Journal ArticleDOI
Comparison of genetic algorithms, random restart and two-opt switching for solving large location-allocation problems
TLDR
This paper examines the application of a genetic algorithm used in conjunction with a local improvement procedure for solving the location-allocation problem, a traditional multifacility location problem, and demonstrated that the genetic algorithm provides the best solutions.About:
This article is published in Computers & Operations Research.The article was published on 1996-06-01. It has received 179 citations till now. The article focuses on the topics: Genetic algorithm & Heuristic (computer science).read more
Citations
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A Genetic Algorithm for Function Optimization: A Matlab Implementation
TL;DR: The genetic algorithm using a oat representation is found to be superior to both a binary genetic algorithm and simulated annealing in terms of e ciency and quality of solution.
Journal ArticleDOI
Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems
TL;DR: In this paper, a hybrid harmony search algorithm (HHSA) is proposed to solve engineering optimization problems with continuous design variables, where sequential quadratic programming (SQP) is employed to speed up local search and improve precision of the HSA solutions.
Journal ArticleDOI
Improvement and Comparison of Heuristics for Solving the Uncapacitated Multisource Weber Problem
TL;DR: It is found that most traditional and some recent heuristics give poor results when the number of facilities to locate is large and that Variable Neighbourhood search gives consistently best results, on average, in moderate computing time.
Journal ArticleDOI
A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms
TL;DR: The proposed method is the combination of a fuzzy multi-objective programming and a genetic algorithm for the optimization of fire station locations, which has three distinguish features: considering fuzzy nature of a decision maker (DM) in the location optimization model, being more understandable and practical to DM.
Journal ArticleDOI
Exploring multiple viewshed analysis using terrain features and optimisation techniques
TL;DR: Two strategies for tackling the calculation of viewsheds are explored, one to restrict the search to key topographic points in the landscape such as peaks, pits and passes and the other to use heuristics which have been applied to other maximal coverage spatial problems such as location-allocation.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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.
Book
Simulation Modeling and Analysis
Averill M. Law,W. David Kelton +1 more
TL;DR: The text is designed for a one-term or two-quarter course in simulation offered in departments of industrial engineering, business, computer science and operations research.