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Hybrid Seeker Optimization Algorithm for Global Optimization |
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PP: 867-875 |
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Author(s) |
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Milan Tuba,
Ivona Brajevic,
Raka Jovanovic,
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Abstract |
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| 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. |
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