Ant Colony Algorithm and its Applications to Optimization of PID Parameters

2010 ◽  
Vol 431-432 ◽  
pp. 568-571 ◽  
Author(s):  
Sheng Li Song ◽  
Jian Zhao Zhou ◽  
Hai Tao Wang ◽  
Hu Sheng Feng ◽  
Ren He
Keyword(s):  
Computer Simulation ◽  
Genetic Algorithms ◽  
Pid Controller ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Mathematical Representation ◽  
Digital Pid ◽  
Tuning Pid Parameters

In this paper, an improved ACA (IACA) is used for tuning PID parameters. Firstly, the mathematical representation of conventional digital PID controller is deduced. Then genetic algorithms (GAs) and ACA are described. Thirdly, a PID controller based on IACA is designed. Computer simulation is performed for the proposed IACA-based PID controller finally. The results show that the proposed method could search the optimization PID parameters efficiently. The tryout of the designed controller in a paver walk system driven by valve-controlled motor runs well up to present.

scholarly journals Solving Random Travelling Salesman Problem using Firefly Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 1037-1041
Keyword(s):  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Firefly Algorithm ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Ant Colony ◽  
Ant Colony Algorithms ◽  
Comparison Of The Results ◽  
Discrete Optimization Problems

The firefly algorithm is a recently developed optimization algorithm, which is suitable for solving any kind of discrete optimization problems. This is an algorithm inspired from the nature. In this paper, a firefly algorithm is proposed to solve random traveling salesman problem. The solution to this problem is already proposed by the algorithms like simulated annealing, genetic algorithms and ant colony algorithms. This algorithm is developed to deal with the issue of accuracy and convergence rate in the solutions provided by those algorithms. A comparison of the results produced by proposed algorithm with the results of simulated annealing, genetic algorithms and ant colony algorithm is given. Finally, the effectiveness of the proposed algorithm is discussed.

scholarly journals Tuning PID Controller Using Multiobjective Ant Colony Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Ibtissem Chiha ◽  
Noureddine Liouane ◽  
Pierre Borne
Keyword(s):  
Genetic Algorithms ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Pid Controllers ◽  
Tuning Method ◽  
Classic Approach ◽  
Optimum Solution ◽  
Multiobjective Ant Colony Optimization

This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (Kp,Ki, andKd) by minimizing the multiobjective function. The potential of using multiobjective ant algorithms is to identify the Pareto optimal solution. The other methods are applied to make comparisons between a classic approach based on the “Ziegler-Nichols” method and a metaheuristic approach based on the genetic algorithms. Simulation results demonstrate that the new tuning method using multiobjective ant colony optimization has a better control system performance compared with the classic approach and the genetic algorithms.

Regulation of PID Controller Parameters Based on Ant Colony Optimization Algorithm in Bending Control System

2011 ◽  
Vol 128-129 ◽  
pp. 205-210 ◽  
Author(s):  
Yu Zhen Yu ◽  
Xin Yi Ren ◽  
Chun Yan Deng ◽  
Jing Jing Yu ◽  
Shu Zhen Li ◽  
...  
Keyword(s):  
Control System ◽  
Pid Controller ◽  
Ant Colony Algorithm ◽  
Control Method ◽  
External Disturbance ◽  
Good Control ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Control Effect ◽  
Dynamic Quality

The bending-roll control system has characteristics of time-varying, time-delay, nonlinearity and serious external disturbance, so it is hard to establish precise math model that be applied to control. In view of the problems above, ant colony algorithm is used to tuning parameters of PID controller and concrete step of PID parameters tuning based on ant colony algorithm is given. The simulation results indicate that the method not only improves control performance and dynamic quality, but also has strong self-adapting ability and robustness. It achieved a very good control effect when used in bending control system that proved the correctness and effectiveness of the control method.

The Main Steam Temperature Cascade PID Controller Parameters Tuning Optimization Based on Ant Colony Algorithm

2012 ◽  
Vol 239-240 ◽  
pp. 1442-1447
Author(s):  
Ji Ming Hu ◽  
Qing Shan Ji
Keyword(s):  
Pid Controller ◽  
Ant Colony Algorithm ◽  
Good Control ◽  
Absolute Error ◽  
Ant Colony ◽  
Steam Temperature ◽  
Control Effect ◽  
Tuning Method ◽  
Multiple Parameters ◽  
Main Steam

The main steam temperature of the boiler adopted usually a cascade PID control. Traditional PID parameter engineering tuning method has a very high demand for the control personnel. And the control loop of inner and outer ring interacted. Adjusting wasted the time and labor, and the control effects were difficult to achieve the optimal. Adopting the ant colony algorithm can optimize Cascade PID controller parameters. A combination of multiple parameters was the ant colony's foraging path, and the moment integral of absolute error was the optimization objective function, through the simulation for an ant foraging process, the optimal combination of parameters was identified. The simulation results showed that the main steam temperature cascade PID controller based on ant colony algorithm has good control effect.

HYBRID GENETIC — ANT COLONY ALGORITHMS FOR SOLVING AGGREGATE PRODUCTION PLAN

2005 ◽  
Vol 04 (01) ◽  
pp. 103-111 ◽  
Author(s):  
G. MOHAN KUMAR ◽  
A. NOORUL HAQ
Keyword(s):  
Genetic Algorithms ◽  
Long Range ◽  
Ant Colony Algorithm ◽  
Job Shop ◽  
Production Capacity ◽  
Ant Colony ◽  
Planning Problem ◽  
Production Plan ◽  
Ant Colony Optimization Algorithm ◽  
Aggregate Production

It is necessary for the management of any industry to workout an intermediate range plan also known as aggregate production plan, consistently with the long range policies and resources allocated by long range decisions. It is a procedure of translating the expected demand and production capacity of the available facilities into future manufacturing plans for a family of products. It includes decisions on production quantity, work force and inventory to workout a low cost product and timely delivery. Ant colony optimization algorithm finds its extensive application in solving job shop scheduling, assignment problems, transportation problems, etc. Genetic algorithms are proposed to solve the problem, already by the authors. In this paper, an attempt is made to solve an aggregate production-planning problem for obtaining an effective solution using ant colony algorithm. Also a hybrid algorithm that combines genetic algorithm and ant colony algorithm is proposed and its effectiveness over the models developed using genetic algorithms and ant colony algorithm is also analyzed.

scholarly journals Study on an improved algorithm for optimization of PID parameters

2016 ◽  
Vol 12 (02) ◽  
pp. 58 ◽  
Author(s):  
Shaofei Wu
Keyword(s):  
Control System ◽  
Pid Controller ◽  
Ant Colony Algorithm ◽  
Dynamic Performance ◽  
Ant Colony ◽  
Temperature Control System ◽  
Steam Temperature ◽  
Main Steam ◽  
Improved Ant Colony Algorithm ◽  
Controlled Object

The improved ant colony algorithm is the hybrid algorithm consisting of the genetic algorithm and ant colony algorithm convergence. Through the introduction of the gauss mutation, we achieve the goal of improving ant colony algorithm. Using coal-fired power plant unit as main steam temperature controlled object, we design the PID controller based on improved ant colony algorithm. And setting of PID parameters by Z - N method has carried on the comparative analysis of the main steam temperature control system. Simulation results show that PID optimization based on improved ant colony algorithm can greatly improve the dynamic performance of the control system. So we verify the sophistication and effectiveness of the algorithm.

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