Research Area:  Metaheuristic Computing

Abstract:

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.

Keywords:  
Squirrel search algorithm
biological-inspired optimization
swarm intelligence
better global convergence
unimodal
multimodal

Author(s) Name:  Tongyi Zheng, Weili Luo

Publisher name:  Hindawi

DOI:  https://doi.org/10.1155/2019/6291968

Volume Information:  Volume 2019

Paper Link:   https://www.hindawi.com/journals/complexity/2019/6291968/