Time Ant Colony Algorithm with Genetic Algorithms

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.

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Solving Random Travelling Salesman Problem using Firefly Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c9113.029420 ◽

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.

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Tuning PID Controller Using Multiobjective Ant Colony Optimization

Applied Computational Intelligence and Soft Computing ◽

10.1155/2012/536326 ◽

2012 ◽

Vol 2012 ◽

pp. 1-7 ◽

Cited By ~ 52

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.

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HYBRID GENETIC — ANT COLONY ALGORITHMS FOR SOLVING AGGREGATE PRODUCTION PLAN

Journal of Advanced Manufacturing Systems ◽

10.1142/s021968670500059x ◽

2005 ◽

Vol 04 (01) ◽

pp. 103-111 ◽

Cited By ~ 16

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.

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