chapter

Scatter search

Authors:

Rafael MartíAuthors Info & Claims

Advances in evolutionary computing: theory and applications

January 2003

Pages 519 - 537

Published: 01 January 2003 Publication History

Abstract

The evolutionary approach called scatter search originated from strategies for creating composite decision rules and surrogate constraints. Recent studies demonstrate the practical advantages of this approach for solving a diverse array of optimisation problems from both classical and real--world settings. Scatter search contrasts with other evolutionary procedures, such as genetic algorithms, by providing unifying principles for joining solutions based on generalised path constructions in Euclidean space and by utilising strategic designs where other approaches resort to randomisation. Additional advantages are provided by intensification and diversification mechanisms that exploit adaptive memory, drawing on foundations that link scatter search to tabu search. The main goal of this chapter is to demonstrate the development of a scatter search procedure by demonstrating how it may be applied to a class of non-linear optimisation problems on bounded variables. We conclude the chapter by highlighting key ideas and research issues that offer the promise of yielding future advances.

References

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Digital Library

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Digital Library

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Digital Library

Cited By

Kar MKar SGuo SLi XMajumder S(2019)A new bi-objective fuzzy portfolio selection model and its solution through evolutionary algorithmsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3094-023:12(4367-4381)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00500-018-3094-0
Hedar AIbrahim AAbdel-Hakim ASewisy ATakadama K(2018)Modulated clustering using integrated rough sets and scatter search attribute reductionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208286(1394-1401)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208286
Diao YShwartz L(2017)Building Automated Data Driven Systems for IT Service ManagementJournal of Network and Systems Management10.1007/s10922-017-9430-325:4(848-883)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s10922-017-9430-3
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Index Terms

Scatter search

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Published In

cover image Guide books

Advances in evolutionary computing: theory and applications

January 2003

1028 pages

ISBN:3540433309

Editors:
Ashish Ghosh
Indian Statistical Institute, Machine Intelligence Unit, 203 B.T. Road, Kolkata 700 035, India
,
Shigeyoshi Tsutsui
Hannan University, Department of Management Information, 5-4-33 Amamhigigashi, Matsubara, Osaka 580-8502, Japan

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2003

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Cited By

Kar MKar SGuo SLi XMajumder S(2019)A new bi-objective fuzzy portfolio selection model and its solution through evolutionary algorithmsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3094-023:12(4367-4381)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00500-018-3094-0
Hedar AIbrahim AAbdel-Hakim ASewisy ATakadama K(2018)Modulated clustering using integrated rough sets and scatter search attribute reductionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208286(1394-1401)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208286
Diao YShwartz L(2017)Building Automated Data Driven Systems for IT Service ManagementJournal of Network and Systems Management10.1007/s10922-017-9430-325:4(848-883)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s10922-017-9430-3
Mesejo PIbáñez ÓCordón ÓCagnoni S(2016)A survey on image segmentation using metaheuristic-based deformable modelsApplied Soft Computing10.1016/j.asoc.2016.03.00444:C(1-29)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.03.004
González MVela CVarela RGonzález-Rodríguez I(2015)An advanced scatter search algorithm for solving job shops with sequence dependent and non-anticipatory setupsAI Communications10.5555/2733572.273357528:2(179-193)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.5555/2733572.2733575
González MOddi ARasconi RVarela R(2015)Scatter search with path relinking for the job shop with time lags and setup timesComputers and Operations Research10.1016/j.cor.2015.02.00560:C(37-54)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1016/j.cor.2015.02.005
Dao TZelinka IDuy V(2013)Use of Simulated Annealing for Adaptive Control SystemInternational Journal of Energy Optimization and Engineering10.4018/ijeoe.20130701032:3(42-54)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.4018/ijeoe.2013070103
Nashed YUgolotti RMesejo PCagnoni SMoore J(2012)libCudaOptimizeProceedings of the 14th annual conference companion on Genetic and evolutionary computation10.1145/2330784.2330803(117-124)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330784.2330803
Sun JGaribaldi JHodgman C(2012)Parameter Estimation Using Metaheuristics in Systems BiologyIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2011.639:1(185-202)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1109/TCBB.2011.63
Zhang TChaovalitwongse WZhang Y(2012)Scatter search for the stochastic travel-time vehicle routing problem with simultaneous pick-ups and deliveriesComputers and Operations Research10.1016/j.cor.2011.11.02139:10(2277-2290)Online publication date: 1-Oct-2012
https://dl.acm.org/doi/10.1016/j.cor.2011.11.021
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