An Improved Squirrel Search Algorithm for Optimization
Joint Authors
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-31, 31 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-01
Country of Publication
Egypt
No. of Pages
31
Main Subjects
Abstract EN
Squirrel search algorithm (SSA) is a new biological-inspired optimization algorithm, which has been proved to be more effective for solving unimodal, multimodal, and multidimensional optimization problems.
However, similar to other swarm intelligence-based algorithms, SSA also has its own disadvantages.
In order to get better global convergence ability, an improved version of SSA called ISSA is proposed in this paper.
Firstly, an adaptive strategy of predator presence probability is proposed to balance the exploration and exploitation capabilities of the algorithm.
Secondly, a normal cloud model is introduced to describe the randomness and fuzziness of the foraging behavior of flying squirrels.
Thirdly, a selection strategy between successive positions is incorporated to preserve the best position of flying squirrel individuals.
Finally, in order to enhance the local search ability of the algorithm, a dimensional search enhancement strategy is utilized.
32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are used to test the global search ability of the proposed ISSA.
Experimental test results indicate that ISSA provides competitive performance compared with the basic SSA and other four well-known state-of-the-art optimization algorithms.
American Psychological Association (APA)
Zheng, Tongyi& Luo, Weili. 2019. An Improved Squirrel Search Algorithm for Optimization. Complexity،Vol. 2019, no. 2019, pp.1-31.
https://search.emarefa.net/detail/BIM-1132415
Modern Language Association (MLA)
Zheng, Tongyi& Luo, Weili. An Improved Squirrel Search Algorithm for Optimization. Complexity No. 2019 (2019), pp.1-31.
https://search.emarefa.net/detail/BIM-1132415
American Medical Association (AMA)
Zheng, Tongyi& Luo, Weili. An Improved Squirrel Search Algorithm for Optimization. Complexity. 2019. Vol. 2019, no. 2019, pp.1-31.
https://search.emarefa.net/detail/BIM-1132415
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132415