1 简介
In this paper, a novel context sensitive energy curve based Masi entropy for image segmentation using moth swarm algorithm (MSA) has been proposed. Although Masi entropy deals with complete probability distribution for image segmentation but the performance is not satisfactory. However, better results can be obtained using the concept of energy curve for Masi entropy, but it consumes more time and also the complexity level for selecting suitable thresholds is high. MSA is a newly developed stochastic meta-heuristic optimization algorithm introduced after observing, mimicking and modeling the life cycle of moth swarm. It is used to simplify the problem of extensive exploration for finding the optimum threshold values and to increase the quality of the images. Experiments on standard daily-life color images are showed to establish the usefulness of the presented approach. The Energy-Masi-MSA technique is examined intensively regarding visual quality and quantitative matrices are considered to evaluate the results of the Energy-Masi-MSA scheme compared to existing methods. Unlike other meta-heuristic algorithms used for thresholding operations, MSA provides a higher performance regarding threshold quality and low computational cost. Experimental data boosts the use of MSA for energy curve based thresholding with Masi entropy.
2 分代码
%______________________________________________________________________________________________
% Moth Swarm Algorithm (MSA)
% Developed in MATLAB R2008b
%
%
%
%_______________________________________________________________________________________________
% This algorithm has a fast convergence characteristics.
% Appreciated results can be obtained with a small number of iterations.
for iji=[1,8,21]
if iji==1;F=('F1');elseif iji==2;F=('F2');elseif iji==3;F=('F3');elseif iji==4;F=('F4');elseif iji==5;F=('F5'); ...
elseif iji==6;F=('F6');elseif iji==7; F=('F7'); elseif iji==8; F=('F8');elseif iji==9; F=('F9'); ...
elseif iji==10; F=('F10');elseif iji==11; F=('F11');elseif iji==12; F=('F12'); ...
elseif iji==13; F=('F13');elseif iji==14; F=('F14');elseif iji==15; F=('F15');
elseif iji==16; F=('F16');elseif iji==17; F=('F17');elseif iji==18; F=('F18');
elseif iji==19; F=('F19');elseif iji==20; F=('F20');elseif iji==21; F=('F21');
elseif iji==22; F=('F22');elseif iji==23; F=('F23');
end
if iji < 14;Max_iteration=1000;else Max_iteration=500;end% Maximum number of iterations
SearchAgents_no=30;% Number of search agents
Nc=6;% Number of Pathfinders: 4 <= Nc <= 20% of SearchAgents_no
% Load details of the selected benchmark function
[lb,ub,dim,fobj]=Get_Functions_details(F);
[Best_pos,Best_score,Convergence_curve]=MSA(SearchAgents_no,Nc,Max_iteration,ub,lb,dim,fobj);
%Draw and display objective function
figure,semilogy(Convergence_curve); title( F ); xlabel('Iteration'); ylabel('Best score');
display(['The optimal solution of ',F, ' is: ',num2str(Best_pos)]);
display(['The optimal value of ',F,' is : ', num2str(Best_score)]);
end
% =====================================================
3 仿真结果
4 参考文献
- Allam, D., Yousri, D. A., & Eteiba, M. B. (2016). Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame Optimization Algorithm. Energy Conversion and Management, 123, 535–548.Google Scholar
- Bentouati, B., Chaib, L., & Chettih, S. (2016). Optimal Power Flow using the Moth Flam Optimizer: A case study of the Algerian power system. Indonesian Journal of Electrical Engineering and Computer Science, 1(3), 431–445.
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。
