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Mathematical Problems in Engineering
Volume 2014, Article ID 936374, 21 pages
Research Article

A Free Search Krill Herd Algorithm for Functions Optimization

College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China

Received 5 April 2014; Revised 13 May 2014; Accepted 21 May 2014; Published 19 June 2014

Academic Editor: Yang Xu

Copyright © 2014 Liangliang Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To simulate the freedom and uncertain individual behavior of krill herd, this paper introduces the opposition based learning (OBL) strategy and free search operator into krill herd optimization algorithm (KH) and proposes a novel opposition-based free search krill herd optimization algorithm (FSKH). In FSKH, each krill individual can search according to its own perception and scope of activities. The free search strategy highly encourages the individuals to escape from being trapped in local optimal solution. So the diversity and exploration ability of krill population are improved. And FSKH can achieve a better balance between local search and global search. The experiment results of fourteen benchmark functions indicate that the proposed algorithm can be effective and feasible in both low-dimensional and high-dimensional cases. And the convergence speed and precision of FSKH are higher. Compared to PSO, DE, KH, HS, FS, and BA algorithms, the proposed algorithm shows a better optimization performance and robustness.