The Late Acceptance Hill-Climbing Heuristic
Edmund K. Burke,Yuri Bykov +1 more
TLDR
The experiments have shown that the LAHC approach is simple, easy to implement and yet is an effective search procedure, and has an additional advantage (in contrast to the above cooling schedule based methods) in its scale independence.About:
This article is published in European Journal of Operational Research.The article was published on 2017-04-01 and is currently open access. It has received 177 citations till now. The article focuses on the topics: Hill climbing & Great Deluge algorithm.read more
Citations
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Recent Advances in Selection Hyper-heuristics
TL;DR: This paper focuses only on selection hyper-heuristics and presents critical discussion, current research trends and directions for future research, and the existing classification of selectionhyper- heuristics is extended, in order to reflect the nature of the challenges faced in contemporary research.
Journal ArticleDOI
Deploying Data-intensive Applications with Multiple Services Components on Edge
TL;DR: The experimental results show that the DSEGA algorithm can get the shortest response time among the service, data components and edge servers.
Journal ArticleDOI
Revisiting simulated annealing: A component-based analysis
Alberto Franzin,Thomas Stützle +1 more
TL;DR: SA is described as an ensemble of algorithmic components, and variants from the literature within these components are described, and the advantages of this proposal are shown.
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Choice function based hyper-heuristics for multi-objective optimization
TL;DR: The experimental results demonstrate the effectiveness of the hyper-heuristic approach over the WFG test suite, a common benchmark for multi-objective optimization, and an adaptive multi-method search namely (AMALGAM).
Journal ArticleDOI
Late Acceptance Hill Climbing Based Social Ski Driver Algorithm for Feature Selection
Bitanu Chatterjee,Trinav Bhattacharyya,Kushal Kanti Ghosh,Pawan Kumar Singh,Zong Woo Geem,Ram Sarkar +5 more
TL;DR: Binary variant of a recently proposed meta-heuristic algorithm called Social Ski Driver (SSD) optimization is introduced, which can be used as a pre-processing tool to reduce dimensionality by eliminating irrelevant or redundant features to be used for a machine learning or data mining algorithm.
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.
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Future paths for integer programming and links to artificial intelligence
TL;DR: Four key areas of Integer programming are examined from a framework that links the perspectives of artificial intelligence and operations research, and each has characteristics that appear usefully relevant to developments on the horizon.
Journal ArticleDOI
An Effective Heuristic Algorithm for the Traveling-Salesman Problem
S. Lin,Brian W. Kernighan +1 more
TL;DR: This paper discusses a highly effective heuristic procedure for generating optimum and near-optimum solutions for the symmetric traveling-salesman problem based on a general approach to heuristics that is believed to have wide applicability in combinatorial optimization problems.
Book
The Traveling Salesman Problem: A Computational Study
TL;DR: Open Library features a library with books from the Internet Archive and lists them in the open library and gives you access to over 1 million free e-Books and the ability to search using subject, title and author.
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Optimization by simulated annealing: an experimental evaluation. Part I, graph partitioning
TL;DR: This paper discusses annealing and its parameterized generic implementation, describes how this generic algorithm was adapted to the graph partitioning problem, and reports how well it compared to standard algorithms like the Kernighan-Lin algorithm.