Variable neighborhood search

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Company LOGO Scientific Research Group in Egypt (SRGE) Meta-heuristics techniques (III) Variable neighborhood search Dr. Ahmed Fouad Ali Suez Canal University, Dept. of Computer Science, Faculty of Computers and informatics Member of the Scientific Research Group in Egypt

Transcript of Variable neighborhood search

CompanyLOGO

Scientific Research Group in Egypt (SRGE) Meta-heuristics techniques

(III)Variable neighborhood searchDr. Ahmed Fouad Ali

Suez Canal University, Dept. of Computer Science, Faculty of Computers and informatics

Member of the Scientific Research Group in Egypt

CompanyLOGO Scientific Research Group

in Egyptwww.egyptscience.net

CompanyLOGO Meta-heuristics

techniques

CompanyLOGO Outline

2. Variable neighborhood search(VNS)(Background)

3. VNS (main concepts)

5. VNS applications

4. VNS algorithm

1. Motivation

CompanyLOGO Motivation

startingpoint

descenddirection

local minima

global minima

barrier to local search?

CompanyLOGO Variable neighborhood search (VNS)

(Background)• Variable neighborhood search (VNS) has been proposed by P. Hansen and N. Mladenovic in 1997.

•The basic idea of VNS is to successively explore a set of predefined neighborhoods to provide a better solution.

•It explores either at random or

systematically a set of neighborhoods to get different local optima and to escape from local optima.

CompanyLOGO VNS (main concepts)

•VNS is a stochastic algorithm in which, first, a set of neighborhood structures Nk (k = 1, . . . , n) are defined.

•Then, each iteration of the algorithm is composed of three steps: shaking, local search, and move.

•VNS explores a set of neighborhoods to get different local optima and escape from local optima.

CompanyLOGO VNS (main concepts)

NeighborhoodN1

NeighborhoodN2

NeighborhoodNmax

Initial solution

Shaking

MovingNon improving

neighbor

Movingimproving neighbor

CompanyLOGO VNS algorithm

CompanyLOGO VNS algorithm•A set of neighborhood structure Nk are defined where k = 1, 2,…, n.

•At each iteration, an initial solution x is generated randomly.

•A random neighbor solution x' is generated in the current neighborhood Nk.

•The local search procedure is applied to the solution x' to generate the solution x".

Shaking

Local search

CompanyLOGO VNS algorithm•If the solution x" is better than the x solution then the solution x" becomes the new current solution and the search starts from the current solution.

•If the solution x" is not better than x solution, the search moves to the next neighborhood Nk+1, generates a new solution in this neighborhood and try to improve it.

•These operations are repeated until a termination criteria satisfied.

Moving

CompanyLOGO SA ApplicationsScheduling Quadratic

assignment

Frequency assignmen

t

Car pooling Capacitated p-

median,

Resource constrained

project scheduling (RCPSP)

Vehicle routing problems

Graph coloring

Retrieval Layout Problem

Maximum Clique Problem,

Traveling Salesman Problems

Database systems

Nurse Rostering Problem

Neural Nets Grammatical

inference,

Knapsack problems

SAT Constrain

t Satisfact

ion Problems

Network design

Telecomunication Network

Global Optimizati

on

CompanyLOGO References Metaheuristics From design to implementation, El-Ghazali Talbi, University of Lille – CNRS – INRIA.

M. Mladenovic and P. Hansen, Variable neighborhood search. Computers and Operations Research, 24:(1997), 1097-1100, .

CompanyLOGO

Thank you

http://www.egyptscience.net

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