The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integra...

This paper presents an improved clustering algorithm with Ant Colony optimization (ACO) based on dynamical pheromones. Pheromone is an important factor for the performance of ACO algorithms. Two strategies based on adaptive pheromones which improved performance are introduced in this paper. One is to adjust the rate of pheromone evaporation dynamically, named as  , and the other is to adjust t...

The paper presents an (ACSA) Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents’ approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called “Ants” co-operates to find goo...

This paper presents an ant colony optimization based algorithm to solve real parameter optimization problems. In the proposed method, an operation similar to the crossover of the genetic algorithm is introduced into the ant colony optimization. The crossover operation with Laplace distribution following a few promising descent directions (FPDD-LX) is proposed to be performed on the pheromone of...

proposed to improve the performance of the ACO algorithm. In this paper an ant colony optimization with memory is proposed, which is applied to the classical traveling salesman problem (TSP). In the proposed algorithm, each ant searches the solution not only according to the pheromone and heuristic information but also based on the memory which is from the solution of the last iteration. A larg...

This paper proposes an extension method for Ant Colony Optimization (ACO) algorithm called Dynamic Impact. Impact is designed to improve convergence and solution quality solving challenging optimization problems that have a non-linear relationship between resource consumption fitness. proposed tested against the real-world Microchip Manufacturing Plant Production Floor (MMPPFO) problem theoreti...