This paper presents an application of the Ant colony optimization metaheuristic to the job shop scheduling problem. A pheromone alteration strategy which improves the basic ant system by utilizing the behaviour of artificial ants to perform local search is introduced. Experiments using well-known benchmark JSSP problems show that this approach improves on the performance obtained by the basic a...

The well-known ant colony optimization meta-heuristic is applied to design a new command to line-of-sight guidance law. In this regard, the lately developed continuous ant colony system is used to optimize the parameters of a pre-constructed fuzzy sliding mode controller. The performance of the resulting guidance law is evaluated at different engagement scenarios.

Fact Gathering means generating rule base from available numerical data or data base. The intelligence of a fuzzy system lies in its rule base. Generating rule base is one of the most important and difficult tasks when designing fuzzy systems. Various rule base generation methods are used such as Neural networks, genetic algorithms, biogeography based optimization approach, ant colony optimizat...

Search stagnation is a serious problem that all Ant Colony Optimization (ACO) algorithms suffer from regardless of their application domain. The framework of Interacted Multiple Ant Colonies Optimization (IMACO) is a recent proposition. It divides the ants’ population into several colonies and employs certain techniques to organize the work of these colonies. This paper proposes new effective e...

QoS-aware service selection is an active area of research on Web services composition. It is a complex combinatorial optimization problem, which solves how to find a best composition plan that maximizes user QoS requirement. This paper presents a QoS-aware Web service selection algorithm based on Ant Colony System. Firstly, a proposed Web Services Composition graph (WSC graph) is applied to mod...

In this paper we consider MAX–MIN Ant System and Ant Colony System. They are generally recognized to be the best performing algorithms of the Ant Colony Optimization family. They are characterized by a quite different way for dealing with the pheromone trail. We propose an experimental analysis for observing whether this difference impacts significantly on the characteristics of the pheromone d...

The paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, ...

In view of shortcomings of BP neural network, which is slow to converge and tends to trap in local optimum when applied in fault diagnosis, an approach for fault diagnosis based on BP neural network optimized by chaos ant colony algorithm is proposed. Mathematical model of chaos ant colony algorithm is created. Real-coded method is adopted and the weights and thresholds of BP neural network are...

This study presents and evaluates a modified ant colony optimization (ACO) approach for the precedence and resource-constrained multiprocessor scheduling problems. A modified ant colony system is proposed to solve the scheduling problems. A two-dimensional matrix is proposed in this study for assigning jobs on processors, and it has a time-dependency relation structure. The dynamic rule is desi...