Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Soft Computing
Published in: Soft Computing 16/2019

10-07-2018 | Methodologies and Application

Biology migration algorithm: a new nature-inspired heuristic methodology for global optimization

Authors: Qingyang Zhang, Ronggui Wang, Juan Yang, Andrew Lewis, Francisco Chiclana, Shengxiang Yang

Published in: Soft Computing | Issue 16/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this paper, inspired by the biology migration phenomenon, which is ubiquitous in the social evolution process in nature, a new meta-heuristic optimization paradigm called biology migration algorithm (BMA) is proposed. This optimizer consists of two phases, i.e., migration phase and updating phase. The first phase mainly simulates how the species move to new habits. During this phase, each agent should obey two main rules depicted by two random operators. The second phase mimics how some species leave the group and new ones join the group during the migration process. In this phase, a maximum number of iterations will be set to predetermine whether a current individual should leave and be replaced by a new one. Simulation results based on a comprehensive set of benchmark functions and four real engineering problems indicate that BMA is effective in comparison with other existing optimization methods.
previous article Uncertainty quantification with hybrid -cut
next article Chinese and windy postman problem with variable service costs

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Footnotes
Literature
Aidley DJ (1981) Animal migration. Cambridge University Press, Cambridge
Alcala-Fdez J et al (2009) KEEL: a software tool to assess evolutionary algorithms to data mining problems. Soft Comput 13(3):307–318CrossRef
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12CrossRef
Baruah RD, Angelov P (2014) DEC: dynamically evolving clustering and its application to structure identification of evolving fuzzy models. IEEE Trans Cybern 44(9):1619–1631CrossRef
Belegundu AD (1983) Study of mathematical programming methods for structural optimization. Dissertation abstracts international B the sciences and engineering
Bernardino H, Barbosa I, Lemonge A (2007) A hybrid genetic algorithm for constrained optimization in mechanical engineering. In: Proceedings of IEEE congress on evolutionary computation, pp 646–653
Braha D (2012) Global civil unrest: contagion, self-organization, and prediction. Plos one 7(10):1–9
Chang PC, Chen SS, Zhang QF (2008) MOEA/D for flowshop scheduling problems. In: Proceeding of congress of evolutionary computation 2008 (CEC 2008), Hong Kong
Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139(15):98–112CrossRef
Coelho LDS (2010) Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37(2):1676–1683CrossRef
Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127CrossRef
Coello Coello CA (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287MathSciNetMATHCrossRef
Coello Coello CA, Mezura Montes E (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inf 16(3):193–203CrossRef
Cuevas E, Echavarria A, Ramrez-Ortegon MA (2014) An optimziation algorithm inspired by the states of matter that improves the balance between exploration and exploitation. Appl Intell 40(2):256–272CrossRef
Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18CrossRef
Duan HB, Li P (2014) Bio-inspired computation in unmanned aerial vehicles. Springer, BerlinCrossRef
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, pp 39–43
Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166CrossRef
Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183CrossRef
Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845MathSciNetMATHCrossRef
Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35CrossRef
Gandomi A, Yang X-S, Alavi A, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255CrossRef
Hansen N, Muller SD, Kounoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1–18CrossRef
He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99CrossRef
Hsieh TJ (2014) A bacterial gene recombination algorithm for solving constrained optimization problems. Appl Math Comput 231:187–204MathSciNetMATH
Huang FZ, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356MathSciNetMATH
Jiang QY, Wang L, Hei XH (2015) Parameter identification of chaotic systems using artificial raindrop algorithm. J Comput Sci 8:20–31MathSciNetCrossRef
Kannan B, Kramer SN (1994) An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Des 116(2):405–411CrossRef
Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294CrossRef
Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput 27(1):155–182MATHCrossRef
Kayabekir AE, Bekdas G, Nigdeli SM, Yang XS (2018) A comprehensive review of the flower pollination algorithm for solving engineering problems. In: Nature-inspired algorithms and applied optimization. Springer, Cham
Li XT, Zhang J, Yin MH (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877CrossRef
Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algrithm for optimization: virus colony search. Adv Eng Softw 92:65–88CrossRef
Liang JJ, Qin AK, Suganthan PH, Basker S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295CrossRef
Liang J, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC2014 special session and competition on single objective real-parameter numerical optimization. In: Technical report, pp 1–32
Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579MathSciNetMATH
Milner-Gulland EJ, Fryxell JM, Sinclair ARE (2011) Animal migration: a synthesis. Oxford University Press, OxfordCrossRef
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249CrossRef
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRef
Precup RE, Sabau MC, Petriu EM (2015) Nature-inspired optimal tuning of input membership functions of Takagi–Sugeno–Kang fuzzy models for anti-lock braking systems. Appl Soft Comput 27:575–589CrossRef
Ragsdell K, Phillips D (1976) Optimal design of a class of welded structures using geometric programming. J Eng Ind 98(3):1021–1025CrossRef
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315CrossRef
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248MATHCrossRef
Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612CrossRef
Sandgren E (1988) Nonlinear integer and discrete programming in mechanical design, pp 95–105
Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimization algorithm: theory and application. Adv Eng Softw 105:30–47CrossRef
Serdio F, Lughofer E, Zavoianu AC, Pichler K, Pichler M, Buchegger T, Efendic H (2017) Improved fault detection employing hybrid memetic fuzzy modeling and adaptive filters. Appl Soft Comput 51:60–82CrossRef
Siddall JN (1972) Analytical decision-making in engineering design. Prentice-Hall, Englewood Cliffs
Simon D (2008) Biogeograph-based optimization. IEEE Trans Evol Comput 12(6):702–713CrossRef
Stefanescu C et al (2012) Multi-generational long-distance migration of insects: studying the painted lady butterfly in the western Palaearctic. Ecography 36(4):474–486CrossRef
Vrkalovic S, Teban TA, Borlea ID (2017) Stable Takagi–Sugeno fuzzy control designed by optimization. Int J Artif Intell 15(2):17–29
Wang Y, Cai ZX, Zhang QF (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66CrossRef
Wesche T, Goertler G, Hubert W (1987) Modified habitat suitability index model for brown trout in southeastern Wyoming. J Fish Manage 7:232–237CrossRef
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRef
Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NISCO 2010), studies in computational intelligence, vol 284. Berlin, pp 65–74
Yang X-S (2010) Nature-inspired meta-heuristic algorithms, 2nd edn. Luniver Press, Beckington
Yang X-S, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Numer Optim 1(4):330–343MATH
Yang SY, Yao X (2013) Evolutionary computation for dynamic optimization problems. Springer, HeidelbergMATHCrossRef
Zhang ZH, Zhang J, Li Y, Shi YH (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847CrossRef
Metadata
Title
Biology migration algorithm: a new nature-inspired heuristic methodology for global optimization
Authors
Qingyang Zhang
Ronggui Wang
Juan Yang
Andrew Lewis
Francisco Chiclana
Shengxiang Yang
Publication date
10-07-2018
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 16/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3381-9

Other articles of this Issue 16/2019

Soft Computing 16/2019 Go to the issue

Foundations

A multi-recombinative active matrix adaptation evolution strategy for constrained optimization

Methodologies and Application

Solving interval linear programming problems with equality constraints using extended interval enclosure solutions

Methodologies and Application

A binary boosting method in the presence of outliers in training data

Methodologies and Application

An efficient ID-based cryptographic transformation model for extended chaotic-map-based cryptosystem

Methodologies and Application

Two regression methods for hesitant multiplicative preference relations with different consistencies

Methodologies and Application

Uncertainty quantification with hybrid -cut

Premium Partner

    Image Credits