Book ChapterDOI
Meta-heuristics: The State of the Art
Stefan Voß
- pp 1-23
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TLDR
The state of the art of meta-heuristics for combinatorial optimization problems has been discussed in this paper, focusing on the significant progress which general frames within the meta heuristics field have implied.Abstract:
Meta-heuristics support managers in decision-making with robust tools that provide high-quality solutions to important applications in business, engineering, economics and science in reasonable time horizons. In this paper we give some insight into the state of the art of meta-heuristics. This primarily focuses on the significant progress which general frames within the meta-heuristics field have implied for solving combinatorial optimization problems, mainly those for planning and scheduling.read more
Citations
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Journal ArticleDOI
A review of optimization techniques in metal cutting processes
TL;DR: The application potential of several modelling and optimization techniques in metalcutting processes, classified under several criteria, has been critically appraised, and a generic framework for parameter optimization in metal cutting processes is suggested for the benefits of selection of an appropriate approach.
Journal ArticleDOI
Application of soft computing techniques in machining performance prediction and optimization: a literature review
TL;DR: This paper reviews the application of neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization to four machining processes—turning, milling, drilling, and grinding.
Journal ArticleDOI
Evolutionary techniques in optimizing machining parameters
TL;DR: An overview and the comparison of the latest five year researches from 2007 to 2011 that used evolutionary optimization techniques to optimize machining process parameter of both traditional and modern machining are given.
Journal ArticleDOI
Metaheuristic optimization frameworks: a survey and benchmarking
TL;DR: A significant lack of support has been found for hyper-heuristics, and parallel and distributed computing capabilities, and it is also desirable to have a wider implementation of some Software Engineering best practices.
Journal ArticleDOI
Long-term open pit mine production planning: a review of models and algorithms
TL;DR: In this paper, the authors show that there are two approaches for dealing with long-term production planning problems: (1) deterministic and (2) uncertainty-based approaches.
References
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Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.