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...

Ants have always fascinated human beings. What particularly strikes the occasional observer as well as the scientist is the high degree of societal organization that these insects can achieve in spite of very limited individual capabilities (Dorigo et al., 2000). Ants have inspired also a number of optimization algorithms. These algorithms are increasingly successful among researches in compute...

The well-known Ant Colony Optimization method is applied to the problem of tuning the parameters of a Takagi-Sugeno fuzzy rule base. The latest developed Continuous Ant Colony System algorithm is used to find the global solution of the continuous optimization problem. The method is systematically tested for the approximation of one analytical test function and the performance of the proposed sc...

nowadays, software cost estimation (sce) with machine learning techniques are more performance than other traditional techniques which were based on algorithmic techniques. in this paper, we present a new hybrid model of multi-layer perceptron (mlp) artificial neural network (ann) and ant colony optimization (aco) algorithm for high accuracy in sce called multilayer perceptron ant colony optimi...

Ant colony optimization is a technique for optimization that was introduced in the early 1990’s. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems, to continuous optimization problems, and to important problems in telecommun...

In this paper we present an extension of ant colony optimization (ACO) to continuous domains. We show how ACO, which was initially developed to be a metaheuristic for combinatorial optimization, can be adapted to continuous optimization without any major conceptual change to its structure. We present the general idea, implementation, and results obtained. We compare the results with those repor...

This paper describes the so-called Differential Ant-Stigmergy Algorithm (DASA), which is an extension of the Ant-Colony Optimization for continuous domain. A performance study of the DASA on a benchmark of real-parameter optimization problems is presented. The DASA is compared with a number of evolutionary optimization algorithms including covariance matrix adaptation evolutionary strategy, dif...

Many real world problems can be modelled as an optimization problem. Evolutionary algorithms are used to solve these problems. Ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. These ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. Ant colony optim...

Ant colony algorithms are a class of metaheuristics which are inspired from the behaviour of real ants. The original idea consisted in simulating the stigmergic communication, therefore these algorithms are considered as a form of adaptive memory programming. A new formalization was proposed for the design of ant colony algorithms, introducing the biological notions of heterarchy and communicat...