◽  
Optimal Solution ◽  
Ant Colony ◽  
Main Concept ◽  
Pheromone Trails ◽  
Artificial Ants ◽  
Computer Based ◽  
Hard Problems ◽  
Short Time ◽  
Np Hard Problems

An ant colony optimization(ACO) is a techniquewhich is recently introduced ,and it is applied to solve several np-hard problems ,we can get optimal solution in a short time Main concept of the ACO is based on the behavior of ants in their colony for finding a source of food. They will communicate indirectly through pheromone trails. Computer based simulation is can generate good solution by using artificial ants, by using that general behavior we are solving travelling Sale man problem.

An Improved Ant Colony Optimization for VRP with Time Windows

2012 ◽  
Vol 263-266 ◽  
pp. 1609-1613 ◽  
Author(s):  
Su Ping Yu ◽  
Ya Ping Li
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Distribution Systems ◽  
Optimization Problems ◽  
Time Windows ◽  
Optimal Solution ◽  
Ant Colony ◽  
Routing Problem ◽  
Combinatorial Optimization Problems

The Vehicle Routing Problem (VRP) is an important problem occurring in many distribution systems, which is also defined as a family of different versions such as the Capacitated Vehicle Routing Problem (CVRP) and the Vehicle Routing Problem with Time Windows (VRPTW). The Ant Colony Optimization (ACO) is a metaheuristic for combinatorial optimization problems. Given the ACO inadequacy, the vehicle routing optimization model is improved and the transfer of the algorithm in corresponding rules and the trajectory updated regulations is reset in this paper, which is called the Improved Ant Colony Optimization (I-ACO). Compared to the calculated results with genetic algorithm (GA) and particle swarm optimization (PSO), the correctness of the model and algorithm is verified. Experimental results show that the I-ACO can quickly and effectively obtain the optimal solution of VRFTW.

Application of Improved Ant Colony Optimization on Economical Operation of Automatic Generation Control Units in Hydropower Station

2013 ◽  
Vol 860-863 ◽  
pp. 2101-2106 ◽  
Author(s):  
Yi Fan Li ◽  
Ke Guan Wang ◽  
Chuan Li Gong
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Automatic Generation ◽  
Ant Colony ◽  
Automatic Generation Control ◽  
Hydropower Station ◽  
Control Units ◽  
Convergence Performance ◽  
Generation Control

This paper proposed an improved ant colony optimization(ACO), to solve the economical operating dispatch of automatic generation control(AGC) units in hydropower station. The improved ant colony algorithm PSO-ACO imported particle swarm optimization is put forward. Both of the global convergence performance and the effectiveness of this algorithm is improved by using self-adaptive parameters and importing PSO to optimize the current ant paths. The mathematical description and procedure of the PSO-ACO are given with the maximum plant generating efficiency model as an example. Finally the superiority of the PSO-ACO is demonstrated by the application of AGC units on right bank of Three Gorges hydropower station. The optimal solution is more accurate and the calculation speed is higher than other methods.

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