Ant colony optimization algorithm is a heuristic approach for the solution of combinatorial optimization problems. In order to solve continuous optimization models, an ant colony optimization algorithm is designed. Based on this algorithm, two hybrid intelligent algorithms combined with fuzzy simulation and neural network or integral sum approximation are introduced for solving fuzzy expected v...

In allusion to the deficiencies of the ant colony optimization algorithm for solving the complex problem, the genetic algorithm is introduced into the ant colony optimization algorithm in order to propose a novel hybrid optimization (NHGACO) algorithm in this paper. In the NHGACO algorithm, the genetic algorithm is used to update the global optimal solution and the ant colony optimization algor...

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...

A novel version of Ant Colony Optimization (ACO) algorithms for solving continuous space problems is presented in this paper. The basic structure and concepts of the originally reported ACO are preserved and adaptation of the algorithm to the case of continuous space is implemented within the general framework. The stigmergic communication is simulated through considering certain direction vect...

Middle spatial resolution multi-spectral remote sensing image is a kind of color image with low contrast, fuzzy boundaries and informative features. In view of these features, the fuzzy C-means clustering algorithm is an ideal choice for image segmentation. However, fuzzy C-means clustering algorithm requires a pre-specified number of clusters and costs large computation time, which is easy to ...

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...

Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter. Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome ...

Processes that simulate natural phenomena have successfully been applied to a number of problems for which no simple mathematical solution is known or is practicable. Such meta-heuristic algorithms include genetic algorithms, particle swarm optimization and ant colony systems and have received increasing attention in recent years. This paper extends ant colony systems and discusses a novel data...

Constraint Cellular ant algorithm is a new optimization method for solving real problems by using both constraints method, the evolutionary rule of cellular, graph theory and the characteristics of ant colony optimization. Multi-objective vehicle routing problem is very important and practical in logistic research fields, but it is difficult to model and solve because objectives have complicate...