An intelligent ant colony optimization using genetic operators and particles

作者：Kong Xiangling; Li Hongxing^*; Zhang Yinong

来源：Energy Education Science and Technology Part A: Energy Science and Research , 2014, 32(5): 3713-3724.

• Ant colony algorithms
• Ant Colony Optimization algorithms
• Crossover and mutation
• Extreme value
• Genetic operators
• Global optimal solutions
• Particle swarm algorithm
• Travelling salesman problem

Ant colony algorithm is a novel simulated evolutionary algorithm in recent years. It is widely used to solve optimization problems for discrete systems. However, the ant colony algorithm is used to have slow convergence and premature stagnation, when it is employed to optimize the complex system. This will affect the scope of its use. In order to solve the above problems, this paper presents an intelligent ant colony optimization algorithm based on genetic algorithm. It is introduced the idea of particle swarm algorithm in the ant colony algorithm, so the ants with particles. Besides, local and global optimal solutions are implemented by crossover and mutation operator, to dynamically update the pheromone. In this paper, the travelling salesman problem is solved by proposed algorithm and obtained satisfaction results. The emulation results show that it is effective.

• 出版日期2014