EBS-SCA --An Enhanced Brain Storm Sine Cosine Algorithm

The conventional sine cosine algorithm (SCA) does not appropriately balance exploration and exploitation, causing premature convergence, especially for complex optimization problems, such as the complex shifted or shifted rotated problems. To address this issue, this paper proposes an enhanced brain storm SCA (EBSSCA), where an enhanced brain storm (EBS) strategy is employed to improve the population diversity, and by combining it with two different update equations, two new individual update strategies (IUS: IUS-I and IUS-II) are developed to make effective balance between exploration and exploitation during the entire iterative search process. Double sets of benchmark suites involving 46 popular functions and two real-world problems are employed to compare EBS-SCA with other metaheuristic algorithms. Experimental results validate that the proposed EBS-SCA achieves the best overall performance including the global search ability, convergence speed, and scalability. %%% EBS-SCA --An Enhanced Brain Storm Sine Cosine Algorithm for Global Optimization Problems

% This software is copyrighted by CHUNQUAN LI, ZU LUO, ZHENSHOU SONG, FENG YANG, JINGHUI FAN, and PETER X. LIU.
% % Permission is granted to copy and use the software for scientific, % noncommercial purposes, provided this copyright notice is retained and the % origin of the code is cited. The software is provided "as is" and without % any warranties, express or implied.

This package contains three parts:

1.CEC 2013 benchmark functions:

cec13_func.cpp: The function file for the minimization problems.

input_data: The input data files of the 28 benchmark functions.

CEC 2013 benchmark functions.pdf: The introduction document of CEC 2013 benchmark functions.

2. EBS_SCA.m : The main file for EBS-SCA algorithm.

3. TEST.m is an example test code with EBS-SCA algorithm:

   max_iteration = 60000 Maximum number of iterations.

   max_FES = 300000 Maximum number of fitness evaluations.

   N = 50 Population size.

   D = 30 Dimension.

   M = 10 Number of clusters.

   lb = -100 Lower bound of a problem.

   ub = 100 Upper bound of a problem.

Note, operating environment configuration requires:

MatlabR2015a or a newer version;

The computer is configured with a C++ compiler.