Modified seeker optimization algorithm for unconstrained optimization problems

Authors:
Ivona Brajevic;Milan Tuba
Affiliations:
Faculty of Mathematics,%' or paper_id like 'University of Belgrade, Serbia;Faculty of Computer Science, Megatrend University Belgrade, Serbia
Venue:
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
Year:
2012

Citing 3
Cited 0

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Seeker optimization algorithm for tuning the structure and parameters of neural networks

Neurocomputing

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Abstract

Seeker optimization algorithm (SOA) is a novel search algorithm based on simulating the act of human searching, which has been shown to be a promising candidate among search algorithms for unconstrained function optimization. In this article we propose a modified seeker optimization algorithm. In order to enhance the performance of SOA, our proposed approach uses two search equations for producing new population and employs modified inter-subpopulation learning phase of algorithm. This modified algorithm has been implemented and tested on fourteen multimodal benchmark functions and proved to be better on majority of tested problems.