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The Journal of Supercomputing

, Volume 72, Issue 10, pp 3764–3786 | Cite as

Recent advances in metaheuristic algorithms: Does the Makara dragon exist?

  • Simon FongEmail author
  • Xi Wang
  • Qiwen Xu
  • Raymond Wong
  • Jinan Fiaidhi
  • Sabah Mohammed
Article

Abstract

Metaheuristic algorithms (MHs) have a long history that can be traced back to genetic algorithms and evolutionary computing in the 1950s. Since February 2008, with the birth of the Firefly algorithm, MHs started to receive attention from researchers around the globe. Variants and new species of MH algorithms have bloomed like sprouts after rain. However, the necessity for creating more new species of such algorithms is questionable. It can be observed that these algorithms are fundamentally made up of several widely used core components. By explaining these components, the underlying design for a collection of the so-called modern MH optimisation algorithms is revealed. In this paper, the core components in some of the more popular MH algorithms are reviewed, thereby debunking the myths of their novelty, and perhaps dampening claims that something really ‘new’ is invented simply by branding an MH search method with the name of another living creature. Counterintuitive experimentations have shown that by taking snapshots, anyone can show some improvements of an MH over another in some situation. Mixing certain components up indeed adds advantage over the original MH. The same goes to extending MH with slight functional modification. This work also serves as a general guideline and a reference for any algorithm architect who wants to create a new MH algorithm in the future.

Keywords

Metaheuristics Search methods Swarm intelligence Algorithm design 

Abbreviations

ABC

Artificial bee colony algorithm

ACO

Ant colony optimization

AIS

Artificial immune system algorithm

BFO

Bacterial foraging algorithm

CS

Cuckoo search algorithm

DE

Differential evolution

FF

Firefly algorithm

FPA

Flower pollination algorithm

GA

Genetic algorithm

GSA

Gravitational search algorithm

GSS

Golden section search

HJ

Hooke Jeeves algorithm

HS

Harmony search algorithm

IWD

Intelligent water drops

LS

Local search algorithm

MO

Monkey optimization

MS

Memetic search algorithm

OGRs

Optimal Golomb rulers

QEA

Quantum evolutionary algorithm

RFD

River formation dynamics

SA

Simulated annealing

SFLA

Shuffled frog feaping algorithm

SOA

Seeker optimization algorithm

TS

Tree search algorithm

TSA

Tabu search algorithm

Notes

Acknowledgments

The authors are thankful for the financial supports by the Macao Science and Technology Development Fund under the EAE project (No.072/2009/A3), and MYRG2015-00128-FST, by the University of Macau and the Macau SAR government.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Simon Fong
    • 1
    Email author
  • Xi Wang
    • 1
  • Qiwen Xu
    • 1
  • Raymond Wong
    • 2
  • Jinan Fiaidhi
    • 3
  • Sabah Mohammed
    • 3
  1. 1.Department of Computer and Information ScienceUniversity of MacauMacau SARChina
  2. 2.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  3. 3.Department of Computer ScienceLakehead UniversityThunder BayCanada

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