Издательство InTech, 2011, -352 pp.
Invented by Marco Dorigo in 1992, Ant Colony Optimization (ACO) is a meta-heuristic stochastic combinatorial computational discipline inspired by the behavior of ant colonies which belong to a family of meta-heuristic stochastic methodologies such as simulated annealing, Tabu search and genetic algorithms. It is an iterative method in which populations of ants act as agents that construct bundles of candidate solutions, where the entire bundle construction process is probabilistically guided by heuristic imitation of ants’ behavior, tailor-made to the characteristics of a given problem. Since its invention ACO was successfully applied to a broad range of NP hard problems such as the traveling salesman problem (TSP) or the quadratic assignment problem (QAP), and is increasingly gaining interest for solving real life engineering and scientifi c problems.
This book covers state of the art methods and applications of ant colony optimization algorithms. It incorporates twenty chapters divided into two parts: methods (nine chapters) and applications (eleven chapters). New methods, such as multi colony ant algorithms based upon a new pheromone arithmetic crossover and a repulsive operator, as well as a diversity of engineering and science applications from transportation, water resources, electrical and computer science disciplines are presented. The following is a list of the chapter’s titles and authors, and a brief description of their contents.
Part 1 Methods
Multi-Colony Ant Algorithm
Continuous Dynamic Optimization
An AND-OR Fuzzy Neural Network
Some Issues of ACO Algorithm Convergence
On Ant Colony Optimization Algorithms for Multiobjective Problems
Automatic Construction of Programs Using Dynamic Ant Programming
A Hybrid ACO-GA on Sports Competition Scheduling
Adaptive Sensor-Network Topology Estimating Algorithm Based on the Ant Colony Optimization
Ant Colony Optimization in Green Manufacturing
Part 2 Applications
Optimizing Laminated Composites Using Ant Colony Algorithms
Ant Colony Optimization for Water Resources Systems Analysis – Review and Challenges
Application of Continuous ACOR to Neural Network Training: Direction of Arrival Problem
Ant Colony Optimization for Coherent Synthesis of Computer System
Ant Colony Optimization Approach for Optimizing Traffic Signal Timings
Forest Transportation Planning Under Multiple Goals Using Ant Colony Optimization
Ant Colony System-based Applications to Electrical Distribution System Optimization
Ant Colony Optimization for Image Segmentation
SoC Test Applications Using ACO Meta-heuristic
Ant Colony Optimization for Multiobjective Buffers Sizing Problems
On the Use of ACO Algorithm for Electromagnetic Designs
Invented by Marco Dorigo in 1992, Ant Colony Optimization (ACO) is a meta-heuristic stochastic combinatorial computational discipline inspired by the behavior of ant colonies which belong to a family of meta-heuristic stochastic methodologies such as simulated annealing, Tabu search and genetic algorithms. It is an iterative method in which populations of ants act as agents that construct bundles of candidate solutions, where the entire bundle construction process is probabilistically guided by heuristic imitation of ants’ behavior, tailor-made to the characteristics of a given problem. Since its invention ACO was successfully applied to a broad range of NP hard problems such as the traveling salesman problem (TSP) or the quadratic assignment problem (QAP), and is increasingly gaining interest for solving real life engineering and scientifi c problems.
This book covers state of the art methods and applications of ant colony optimization algorithms. It incorporates twenty chapters divided into two parts: methods (nine chapters) and applications (eleven chapters). New methods, such as multi colony ant algorithms based upon a new pheromone arithmetic crossover and a repulsive operator, as well as a diversity of engineering and science applications from transportation, water resources, electrical and computer science disciplines are presented. The following is a list of the chapter’s titles and authors, and a brief description of their contents.
Part 1 Methods
Multi-Colony Ant Algorithm
Continuous Dynamic Optimization
An AND-OR Fuzzy Neural Network
Some Issues of ACO Algorithm Convergence
On Ant Colony Optimization Algorithms for Multiobjective Problems
Automatic Construction of Programs Using Dynamic Ant Programming
A Hybrid ACO-GA on Sports Competition Scheduling
Adaptive Sensor-Network Topology Estimating Algorithm Based on the Ant Colony Optimization
Ant Colony Optimization in Green Manufacturing
Part 2 Applications
Optimizing Laminated Composites Using Ant Colony Algorithms
Ant Colony Optimization for Water Resources Systems Analysis – Review and Challenges
Application of Continuous ACOR to Neural Network Training: Direction of Arrival Problem
Ant Colony Optimization for Coherent Synthesis of Computer System
Ant Colony Optimization Approach for Optimizing Traffic Signal Timings
Forest Transportation Planning Under Multiple Goals Using Ant Colony Optimization
Ant Colony System-based Applications to Electrical Distribution System Optimization
Ant Colony Optimization for Image Segmentation
SoC Test Applications Using ACO Meta-heuristic
Ant Colony Optimization for Multiobjective Buffers Sizing Problems
On the Use of ACO Algorithm for Electromagnetic Designs