In cloud computing tasks scheduling problem is NP-hard, furthermore it does onerous for attaining an optimum resolution. Extremely quick optimization algorithms are used to proximate the optimum resolution, like ACO (ant colony optimization) algorithm. In cloud computing, in consideration to solve the problem of task scheduling, a period ACO (PACO)-based arranging algorithmic rule has been used...

water conveyance systems (wcss) are costly infrastructures in terms of materials, construction, maintenance and energy requirements. much attention has been given to the application of optimization methods to minimize the costs associated with such infrastructures. historically, traditional optimization techniques have been used, such as linear and non-linear programming. in this paper, applica...

This study deals with performance-based design optimization (PBDO) of steel moment frames employing four different metaheuristics consisting of genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), and particle swarm optimization (PSO). In order to evaluate the seismic capacity of the structures, nonlinear pushover analysis is conducted (PBDO). This method is an iterative ...

Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based...

-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...

Search-based optimization techniques (e.g., hill climbing, simulated annealing, and genetic algorithms) have been applied to a wide variety of software engineering activities including cost estimation, next release problem, and test generation. Several search based test generation techniques have been developed. These techniques had focused on finding suites of test data to satisfy a number of ...

we propose a novel hybrid algorithm named ACO-FA, which integrates the merits of ant colony optimization (ACO) with firefly algorithm (FA) to solve unconstrained optimization problems. The main feature of the hybrid algorithm is to hybridize the solution construction mechanism of the ACO with the FA. In our hybrid algorithm, the initial solutions are generated randomly from the search space, an...

Ant colony optimization (ACO) algorithms have been applied successfully to combinatorial optimization problems. More recently, Parpinelli et al have applied ACO to data mining classification problems, where they introduced a classification algorithm called Ant Miner. In this paper, we present a system that combines both the proposed Improved Quickreduct algorithm for data preprocessing and ant ...

High dimensionality has been a major problem for gene array-based cancer classification. Feature Selection (FS) is ordinarily used as a useful technique in order to reduce the dimension of the dataset. For that to get advantages from both methods of feature selection, Individual Feature Ranking (IFR) and Feature Subset Selection (FSS) are combined which is a hybrid approach. Information Gain fr...

Limited amount of time and computational resources in industrial domain makes Ant Colony Optimization (ACO) a useful approach to find near optimal solutions in polynomial time for Nondeterministic Polynomial time (NP) problems. For dynamically changing graphs, such as in case of network routing and urban transportation systems which are based on Travelling Salesman Problem (TSP), the ant colony...