Enhancing forest optimization algorithm with gravitational search for nonlinear continuous optimization

Abstract

Meta-heuristic algorithms have great role in solving problems related to optimization. Meta-heuristic method cannot solve problems related to optimization due to No Free Lunch theory. Hence different optimization methods are proposed by various researchers each year in order to solve optimization problems. Forest Optimization Algorithm (FOA) is an evolutionary optimization algorithm that is appropriate for continuous nonlinear optimization problems. The algorithm drawbacks include entrapment in local optimum and failure in achieving global optimum. The paper proposes hybrid algorithm called FOAGSA, in which the Gravitational Search Algorithm (GSA) is employed to improve the FOA performance in order to solve nonlinear continuous problems. The FOAGSA was evaluated through 39 benchmark optimization functions and two engineering problems. The experimental results proved that the FOAGSA exhibited acceptable results compared to state-of-art and well-known Meta-heuristic algorithms. Friedman ranking algorithm was utilized to compare FOAGSA with existing methods. The FOAGSA was ranked first on that basis.

Keywords:

• Optimization
• metaheuristic algorithms
• Forest Optimization Algorithm
• Gravitational Search Algorithm
• hybrid algorithm

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The benchmark functions analysed during the current study are available in the: https://github.com/P-N-Suganthan/CEC2019/blob/master/Matlab%20version.zip https://www.mathworks.com/matlabcentral/fileexchange/27756-gravitational-search-algorithm-gsa

You can access the MATLAB codes for all the competitive algorithms utilized in this study through the provided links:

FOA: https://github.com/cominsys/FOA

FDA: https://seyedalimirjalili.com/projects

HGSO: https://seyedalimirjalili.com/hgso

AOA: https://seyedalimirjalili.com/aoa

WOA: https://seyedalimirjalili.com/woa

MFO: http://www.alimirjalili.com/MFO.html

GWO: https://seyedalimirjalili.com/gwo

SSA: https://seyedalimirjalili.com/ssa

SHO: https://www.mathworks.com/matlabcentral/fileexchange/64409-the-selfish-herd-optimizer-sho

GA-MPC: https://www3.ntu.edu.sg/home/epnsugan/index_files/CEC11-RWP/CEC11-RWP.htm

GSA: https://www.mathworks.com/matlabcentral/fileexchange/27756-gravitational-search-algorithm-gsa

PSO: https://seyedalimirjalili.com/pso

DE: https://github.com/sriki18/MDEpBX-Matlab/blob/master/Rundeopt.m

GA: https://github.com/jangholi/stock-prediction/blob/master/ga.m