Using Simulated Annealing And Ant Colony Optimization Algorithms To Solve The Scheduling Problem
Download Using Simulated Annealing And Ant Colony Optimization Algorithms To Solve The Scheduling Problem PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Using Simulated Annealing And Ant Colony Optimization Algorithms To Solve The Scheduling Problem book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Using Simulated Annealing and Ant-colony Optimization Algorithms to Solve the Scheduling Problem
The scheduling problem is one of the most challenging problems faced in many different areas of everyday life. This problem can be formulated as a combinatorial optimization problem, and it has been solved with various methods using heuristics and intelligent algorithms. We present in this research an efficient solution to the scheduling problem using two different heuristics namely Simulated Annealing and Ant Colony Optimization. A study comparing the performances of both solutions is described and the results are analyzed.
Informatics and Management Science V
Author: Wenjiang Du
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
The International Conference on Informatics and Management Science (IMS) 2012 will be held on November 16-19, 2012, in Chongqing, China, which is organized by Chongqing Normal University, Chongqing University, Shanghai Jiao Tong University, Nanyang Technological University, University of Michigan, Chongqing University of Arts and Sciences, and sponsored by National Natural Science Foundation of China (NSFC). The objective of IMS 2012 is to facilitate an exchange of information on best practices for the latest research advances in a range of areas. Informatics and Management Science contains over 600 contributions to suggest and inspire solutions and methods drawing from multiple disciplines including: Computer Science Communications and Electrical Engineering Management Science Service Science Business Intelligence
Ant Colony Optimization
Ants communicate information by leaving pheromone tracks. A moving ant leaves, in varying quantities, some pheromone on the ground to mark its way. While an isolated ant moves essentially at random, an ant encountering a previously laid trail is able to detect it and decide with high probability to follow it, thus reinforcing the track with its own pheromone. The collective behavior that emerges is thus a positive feedback: where the more the ants following a track, the more attractive that track becomes for being followed; thus the probability with which an ant chooses a path increases with the number of ants that previously chose the same path. This elementary ant's behavior inspired the development of ant colony optimization by Marco Dorigo in 1992, constructing a meta-heuristic stochastic combinatorial computational methodology belonging to a family of related meta-heuristic methods such as simulated annealing, Tabu search and genetic algorithms. This book covers in twenty chapters state of the art methods and applications of utilizing ant colony optimization algorithms. New methods and theory such as multi colony ant algorithm based upon a new pheromone arithmetic crossover and a repulsive operator, new findings on ant colony convergence, and a diversity of engineering and science applications from transportation, water resources, electrical and computer science disciplines are presented.