Ant colony optimization (ACO) and particle swarm optimization (PSO) are two popular algorithms in swarm intelligence. Recently, a continuous ACO named ACOR was developed to solve the continuous optimization problems. This study incorporated ACOR with PSO to improve the search ability, investigating four types of hybridization as follows: (1) sequence approach, (2) parallel approach, (3) sequenc...

Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the pheromone trail laying and following behavior of some ant species. Artificial ants in ACO are stochastic solution construction procedures that build candidate solutions for the problem instance under concern by exploiting (artificial) pheromone information that is adapted based on the ants’ search experience and possibly a...

For a decade swarm Intelligence is concerned with the design of intelligent systems by taking inspiration from the collective behaviors of social insects. Swarm Intelligence is a successful paradigm for the algorithm with complex problems. This paper focuses on the procedure of most successful methods of optimization techniques inspired by Swarm Intelligence: Ant Colony Optimization (ACO) and P...

Ant Colony Optimization (ACO) a natureinspired metaheuristic algorithm has been successfully applied in the traveling salesman problem (TSP) and a variety of combinatorial problems. ACO algorithms have been modified in recent years to improve the performance of the first algorithm, posed by Dorigo. In this paper we compare different ACO algorithms and combine them in order to collect their adva...

In this paper, an improved ant colony optimization (ACO) algorithm is proposed to enhance the performance of global optimum search. The strategy of the proposed algorithm has the capability of fuzzy pheromone updating, adaptive parameter tuning, and mechanism resetting. The proposed method is utilized to tune the parameters of the fuzzy controller for a real beam and ball system. Simulation and...

Ant colony optimization (ACO) is a metaheuristic that produces good results for a wide range of combinatorial optimization problems. Often such successful applications use a combination of ACO and local search procedures that improve the solutions constructed by the ants. In this paper, we study this combination from a theoretical point of view and point out situations where introducing local s...

In the current Mobile Ad-hoc Network (MANET) route discovery procedure, traffic overflow and overhead may pose a major challenge for path finding between communicating nodes. Swarm intelligence technique has been applied various routing problems. We suggest an ant-based bottleneck method MANET to identify weak links in chosen delay issues. During data exchange, it selects using swarm based call...

Wireless Sensor Networks have become an important research topic in last year. WSN is a collection of tiny, large number of densely deployed sensor node; these sensor nodes are smart, effective which is very powerful and versatile networking where traditional wired and wireless networking is unable to deploy. These sensor nodes have limited transmission range, processing and storage capabilitie...

In this paper, a procedure for quantifying valve stiction in control loops based on ant colony optimization has been proposed. Pneumatic control valves are widely used in the process industry. The control valve contains non-linearities such as stiction, backlash, and deadband that in turn cause oscillations in the process output. Stiction is one of the long-standing problems and it is the most ...

Ant colony optimization (ACO) is a metaheuristic for solving combinatorial optimization problems that is based on the foraging behavior of biological ant colonies. Starting with the 1996 seminal paper by Dorigo, Maniezzo and Colorni, ACO techniques have been used to solve the traveling salesperson problem (TSP). In this paper, we focus on a particular type of the ACO algorithm, namely, the rank...