-
Notifications
You must be signed in to change notification settings - Fork 37
/
Copy pathGWO.m
160 lines (135 loc) · 5.32 KB
/
GWO.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
function solution = GWO(UAV, SearchAgents, Max_iter)
%GWO 灰狼优化算法
%Gray Wolf Optimization
% 超参数
g = 50; % 动态加权系数
% 算法初始化
[WolfPops, Tracks] = PopsInit(UAV, SearchAgents, false); % 随机生成 初始狼群 和 轨迹们
dim = WolfPops.PosDim; % 状态变量维度
% 初始化解
Alpha_pos = zeros(1, dim); % α解
Alpha_score = inf; % α解适应度
Alpha_no = 1; % α解编号
Beta_pos = zeros(1, dim); % β解
Beta_score = inf; % β解适应度
Beta_no = 1; % β解编号
Delta_pos = zeros(1, dim); % δ解
Delta_score = inf; % δ解适应度
Delta_no = 1; % δ解编号
Fitness_list = zeros(1, Max_iter);
% 迭代求解
tic
fprintf('>>GWO 优化中 00.00%%')
for iter = 1 : Max_iter
% ① 计算每只狼的适应度，更改其种群等级
ProbPoints = cell(SearchAgents, 1);
for i = 1 : SearchAgents
% 计算目标函数
[fitness, ~, Data] = ObjFun(Tracks{i}, UAV); % 一个智能体的目标函数
ProbPoints{i} = Data.ProbPoint; % 所有智能体不符合条件的状态
% 更新 Alpha、Beta 和 Delta 解
if fitness <= Alpha_score % 适应能力最强（因为性能指标越小越好，因此为小于号）
Alpha_score = fitness;
Alpha_pos = WolfPops.Pos(i, :);
Alpha_no = i;
end
if fitness > Alpha_score && fitness <= Beta_score
Beta_score = fitness;
Beta_pos = WolfPops.Pos(i, :);
Beta_no = i;
end
if fitness > Alpha_score && fitness > Beta_score && fitness <= Delta_score
Delta_score = fitness;
Delta_pos = WolfPops.Pos(i, :);
Delta_no = i;
end
end
% ② 更新参数a
a = 2 - iter * 2 / Max_iter; % 线性递减
%a = 2 * cos((iter / Max_iter) * pi/2); % 非线性递减
% ③ 更新位置（朝着前三只狼位置前进）
% r1 = rand(SearchAgents, dim);
% r2 = rand(SearchAgents, dim);
% A1 = 2*a*r1 - a;
% C1 = 2*r2;
% D_alpha = abs(C1.*repmat(Alpha_pos, SearchAgents, 1) - WolfPops.Pos);
% X1 = repmat(Alpha_pos, SearchAgents, 1) - A1.*D_alpha;
%
% r1 = rand(SearchAgents, dim);
% r2 = rand(SearchAgents, dim);
% A2 = 2*a*r1 - a;
% C2 = 2*r2;
% D_beta = abs(C2.*repmat(Beta_pos, SearchAgents, 1) - WolfPops.Pos);
% X2 = repmat(Beta_pos, SearchAgents, 1) - A2.*D_beta;
%
% r1 = rand(SearchAgents, dim);
% r2 = rand(SearchAgents, dim);
% A3 = 2*a*r1 - a;
% C3 = 2*r2;
% D_delta = abs(C3.*repmat(Delta_pos, SearchAgents, 1) - WolfPops.Pos);
% X3 = repmat(Delta_pos, SearchAgents, 1) - A3.*D_delta;
%
% %-- 静态更新位置
% WolfPops.Pos = (X1 + X2 + X3) / 3;
%-- 动态更新位置
% q = g * a;
% if abs(Alpha_score - Delta_score) > q
% Sum_score = Alpha_score + Beta_score + Delta_score;
% WolfPops.Pos = (Alpha_score*X1 + Beta_score*X2 + Delta_score*X3) / Sum_score;
% else
% WolfPops.Pos = (X1 + X2 + X3) / 3;
% end
for i = 1 : SearchAgents
for j = 1 : dim
r1 = rand();
r2 = rand();
A1 = 2*a*r1 - a;
C1 = 2*r2;
D_alpha = abs(C1*Alpha_pos(j) - WolfPops.Pos(i, j));
X1 = Alpha_pos(j) - A1*D_alpha;
r1 = rand();
r2 = rand();
A2 = 2*a*r1 - a;
C2 = 2*r2;
D_beta = abs(C2*Beta_pos(j) - WolfPops.Pos(i, j));
X2 = Beta_pos(j) - A2*D_beta;
r1 = rand();
r2 = rand();
A3 = 2*a*r1 - a;
C3 = 2*r2;
D_delta = abs(C3*Delta_pos(j) - WolfPops.Pos(i, j));
X3 = Delta_pos(j) - A3*D_delta;
%静态更新
WolfPops.Pos(i, j) = (X1 + X2 + X3) / 3;
% %动态更新
% % q = g * a; %阈值
% % if abs(Alpha_score - Delta_score) > q
% % Sum_score = Alpha_score + Beta_score + Delta_score;
% % WolfPops.Pos(i, j) = (Alpha_score*X1 + Beta_score*X2 + Delta_score*X3) / Sum_score;
% % else
% % WolfPops.Pos(i, j) = (X1 + X2 + X3) / 3;
% % end
end
end
% ④ 调整不符合要求的状态变量
[WolfPops, Tracks] = BoundAdjust(WolfPops, ProbPoints, UAV);
% ⑤ 存储适应度
Fitness_list(iter) = Alpha_score;
if iter/Max_iter*100 < 10
fprintf('\b\b\b\b\b%.2f%%', iter/Max_iter*100)
else
fprintf('\b\b\b\b\b\b%.2f%%', iter/Max_iter*100)
end
end
fprintf('\n\n>>计算完成！\n\n')
toc
% 输出值
solution.method = 'GWO'; % 算法
solution.WolfPops = WolfPops; % 所有解种群信息
solution.Tracks = Tracks; % 所有解航迹信息
solution.Fitness_list = Fitness_list; % α解适应度曲线
solution.Alpha_Data = Data; % α解的威胁信息
solution.Alpha_no = Alpha_no; % α解的位置
solution.Beta_no = Beta_no; % β解的位置
solution.Delta_no = Delta_no; % δ解的位置
end