algorithms
Article
An Improved Squirrel Search Algorithm for Global
Function Optimization
Yanjiao Wang and Tianlin Du *
School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China;
wangyanjiao1028@126.com
* Correspondence: 2201700466@neepu.edu.cn
Received: 10 March 2019; Accepted: 12 April 2019; Published: 17 April 2019
Abstract:
An improved squirrel search algorithm (ISSA) is proposed in this paper. The proposed
algorithm contains two searching methods, one is the jumping search method, and the other is
the progressive search method. The practical method used in the evolutionary process is selected
automatically through the linear regression selection strategy, which enhances the robustness of
squirrel search algorithm (SSA). For the jumping search method, the ‘escape’ operation develops
the search space sufficiently and the ‘death’ operation further explores the developed space, which
balances the development and exploration ability of SSA. Concerning the progressive search method,
the mutation operation fully preserves the current evolutionary information and pays more attention
to maintain the population diversity. Twenty-one benchmark functions are selected to test the
performance of ISSA. The experimental results show that the proposed algorithm can improve the
convergence accuracy, accelerate the convergence speed as well as maintain the population diversity.
The statistical test proves that ISSA has significant advantages compared with SSA. Furthermore,
compared with five other intelligence evolutionary algorithms, the experimental results and statistical
tests also show that ISSA has obvious advantages on convergence accuracy, convergence speed
and robustness.
Keywords:
the squirrel search algorithm; the jumping search method; the progressive search method;
linear regression selection strategy
1. Introduction
Optimization is one of the most common problems in the engineering field, and with the
development of new technology, the problems that need to be optimized have gradually turn to large
scale, multi peak and nonlinear approaches. The intelligence evolutionary algorithm is a mature global
optimization method with high robustness and wide applicability. The fact that the evolutionary
process is not constrained by search space and does not require other auxiliary information means
that the intelligence evolutionary algorithm can deal with complex problems effectively, which are
too difficult to be solved by the traditional optimization algorithms [
1
,
2
]. The applications of the
intelligence evolutionary algorithms have covered system control, machine design and engineering
planning, for example [3–7].
The intelligence evolutionary algorithms can be divided into the evolutionary heuristic algorithms,
the physical heuristic algorithms and the group heuristic algorithms, according to their inspiration. The
evolutionary heuristic algorithms originate from the genetic evolution process, with the representative
algorithms described as follows: The genetic algorithm imitates Darwin’s theory of natural selection
and finds the optimal solution by selection, crossover and mutation [
8
]. Similarly, the essence of the
differential evolutionary algorithm is the genetic algorithm based on real coding; the mutation operation
modifies each individual according to the difference vectors of population [
9
]. In the covariance-matrix
Algorithms 2019, 12, 80; doi:10.3390/a12040080 www.mdpi.com/journal/algorithms
