Context: Emergence is a term used to describe complex patterns or systems which are formed from very basic rules or agents. Once such example which has intrigued scientists across several fields has been ant colonies. Computer scientists are particularly interested in ant colonies as although each individual ant acts in a very simple manor, the ant colony as a whole is able to find shortest pat...

Guiding vehicles to their destination under dynamic traffic conditions is an important topic in the field of Intelligent Transportation Systems (ITS). Nowadays, many complex systems can be controlled by using multi agent systems. Adaptation with the current condition is an important feature of the agents. In this research, formulation of dynamic guidance for vehicles has been investigated based...

the capacitated clustering problem (ccp) is one of the most importantcombinational optimization problems that nowadays has many real applications inindustrial and service problems. in the ccp, a given n nodes with known demandsmust be partitioned into k distinct clusters in which each cluster is detailed by anode acting as a cluster center of this cluster. the objective is to minimize the sumof...

This paper presents a robust hybrid improved dolphin echolocation and ant colony optimization algorithm (IDEACO) for optimization of truss structures with discrete sizing variables. The dolphin echolocation (DE) is inspired by the navigation and hunting behavior of dolphins. An improved version of dolphin echolocation (IDE), as the main engine, is proposed and uses the positive attributes of an...

Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages long running time and easy stagnation still restrict its further wide application many fields. In this study, saltatory evolution (SEACO) is proposed to incr...

This paper gives a brief about two of the metaheuristic techniques that are used to find best among the optimal solutions for complex problems like travelling salesman problem, Quadratic problem. Both of these techniques are based on the natural phenomenon of ant. Ant algorithm find good path but due to some short comings of it, this algorithm is not able to give best out of the good or optimal...

This work proposes an algorithm for rule discovery called Ant-Miner (Ant Colony-based Data Miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is based on recent research on the behavior of real ant colonies as well as in some data mining concepts. We compare the performance of Ant-Miner with the performance of the well-known C4.5 algorithm on six public do...

Ant colony algorithm, a heuristic simulated algorithm, provides better solutions for non-convex, non-linear and discontinuous optimization problems. For ant colony algorithm, it is frequently to be trapped into local optimum, which might lead to stagnation. This article presents the city-select strategy, local pheromone update strategy, optimum solution prediction strategy and local optimizatio...

Designing the fuzzy controllers by using evolutionary algorithms and reinforcement learning is an important subject to control the robots. In the present article, some methods to solve reinforcement fuzzy control problems are studied. All these methods have been established by combining Fuzzy-Q Learning with an optimization algorithm. These algorithms include the Ant colony, Bee Colony and Arti...

The first ant colony optimization (ACO) called ant system was inspired through studying of the behavior of ants in 1991 by Macro Dorigo and co-workers [1]. An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony. Optimization problems can be solved through simulating ant’s behaviors. Since the first ant system algorithm was proposed, there is...