Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony Ant colony optimization exploits a similar mechanism for solving optimization problems From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO The goal of this article is to introduce ant colony optimization and to survey its most notable applications

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Ant Colony Optimization

The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

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Metaheuristics in combinatorial optimization: Overview and conceptual comparison

A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

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Metaheuristics: From Design to Implementation

The introduction of ant colony optimization (ACO) and to survey its most notable applications are discussed. Ant colony optimization takes inspiration from the forging behavior of some ant species. These ants deposit Pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. The model proposed by Deneubourg and co-workers for explaining the foraging behavior of ants is the main source of inspiration for the development of ant colony optimization. In ACO a number of artificial ants build solutions to an optimization problem and exchange information on their quality through a communication scheme that is reminiscent of the one adopted by real ants. ACO algorithms is introduced and all ACO algorithms share the same idea and the ACO is formalized into a meta-heuristics for combinatorial problems. It is foreseeable that future research on ACO will focus more strongly on rich optimization problems that include stochasticity.

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Ant colony optimization: artificial ants as a computational intelligence technique

http://personal.cimat.mx:8181/~jortega/MaterialDidactico/modestoI10/Proyectos/Blum%20%282005%29AntCol.pdf

Ant colony optimization: Introduction and recent trends

Scheduling and multicriteria optimisation theory have been subject, separately, to numerous studies. Since the last twenty years, multicriteria scheduling problems have been subject to a growing interest. However, a gap between multicriteria scheduling approaches and multicriteria optimisation field exits. This book is an attempt to collect the elementary of multicriteria optimisation theory and the basic models and algorithms of multicriteria scheduling. It is composed of numerous illustrations, algorithms and examples which may help the reader in understanding the presented concepts. This book covers general concepts such as Pareto optimality, complexity theory, and general method for multicriteria optimisation, as well as dedicated scheduling problems and algorithms: just-in-time scheduling, flexibility and robustness, single machine problems, parallel machine problems, shop problems, etc. The second edition contains revisions and new material.

Multicriteria Scheduling: Theory, Models and Algorithms

An Ant Colony Optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem

I Methodology.- A Tutorial on Evolutionary Multiobjective Optimization.- 2 Bounded Pareto Archiving: Theory and Practice.- 3 Evaluation of Multiple Objective Metaheuristics.- 4 An Introduction to Multiobjective Metaheuristics for Scheduling and Timetabling.- II Problem-oriented Contributions.- 5 A Particular Multiobjective Vehicle Routing Problem Solved by Simulated Annealing.- 6 A Dynasearch Neighborhood for the Bicriteria Traveling Salesman Problem.- 7 Pareto Local Optimum Sets in the Biobjective Traveling Salesman Problem: An Experimental Study.- 8 A Genetic Algorithm for Tackling Multiobjective Job-shop Scheduling Problems.- 9 RPSGAe - Reduced Pareto Set Genetic Algorithm: Application to Polymer Extrusion.

Metaheuristics for Multiobjective Optimisation

This paper presents a state-of-the-art survey on multicriteria scheduling and introduces a definition of a multicriteria scheduling problem. It provides a framework that allows to tackle multicriteria scheduling problems, according to Decision Aid concepts. This problem is decomposed into three different problems. The first problem is about obtaining a model. The second one is how to take criteria into account and the third one is about solving a scheduling problem. An extension to an existing notation for scheduling problems is proposed for multicriteria scheduling problems. Then, basic results from the literature on multicriteria optimization are presented. These results are used to build the final scheduling problem to solve. Finally a survey is presented for one-machine, parallel machines and flowshop multicriteria scheduling problems.

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Multicriteria scheduling problems: a survey

This book gives the reader an insight into the state of the art in the field of multiobjective (linear, nonlinear and combinatorial) programming, goal programming and multiobjective metaheuristics. The 26 papers describe all relevant trends in this fields of research . They cover a wide range of topics ranging from theoretical investigations to algorithms, dealing with uncertainty, and applications to real world problems such as engineering design, water distribution systems and portfolio selection. The book is based on the papers of the seventh international conference on multiple objective programming and goal programming (MOPGP06).

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Vincent t'Kindt

Papers

Multicriteria Scheduling: Theory, Models and Algorithms

An Ant Colony Optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem

Metaheuristics for Multiobjective Optimisation

Multicriteria scheduling problems: a survey

MultiObjective Programming and Goal Programming