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What is Local Search

Chapter 3
Handbook of Research on Nature-Inspired Computing for Economics and Management
Local search methods are usually deterministic. They can find a local optimum very quickly, but will get stuck there.
Published in Chapter:
Stochastic Optimization Algorithms
P. Collet (Université du Littoral Côte d’Opale, France) and J. Rennard (Grenoble Graduate School of Business, France)
Copyright: © 2007 |Pages: 17
DOI: 10.4018/978-1-59140-984-7.ch003
Abstract
When looking for a solution, deterministic methods have the enormous advantage that they do find global optima. Unfortunately, they are very CPU intensive, and are useless on untractable NP-hard problems that would require thousands of years for cutting-edge computers to explore. In order to get a result, one needs to revert to stochastic algorithms that sample the search space without exploring it thoroughly. Such algorithms can find very good results, without any guarantee that the global optimum has been reached; but there is often no other choice than using them. This chapter is a short introduction to the main methods used in stochastic optimization.
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More Results
Chapter 174
Nelder-Mead Evolutionary Hybrid Algorithms
A search algorithm to carry out exploitation.
Published in Chapter: Nelder-Mead Evolutionary Hybrid Algorithms; From: Encyclopedia of Artificial Intelligence
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Chapter 91
Evolutionary Grammatical Inference
A type of search method that starts at some point in search space and iteratively moves from position to neighbouring position using heuristics.
Published in Chapter: Evolutionary Grammatical Inference; From: Encyclopedia of Artificial Intelligence
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Chapter 13
A Memetic Algorithm for the Multi-Depot Vehicle Routing Problem with Limited Stocks
Given a set of solutions S , every solution has an associated set of neighbors, expressed by , each of them is called the neighborhood of s . All the neighborhood of s , namely , can be reached directly from s by one move (or transition) to . Local search is performed in such a way to find a local optimal solution. It should be noted that this search is achieved by defining a neighborhood structure.
Published in Chapter: A Memetic Algorithm for the Multi-Depot Vehicle Routing Problem with Limited Stocks; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 4
Nature Inspired Methods for Multi-Objective Optimization
A search algorithm to carry out exploitation. Pure local search algorithms are prone to getting trapped in local optima, without converging to good solutions.
Published in Chapter: Nature Inspired Methods for Multi-Objective Optimization; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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Chapter 403
Experimental Analysis with Variable Neighborhood Search for Discrete Optimization Problems
Method that starts with an initial solution then applies a sequence of local changes in attempt to improve the value of the objective function and obtain the local optima.
Published in Chapter: Experimental Analysis with Variable Neighborhood Search for Discrete Optimization Problems; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 53
A Comparison of Cooling Schedules for Simulated Annealing
Local search (LS) is a metaheuristic or general class of approximate optimization algorithms, based on the deterministic heuristic search technique called hill-climbing
Published in Chapter: A Comparison of Cooling Schedules for Simulated Annealing; From: Encyclopedia of Artificial Intelligence
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