### Abstract

Swarm intelligence algorithms have been succesfully applied to hard optimization problems. Seeker optimization algorithm is one of the latest members of that class of metaheuristics and it has not yet been thorougly researched. Since the early versions of this algorithm were less succesful with multimodal functions, we propose in this paper hybridization of the seeker optimization algorithm with the well known artificial bee colony (ABC) algorithm. At certain stages we modify seeker's position by search formulas from the ABC algorithm and also modify the inter-subpopulation learning phase by using the binomial crossover operator. Our proposed algorithm was tested on the complete set of 23 well-known benchmark functions. Comparisons show that our proposed algorithm outperforms six state-of-the-art algorithms in terms of the quality of the resulting solutions as well as robustenss on most of the test functions.

Original language | English |
---|---|

Pages (from-to) | 867-875 |

Number of pages | 9 |

Journal | Applied Mathematics and Information Sciences |

Volume | 7 |

Issue number | 3 |

Publication status | Published - 1 May 2013 |

Externally published | Yes |

### Fingerprint

### Keywords

- Artificial bee colony
- Nature inspired algorithms.
- Seeker optimization algorithm
- Swarm intelligence
- Unconstrained optimization metaheuristics

### ASJC Scopus subject areas

- Applied Mathematics
- Numerical Analysis
- Analysis
- Computer Science Applications
- Computational Theory and Mathematics

### Cite this

*Applied Mathematics and Information Sciences*,

*7*(3), 867-875.

**Hybrid seeker optimization algorithm for global optimization.** / Tuba, Milan; Brajevic, Ivona; Jovanovic, Raka.

Research output: Contribution to journal › Article

*Applied Mathematics and Information Sciences*, vol. 7, no. 3, pp. 867-875.

}

TY - JOUR

T1 - Hybrid seeker optimization algorithm for global optimization

AU - Tuba, Milan

AU - Brajevic, Ivona

AU - Jovanovic, Raka

PY - 2013/5/1

Y1 - 2013/5/1

N2 - Swarm intelligence algorithms have been succesfully applied to hard optimization problems. Seeker optimization algorithm is one of the latest members of that class of metaheuristics and it has not yet been thorougly researched. Since the early versions of this algorithm were less succesful with multimodal functions, we propose in this paper hybridization of the seeker optimization algorithm with the well known artificial bee colony (ABC) algorithm. At certain stages we modify seeker's position by search formulas from the ABC algorithm and also modify the inter-subpopulation learning phase by using the binomial crossover operator. Our proposed algorithm was tested on the complete set of 23 well-known benchmark functions. Comparisons show that our proposed algorithm outperforms six state-of-the-art algorithms in terms of the quality of the resulting solutions as well as robustenss on most of the test functions.

AB - Swarm intelligence algorithms have been succesfully applied to hard optimization problems. Seeker optimization algorithm is one of the latest members of that class of metaheuristics and it has not yet been thorougly researched. Since the early versions of this algorithm were less succesful with multimodal functions, we propose in this paper hybridization of the seeker optimization algorithm with the well known artificial bee colony (ABC) algorithm. At certain stages we modify seeker's position by search formulas from the ABC algorithm and also modify the inter-subpopulation learning phase by using the binomial crossover operator. Our proposed algorithm was tested on the complete set of 23 well-known benchmark functions. Comparisons show that our proposed algorithm outperforms six state-of-the-art algorithms in terms of the quality of the resulting solutions as well as robustenss on most of the test functions.

KW - Artificial bee colony

KW - Nature inspired algorithms.

KW - Seeker optimization algorithm

KW - Swarm intelligence

KW - Unconstrained optimization metaheuristics

UR - http://www.scopus.com/inward/record.url?scp=84873441660&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84873441660&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84873441660

VL - 7

SP - 867

EP - 875

JO - Applied Mathematics and Information Sciences

JF - Applied Mathematics and Information Sciences

SN - 1935-0090

IS - 3

ER -