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
Neural Computing and Applications
Published in: Neural Computing and Applications 8/2019

12-01-2018 | Original Article

A multi-objective artificial sheep algorithm

Authors: Xinjie Lai, Chaoshun Li, Nan Zhang, Jianzhong Zhou

Published in: Neural Computing and Applications | Issue 8/2019

Log in

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

search-config
loading …

Abstract

In this paper, a novel multi-objective artificial sheep algorithm (MOASA) is proposed. The basic search idea of MOASA inherits from the BASA, which is inspired by the social behavior of sheep herd, while some modifications are made to extend the algorithm to multi-objective problems. The Pareto-based theory is adopted in the MOASA along with external archive and leader selection mechanism to bring about multi-objective optimization. Furthermore, a novel neighborhood search method is proposed and applied to the external archive to enhance the performance of the algorithm. The proposed MOASA is then tested on 17 multi-objective benchmark problems to verify its efficiency and effectiveness by comparing with six powerful multi-objective optimization algorithms (MOAs). Experimental results show that the MOASA is generally superior to its competitors in solving those benchmark problems in terms of convergence and Pareto front distribution.
previous article Asymptotic and finite-time synchronization of memristor-based switching networks with multi-links and impulsive perturbation
next article Numerical solutions of fuzzy fractional diffusion equations by an implicit finite difference scheme

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

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
Appendix
Available only for authorised users
Literature
1.
Fonseca CM, Fleming PJ (1995) An overview of evolutionary algorithms in multi-objective optimization. Evol Comput 3(1):1–16CrossRef
2.
Coello CAC (1999) A comprehensive survey of evolutionary-based multi-objective optimization techniques. Knowl Inf Syst 1(3):269–308CrossRef
3.
Deb K, Kalyanmoy D (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New YorkMATH
4.
Sawaragi Y, Nakayama H, Tanino T (2010) Theory of multi-objective optimization. Academic Press, LondonMATH
5.
Cass D (1965) Optimum growth in an aggregative model of capital accumulation. Rev Econ Stud 32(32):233–240CrossRef
6.
Sun CCI (1998) Use of vehicle signature analysis and lexicographic optimization for vehicle reidentification on freeways, vol 7. University of California, Irvine, pp 167–185
7.
Aghaei J, Amjady N, Shayanfar HA (2011) Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method. Appl Soft Comput 11(4):3846–3858CrossRef
8.
Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q (2011) Multi-objective evolutionary algorithms: a survey of the state-of-the-art. J Swarm Evol Comput 1(1):32–49CrossRef
9.
Guliashki V, Toshev H, Korsemov C (2009) Survey of evolutionary algorithms used in multi-objective optimization. J Probl Eng Cybern Robot 60:42–54
10.
Sierra MR, Coello CAC (2006) Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intell Res 2(3):287–308MathSciNet
11.
Coello CAC, Pulido G, Lechuga M (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279CrossRef
12.
Hassanzadeh H, Rouhani M (2010) A multi-objective gravitational search algorithm. In: International conference on computational intelligence, communication systems and networks. Liverpool, pp 7–12
13.
Akbari R, Hedayatzadeh R, Ziarati K, Hassanizadeh B (2012) A multi-objective artificial bee colony algorithm. Swarm Evol Comput 1:39–52CrossRef
14.
Mirjalili S, Saremi S, Mirjalili S (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119CrossRef
15.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, pp 1942–1948
16.
Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRef
17.
Jiang Q, Wang L, Hei X, Yu XG, Lin Y, Lu X (2016) MOEA/D-ASA + SBX: a new multi-objective evolutionary algorithm based on decomposition with artificial raindrop algorithm and simulated binary crossover. Knowl Based Syst 107:197–218CrossRef
18.
Cai D, Yuping W (2015) A new uniform evolutionary algorithm based on decomposition and CDAS for many-objective optimization. Knowl Based Syst 85(1):131–142CrossRef
19.
Sun G, Zhang A, Jia X (2016) DMMOGSA: diversity-enhanced and memory-based multi-objective gravitational search algorithm. Inf Sci 363:52–71CrossRef
20.
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: nSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRef
21.
Srinivas N, Deb K (1995) Multi-objective function optimization using non-dominated sorting genetic algorithms. Evol Comput 2(3):221–248CrossRef
22.
Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3(4):257–271CrossRef
23.
Zhang Q, Li H (2007) MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731CrossRef
24.
Glover F, Kochenberger GA (2010) Handbook of metaheuristics. Springer, BerlinMATH
25.
Jones DF, Mirrazavi SK, Tamiz M (2002) Multi-objective meta-heuristics: an overview of the current state-of-the-art. Eur J Oper Res 137(1):1–9MATHCrossRef
26.
Talbi EG (2009) Metaheuristics: from design to implementation, In Proc. SPIE Int Soc Opt Eng 42(4):497–541
27.
Coello CAC, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the Congress on evolutionary computation (CEC’2002), vol 1, pp 1051–1056
28.
Mousa AA, El-Shorbagy MA, Abd-El-Wahed WF (2012) Local search based hybrid particle swarm optimization algorithm for multi-objective optimization. Swarm Evol Comput 3:1–14CrossRef
29.
Benameur L, Alami J, Imrani AHE (2009) A new hybrid particle swarm optimization algorithm for handling multi-objective problem using fuzzy clustering technique. In: International conference on computational intelligence, modelling and simulation. IEEE, pp 48–53
30.
Sahoo NC, Ganguly S, Das D (2011) Simple heuristics-based selection of local guides for multi-objective PSO with an application to electrical distribution system planning. Eng Appl Artif Intell Elsevier 24(4):567–585CrossRef
31.
Ganguly S, Sahoo NC, Das D (2013) Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation. Fuzzy Sets Syst Elsevier 213:47–73MathSciNetCrossRef
32.
Hassanzadeh HR, Rouhani M (2010) A multi-objective gravitational search algorithm. Int Conf Comput Intell 12:7–12
33.
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2012) GSA: a gravitational search algorithm. Intell Inf Manag 4(6):390–395MATH
34.
Alaya I, Solnon C, Ghedira K (2017) Ant colony optimization for multi-objective optimization problems. In: IEEE international conference on TOOLS with artificial intelligence, pp 450–457
35.
Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073CrossRef
36.
Scharfstein DS, Stein JC (1990) Herd behavior and investment. Am Econ Rev 80(3):465–479
37.
Bonabeau E (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, OxfordMATH
38.
Tarasewich P, Mcmullen PR (2002) Swarm intelligence. Commun ACM 45(8):62–67CrossRef
39.
Wang W, Li C, Liao X, Qin H (2017) Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm. Appl Energy 187:612–626CrossRef
40.
Zhang Q, Zhou A, Zhao S, Suganthan PN, Liu W, Tiwari S (2009) Multi-objective optimization test instances for the CEC 2009 special session and competition, University of Essex, 2009
41.
Qu BY, Suganthan PN (2011) Constrained multi-objective optimization algorithm with ensemble of constraint handling methods. Eng Optim 43(4):403–416MathSciNetCrossRef
42.
Van Veldhuizen DA, Lamont GB (1998) Multi-objective evolutionary algorithm research: a history and analysis. Evol Comput 8(2):125–147CrossRef
43.
Sierra MR, Coello CAC (2005) Improving PSO-based multi-objective optimization using crowding, mutation and ∈-dominance. In: Evolutionary multi-criterion optimization. Springer, Berlin
44.
Su YX, Chi R (2017) Multi-objective particle swarm-differential evolution algorithm. Neural Comput Appl 28(2):1–12CrossRef
45.
46.
Basu M (2004) An interactive fuzzy satisfying method based on evolutionary programming technique for multi-objective short-term hydrothermal scheduling. Electr Power Syst Res 69(2–3):277–285CrossRef
Metadata
Title
A multi-objective artificial sheep algorithm
Authors
Xinjie Lai
Chaoshun Li
Nan Zhang
Jianzhong Zhou
Publication date
12-01-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3348-x

Other articles of this Issue 8/2019

Neural Computing and Applications 8/2019 Go to the issue

Original Article

Face recognition using AMVP and WSRC under variable illumination and pose

Original Article

Polynomial Kernel Discriminant Analysis for 2D visualization of classification problems

Original Article

Automatic clustering and feature selection using gravitational search algorithm and its application to microarray data analysis

Original Article

Multi-objective particle swarm optimization algorithm using adaptive archive grid for numerical association rule mining

Original Article

Predicting power production from a photovoltaic panel through artificial neural networks using atmospheric indicators

Original Article

A novel VIKOR method with an application to multiple criteria decision analysis for hospital-based post-acute care within a highly complex uncertain environment

Premium Partner

    Image Credits