Abstract
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.
Highlights
Ant colony algorithm[1] is a kind of hot bionic algorithm following new bionic algorithms such as the simulated annealing method, genetic algorithm, tabu search algorithm, and artificial neural network
A lot of research results show that the single use of a genetic algorithm or an ant colony algorithm to solve a variety of optimization problems makes it impossible to find a satisfied solution. [3]
The ant colony optimization (ACO) algorithm is a population based on a heuristic bionic evolutionary algorithm; this algorithm adopts distributed parallel computing, a positive feedback mechanism, is easy to combine with other methods, has stronger robustness, and is especially suitable for solution of the combinatorial optimization problem (COP)
Summary
Ant colony algorithm[1] is a kind of hot bionic algorithm following new bionic algorithms such as the simulated annealing method, genetic algorithm, tabu search algorithm, and artificial neural network. Genetic algorithm [2] is a kind of search algorithm based on space, through selection, heredity and mutation operation, and Darwin's theory of survival of the fittest, simulating the natural evolution to find a solution for the problem. Genetic algorithm and ant colony algorithm are both very good artificial intelligence algorithms, including the good abilities of global optimization. A lot of research results show that the single use of a genetic algorithm or an ant colony algorithm to solve a variety of optimization problems makes it impossible to find a satisfied solution. We put forward a kind of improved ant colony algorithm combined with a new ant colony algorithm and genetic algorithm
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