1 简介

This paper presents a novel bio-inspired optimization algorithm called Rat Swarm Optimizer (RSO) for solving the challenging optimization problems. The main inspiration of this optimizer is the chasing and attacking behaviors of rats in nature. This paper mathematically models these behaviors and benchmarks on a set of 38 test problems to ensure its applicability on different regions of search space. The RSO algorithm is compared with eight well-known optimization algorithms to validate its performance. It is then employed on six real-life constrained engineering design problems. The convergence and computational analysis are also investigated to test exploration, exploitation, and local optima avoidance of proposed algorithm. The experimental results reveal that the proposed RSO algorithm is highly effective in solving real world optimization problems as compared to other well-known optimization algorithms.

2 部分代码

%_________________________________________________________________________%

%  Rat Swarm Optimizer (RSO)                                              %

%                                                                         %

%  Developed in MATLAB R2019b                                             %

%        %

%_________________________________________________________________________%

clear all 

clc

SearchAgents=30; 

Fun_name='F4';  

Max_iterations=300; 

[lowerbound,upperbound,dimension,fitness]=fun_info(Fun_name);

[Best_score,Best_pos,SHO_curve]=rso(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness);

figure('Position',[300 300 660 290])

%Draw search space

subplot(1,2,1);

fun_plot(Fun_name);

title('Parameter space')

xlabel('x_1');

ylabel('x_2');

zlabel([Fun_name,'( x_1 , x_2 )'])

%Draw objective space

subplot(1,2,2);

plots=semilogy(SHO_curve,'Color','g');

set(plots,'linewidth',2)

title('Objective space')

xlabel('Iterations');

ylabel('Best score');

axis tight

grid on

box on

legend('RSO')

display(['The best optimal value of the objective function found by RSO is : ', num2str(Best_score)]);

3 仿真结果

4 参考文献

[1] Dhiman G ,  Garg M ,  Nagar A , et al. A novel algorithm for global optimization: Rat Swarm Optimizer[J]. Journal of Ambient Intelligence and Humanized Computing, 2021, 12(8):8457-8482.

博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，有科研问题可私信交流。

部分理论引用网络文献，若有侵权联系博主删除。