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 |
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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
Hybrid seeker optimization algorithm for global optimization. / Tuba, Milan; Brajevic, Ivona; Jovanovic, Raka.
In: Applied Mathematics and Information Sciences, Vol. 7, No. 3, 01.05.2013, p. 867-875.Research output: Contribution to journal › Article
}
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
VL - 7
SP - 867
EP - 875
JO - Applied Mathematics and Information Sciences
JF - Applied Mathematics and Information Sciences
SN - 1935-0090
IS - 3
ER -