— This paper examines ant colony optimization technique for edge detection. ACO is a technique to find out solutions for combinatorial optimization problem. The essential part of ACO algorithms is the combination of prior information about the structure of a solution with post information about the structure of previously obtained good solutions. Keywords— Ant Colony Optimization, Edge Detectio...

-The social insect allegory for working out problems has become a promising area in the recent years emphasizing on stochastic construction practice building the key probabilistically. The approach focuses on direct or indirect communications among uncomplicated agents. Swarm Intelligence is the collective behavior of decentralized, self-organized system whereby the joint behavior of agent inte...

Inventory routing problems arise as simultaneous decisions in inventory and routing optimization. In the present study, vendor managed inventory is proposed as a collaborative model for reverse supply chains and the optimization problem is modeled in terms of an inventory routing problem. The studied reverse supply chains include several return generators and recovery centers and one collection...

Over the past few years, swarm intelligence based optimization techniques such as ant colony optimization and particle swarm optimization have received considerable attention from engineering researchers. These algorithms have been used in the solution of various structural optimization problems where the main goal is to minimize the weight of structures while satisfying all design requirements...

In DNA based computation, the design of good DNA sequences has turned out to be a fundamental problem and one of the most practical and important research topics. Although the design of DNA sequences is dependent on the protocol of biological experiments, it is highly required to establish a method for the systematic design of DNA sequences, which could be applied to various design constraints....

Ant colony algorithm of the traditional combinative optimization consumes a large amount of time in the process of solving the optimization, which has a tendency to partial optimization and slow convergence along with many redundant useless iterative codes and low operation efficiency. A generic optimized ant colony algorithm is thus proposed. This algorithm has the ability to fast global searc...

A two-stage optimization method is presented by employing the evolutionary structural optimization (ESO) and ant colony optimization (ACO), which is called ESO-ACO method. To implement ESO-ACO, size optimization is performed using ESO, first. Then, the outcomes of ESO are employed to enhance ACO. In optimization process, the weight of double layer grid is minimized under various constraints whi...

Ant Colony Optimization (ACO) [2] is a nondeterministic algorithm framework that mimics the foraging behavior of ants to solve difficult optimization problems. Several researchers have successfully applied ant based algorithm framework in different fields of engineering, but never in VLSI testing. In this paper, we first describe the basics of ACO. We then consider the problem of simultaneously...

To enhance the optimization ability of ant colony optimization, this paper proposes a Bloch sphere-based quantum-inspired ant colony optimization algorithm. In the proposed approach, the positions of ants are encoded by qubits described on Bloch sphere. First, the destinations of ants are obtained by the select probability designed by the pheromone and heuristic information, and then, the movem...

Cloud Computing is growing rapidly and clients are demanding more services with better results. Therefore, load balancing as well as task scheduling in the Cloud has become a very interesting and important research area. In this paper, we present proposed approach for improving parameters in ant colonies function. The suggested definition of a new computational paradigm, is called Ant Colony Op...