One merit of genetic algorithm is fast overall searching, but this algorithm usually results in low efficiency because of large quantities of redundant codes. The advantages of ant colony algorithm are strong suitability and good robustness while its disadvantages are tendency to stagnation, slow speed of convergence. Put forward based on improved ant colony algorithm for wireless sensor networ...

In this paper, we present a hybrid algorithm combining ant colony optimization algorithm with the taboo search algorithm for the classical job shop scheduling problem. Instead of using the conventional construction approach to construct feasible schedules, the proposed ant colony optimization algorithm employs a novel decomposition method inspired by the shifting bottleneck procedure, and a mec...

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...

Considering the complete process of surgery including the preoperative and postoperative stages, multiple resource constraints involved and the integration of surgical upstream and downstream resources, surgery scheduling was described as an extended multi-resource constrained flexible job-shop scheduling problem and an optimization approach was proposed based on an improved ant colony algorith...

In this paper we describe the application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of PI controller and to find the best optimal intelligent controller. The ACO algorithm is a bio-inspired optimization method that has proven its success through various combinatorial optimization problems. The parameters of the PI Controller are evaluated by an ant c...

To find out an effective way to solve the real estate portfolio optimization, an improved Ant Colony Algorithm based on information entropy was proposed. The information entropy was used to control the path selection and evolutional strategy by self-adjusting to overcome the premature convergence problem of the basic Ant Colony Algorithm. Simulation study on Traveling Salesman Problem and the a...

Solving large traveling salesman problem (TSP) in an efficient way is a challenging area for the researchers of computer science. This paper presents a modified version of the ant colony system (ACS) algorithm called Red-Black Ant Colony System (RB-ACS) for the solutions of TSP which is the most prominent member of the combinatorial optimization problem. RB-ACS uses the concept of ant colony sy...

Genetic Algorithm (GA), Ant Colony Optimization (ACO) algorithm and Particle Swarm Optimization (PSO) are proposed for feature selection, and their performance is compared. The Spatial Gray Level Dependence Method (SGLDM) is used for feature extraction. The selected features are fed to a three-layer Backpropagation Network hybrid with Ant Colony Optimization and Particle Swarm Optimization (BPN...

Job scheduling is one of the main factors affecting grid computing performance. Job scheduling problem classified as an NP-hard problem. Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms. Ant colony system algorithm is a meta-heuristic algorithm which has the ability to solve differenttypes of NP-hard problems.However, ant colony s...