Parameters Of Ant Colony Algorithm Research Articles

Ant colony optimization-based adjusted PID parameters: a proposed method

The ant colony algorithm (ACA) is a heuristic algorithm that resolves the optimality problem by simulating an ant’s foraging process, which finds the shortest path. The connotation of the ACA is to find the optimal solution. The Proportional Integral Derivative (PID) parameter tuning is an essential tool in the control field and includes three parameters, Kp, Ki, and Kd, to achieve the best control effect. Besides, tuning the PID parameters is closely related to finding the “optimal” solution that can be attained based on the feasible combination of the two. This article transforms the PID parameter tuning problem into an ACA that finds the optimal solution called ACA-based PID parameters tuning. Furthermore, PID control is simulated by setting the parameters of ACA, such as ant colony size, iteration times, nodes, paths, path evaluation criteria, pheromone concentration, heuristic function, weight factor, and decision function. Eventually, the two PID controller parameter tuning strategies are compared and analyzed, and the advantages and disadvantages of each are obtained. Compared with the 4:1 attenuation curve method, the proposed method can significantly reduce the MP score of the overshoot of the system, increase the time, and improve the dynamic and steady-state performance of the system, but reduce the steady-state error of the system. Therefore, the feasibility and effectiveness of the proposed method is verified.

Optimization of Tool Path Length on Three-Dimensional Drilling Application Using Ant Colony Algorithm

The lower machining time is important characteristic in the drilling machining process. Drilling process costs will increase if the machining time is high. Therefore, the main objective of this research is to develop Ant Colony Algorithm (ACO) to reduce the machining time by obtain the optimal tool path length. By using this algorithm, it can minimize the tool path length and significantly decreasing the machining time of drilling process. Simulating in 3-dimensional drilling on ACO has been constructed to minimize the shortest path of the drilling process. There are two type of workpiece has been used, which is simple block with 10 holes and complex block design that has 154 holes. ACO algorithm has been developed in Matlab R2017b to determine the optimal parameters of ACO of tool path length in drilling. Besides, simulation also has been done to investigate the effect of ACO parameter which is weight of pheromone (α), weight of trail (β), evaporation coefficient (e), and number of iterations. As a result, by define the parameter of iteration number at 900, the optimum parameter of weight of pheromone (α) is 5, weight of trail (β) is 4 and evaporation coefficient (e) is 0.4. Based on these parameters, the minimal tool path length obtain for simple and complex model are 286.965 mm and 6770.9860 mm respectively. Then, the result of tool path length of ACO simulation has been compared with the Mastercam outcome. ACO achieves a total tool path length of 286.965 mm while Mastercam achieved 569.878 mm for simple block design. Meanwhile, for complex block design, ACO produces a total tool path length of 6770.9860 mm while Mastercam has generate 55828.9050 mm of tool path length. By comparing these two approaches, ACO and Mastercam, ACO has that the short total tool path length by 49.64% on simple block design and 87.87% for complex block design.

Dynamic Path Planning Based on Improved Ant Colony Algorithm in Traffic Congestion

Congestion road condition is an important factor that must be considered in urban traffic path planning, while most path planning algorithms only consider the distance factor, which is not suitable for the current complex urban traffic congestion road condition. In order to solve the above problems, this article proposes a dynamic path planning method based on improved ant colony algorithm in congested traffic. The method quantifies the main attributes of urban road length, number of lanes, incoming and outgoing traffic flow, and introduces the road factor used for replacing the distance parameters of particle swarm optimization and ant colony algorithm. In the method, the particle swarm algorithm can effectively optimize the parameters of the ant colony algorithm, and significantly improve the efficiency of ant colony algorithm, such that it is more applicable for dynamic path planning application to greatly reduce the congestion rate of path planning. In addition, this article selects some intersections in the Beijing area to carry out the dynamic path planning experiment based on the improved ant colony algorithm under congested road conditions. The experimental results show that, compared with the ant colony algorithm based on distance parameter, the proposed dynamic path planning method can effectively reduce the average congestion rate ranging from 9.73% to 13.63%.

An improved particle swarm-ant colony hybrid algorithm for HMM training

The traditional parameter estimation methods of Hidden Markov Models (HMM) are easy to fall into local optimum, have higher requirements for initial parameter values and might result in over-coupling phenomena. In order to improve the robustness and identification performance of the model, a novel HMM parameter training method based on an improved particle swarm-ant colony algorithm (IPSAA) is presented. First, extremum disturbance is added into particle swarm optimisation algorithm (PSO) and parameters of ant colony algorithm (ACA) such as stimulating factor, volatilisation coefficients and pheromone are all improved adaptively. Second, the fitness function values of particles' history optimal solutions after PSO coarse search are used to adjust the initial pheromone distribution in fine search of ACA. Finally, Baum-Welch algorithm (B-W) is adopted to locally modify the approximate global optimal solution. The new algorithm not only solves the BW dependency on initial values and the trapped local optimum problem, but also makes full use of the global search ability of IPSAA and local development ability of B-W. The experimental results show that the system using the new algorithm is more efficient, more stable, and has better classification performance than that of traditional HMM training algorithm.

Application of Ant Colony Algorithm Based on Optimization Parameters in Equipment Material Transport

In order to solve the path selection problem in the transport of equipment and materials, while improving the quality of solutions, this paper uses ant colony algorithm based on optimization parameters to achieve. Through genetic algorithm to solve the parameters of ant colony algorithm, resulting in a better performance parameters. The experimental results show that ant colony algorithm based on optimization parameters has been improved on path length and computation time than the traditional ant colony algorithm.

An Anycast Routing Algorithm Based on the Combination of Genetic Algorithm and Ant Colony Algorithm

To solve anycast routing problem with multiple QoS constraints, a improved hybrid algorithm which combines genetic algorithm and ant colony algorithm is proposed. In the initial period of hybrid algorithm, genetic algorithm was used to distribute pheromones in links and code and optimize control parameters of ant colony algorithm. Through judgment function, this algorithm can judge the time to combine the genetic algorithm with ant colony algorithm, and initialize the pheromones and start the ant colony algorithm at the last period of hybrid algorithm. To avoid hybrid algorithm falling into local optimal solution, a mutation operator was introduced in algorithm hybrid to update local pheromones of new path produced by mutation operation and reduced pheromones concentration on optimal path in time. The NS2 simulation results show that this algorithm can commendably solve the anycast routing problem with multiple QoS constraints, and its performance is better than other two algorithms.