Skip to main content
Springer Nature Link
Log in

Novel meta-heuristic bald eagle search optimisation algorithm

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

This study proposes a bald eagle search (BES) algorithm, which is a novel, nature-inspired meta-heuristic optimisation algorithm that mimics the hunting strategy or intelligent social behaviour of bald eagles as they search for fish. Hunting by BES is divided into three stages. In the first stage (selecting space), an eagle selects the space with the most number of prey. In the second stage (searching in space), the eagle moves inside the selected space to search for prey. In the third stage (swooping), the eagle swings from the best position identified in the second stage and determines the best point to hunt. Swooping starts from the best point and all other movements are directed towards this point. BES is tested by adopting a three-part evaluation methodology that (1) describes the benchmarking of the optimisation problem to evaluate the algorithm performance, (2) compares the algorithm performance with that of other intelligent computation techniques and parameter settings and (3) evaluates the algorithm based on mean, standard deviation, best point and Wilcoxon signed-rank test statistic of the function values. Optimisation results and discussion confirm that the BES algorithm competes well with advanced meta-heuristic algorithms and conventional methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Barhen J, Protopopescu V, Reister D (1997) Trust: a deterministic algorithm for global optimization. Science 276:1094–1097

    Article  MathSciNet  Google Scholar 

  • Birge B (2003) PSOt—a particle swarm optimization toolbox for use with MATLAB. In: Proceedings of 2003 IEEE swarm intelligence symposium, pp 182–186

  • Brest J, Greiner S, Boškovic B, Mernik M, Žumer V (2006) Self- adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657

    Article  Google Scholar 

  • Del Valle Y et al (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12(2):171–195

    Article  Google Scholar 

  • Derrac J et al (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):3–18

    Article  Google Scholar 

  • Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Magaz 1:28–39

    Article  Google Scholar 

  • Fiacco AV, McCormick GP (1968) Nonlinear programming: sequential unconstrained minimization techniques. Wiley, New York

    MATH  Google Scholar 

  • Fogel DB (1995) Evolutionary computation: toward a new philosophy of machine intelligence. IEEE Press, New York

    MATH  Google Scholar 

  • Fogel D (2009) Artificial intelligence through simulated evolution. Wiley, Hoboken

    Book  Google Scholar 

  • Fogel LJ, Owens AJ, Walsh MJ (1965) Artificial intelligence through a simulation of evolution. In: Proceedings of 2nd cybernetics science symposium on biophysics cybernetics systems. Spartan Books, Washington, pp 131–155

  • Hansen AJ (1986) Fighting behavior in bald eagles: a test of game theory ecological society of America. Ecology 67(3):787–797

    Article  Google Scholar 

  • Hansen AJ, Boeker EL, Hodges JI, Cline DR (1984) Bald eagles of the Chilkat Valley, Alaska: ecology, behavior, and management. National Audubon Society and U.S. Fish & Wildlife, Service, Juneau

    Google Scholar 

  • Hatamlou A (2012) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175

    Article  MathSciNet  Google Scholar 

  • He S, Wu QH, Saunders JR (2006) A novel group search optimizer inspired by animal behavioural ecology. In: IEEE congress on evolutionary computation, 2006. CEC 2006. IEEE

  • He S, Wu QH, Saunders JR (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13(5):973–990

    Article  Google Scholar 

  • Holland JH (1992) Genetic algorithms. Sci Am 267:66–72

    Article  Google Scholar 

  • Houck C, Joines J, Kay M (1995) A genetic algorithm for function optimization: a MATLAB implementation. North Carolina State University, Raleigh, NC, Technical report NCSU-IE-TR-95-09

  • Joines J, Houck C (1994) On the use of nonstationary penalty functions to solve nonlinear constrained optimization problems with GAs. In: Proceedings of IEEE international conference on evolutionary computation. IEEE, Piscataway, NJ, pp 579–584

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    Article  MathSciNet  Google Scholar 

  • Kazarlis S, Petridis V (1998) Varying fitness functions in genetic algorithms: studying the rate of increase in the dynamic penalty terms. In: Parallel problem solving from nature. Lecture notes in computer science. Springer, Berlin, vol 1498, pp 211–220

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, pp 1942–1948

  • Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

    MATH  Google Scholar 

  • Lasserre JB (2001) Global optimization with polynomials and the problem of moments. SIAM J Optim 11(3):796–817

    Article  MathSciNet  Google Scholar 

  • Li X (2003) A new intelligent optimization-artificial fish swarm algorithm. Doctor thesis, Zhejiang University of Zhejiang, China

  • Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10:281–295

    Article  Google Scholar 

  • Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and technical report, Nanyang Technological University, Singapore

  • Lynn N, Suganthan PN (2017) Ensemble particle swarm optimizer. Appl Soft Comput 55:533–548

    Article  Google Scholar 

  • Michalewicz Z, Attia N (1994) Evolutionary optimization of constrained problems. In: Sebald AV, Fogel LJ (eds) Proceedings of the 3rd annual conference on evolutionary programming. World Scientific, River Edge, pp 98–108

  • Mirjalili S (2015a) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  • Mirjalili S (2015b) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the 2003 swarm intelligence symposium, 2003. SIS ’03. IEEE, pp 174–181

  • Qu B-Y, Liang JJ, Suganthan PN (2012) Niching particle swarm optimization with local search for multi-modal optimization. Inf Sci 197:131–143

    Article  Google Scholar 

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248

    Article  Google Scholar 

  • Rechenberg I (1994) Evolution strategy. In: Zurada M, Marks RJ, Robinson CJ (Eds) Computational intelligence: lmitating life, vol 1. IEEE Press, Piscataway, NJ, pp 147–159

  • Sameer FO, Abu Bakar MR, Zaidan AA, Zaidan BB (2019) A new algorithm of modified binary particle swarm optimization based on the Gustafson–Kessel for credit risk assessment. Neural Comput Appl 31(2):337–346

    Article  Google Scholar 

  • Schwefel H-P (1995) Evolution and optimum seeking. Wiley, New York

    MATH  Google Scholar 

  • Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of IEEE international conference on evolutionary computation, 1998, pp 69–73

  • Sörensen K (2015) Metaheuristics—the metaphor exposed. Int Trans Oper Res 22:3–18

    Article  MathSciNet  Google Scholar 

  • Stalmaster MV (1987) The bald eagle. Universe Books, New York

    Google Scholar 

  • Stalmaster MV, Gessaman JA (1982) Food consumption and energy requirements of captive Bald Eagles. J Wildl Manag 46(3):646–654

    Article  Google Scholar 

  • Stalmaster PA, Kaiser KH (1997) Winter ecology of bald eagles on the Nisqually River Drainage, Washington. Northwest Sci 71:214–223

    Google Scholar 

  • Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359

    Article  MathSciNet  Google Scholar 

  • Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report, 2005005

  • Tariq I, AlSattar HA, Zaidan AA, Zaidan BB, Abu Bakar MR, Mohammed RT, Albahri OS, Alsalem MA, Albahri AS (2018) MOGSABAT: a metaheuristic hybrid algorithm for solving multi-objective optimisation problems. Neural Comput Appl 30(2):1–15

    Google Scholar 

  • Todd CS, Young LS, Owen RB Jr, Gramlich FJ (1982) Food habits of bald eagles in Maine. J Wildl Manag 46:636–645

    Article  Google Scholar 

  • Whitley D (2001) An overview of evolutionary algorithms: practical issues and common pitfalls. Inf Softw Technol 43(14):817–831

    Article  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evolut Comput 1:67–82

    Article  Google Scholar 

  • Yao X, Liu Y (1997) Fast evolution strategies. In: Proceedings of evolutionary programming VI. Springer, Berlin, pp 151–161

  • Yao X, Liu Y, Lin G (1999a) Evolutionary programming made faster. IEEE Trans Evolut Comput 3:82–102

    Article  Google Scholar 

  • Yao X, Liu Y, Liu G (1999b) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  • Zaidan AA, Kalaf BA, Abu Bakar MR, Zaidan BB (2017) A new hybrid algorithm of simulated annealing and simplex downhill for solving multiple-objective aggregate production planning on fuzzy environment. Neural Comput Appl 28(8):1–12

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia

    H. A. Alsattar, A. A. Zaidan & B. B. Zaidan

  2. Al-Rafidain University College, Baghdad, Iraq

    H. A. Alsattar

Authors
  1. H. A. Alsattar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. A. Zaidan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. B. Zaidan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. A. Zaidan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alsattar, H.A., Zaidan, A.A. & Zaidan, B.B. Novel meta-heuristic bald eagle search optimisation algorithm. Artif Intell Rev 53, 2237–2264 (2020). https://doi.org/10.1007/s10462-019-09732-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-019-09732-5

Keywords

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy