Approach to WTA in air combat using IAFSA-IHS algorithm

doi:10.21629/JSEE.2018.03.09

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (3): 519-529.doi: 10.21629/JSEE.2018.03.09

• Systems Engineering • Previous Articles Next Articles

Approach to WTA in air combat using IAFSA-IHS algorithm

Zhanwu LI¹(), Yizhe CHANG^2,*(), Yingxin KOU²(), Haiyan YANG²(), An XU²(), You LI²()

¹ College of Electronic Communication, Northwestern Polytechnical University, Xi’an 710072, China
² Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, China

Received:2016-12-30 Online:2018-06-28 Published:2018-07-02
Contact: Yizhe CHANG E-mail:afeulzw@189.cn;mastercyz@163.com;afcekyx@hotmail.com;yanghy07@yeah.net;aspire2010@sohu.com;reason891020@qq.com
About author:LI Zhanwu was born in 1978. He received his B.S. and M.S. degree in fire control engineering from Air Force Engineering University in 2000 and 2007, respectively. Now he is a Ph.D. candidate in Northwestern Polytechnical University. His research interests include aviation fire control theory and system engineering. E-mail: afeulzw@189.cn|CHANG Yizhe was born in 1991. He received his B.S. and M.S. degrees in fire control engineering from Air Force Engineering University in 2013 and 2015, respectively. Now he is a Ph.D. candidate in Air Force Engineering University. His research interests include aviation fire control theory and system engineering. E-mail: mastercyz@163.com|KOU Yingxin was born in 1965. He received his Ph.D degree in fire control engineering from Air Force Engineering University in 2006. Now he is a professor in Air Force Engineering University. His research interests include aviation fire control theory and system engineering. E-mail: afcekyx@hotmail.com|YANG Haiyan was born in 1972. She received her Ph.D. degree in automation from Tsinghua University in 2009. Now she is a lecturer in Air Force Engineering University. Her research interests include situation assessment and Bayesian estimation. E-mail: yanghy07@yeah.net|XU An was born in 1984. He received his Ph.D. degree from Air Force Engineering University in 2012. Now he is a lecturer in Air Force Engineering University. His research interests include aviation fire control theory and system engineering. E-mail: aspire2010@sohu.com|LI You was born in 1989. He received his master degree from Air Force Engineering University in 2014. Now he is a Ph.D. candidate in Air Force Engineering University. His research interests include aviation fire control theory and system engineering. E-mail: reason891020@qq.com
Supported by:
the National Natural Science Foundation of China(61472441);This work was supported by the National Natural Science Foundation of China (61472441)

Abstract

Abstract:

In this paper, a static weapon target assignment (WTA) problem is studied. As a critical problem in cooperative air combat, outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm (IAFSA-IHS) is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.

Key words: air combat, weapon target assignment, improved artificial fish swarm algorithm-improved harmony search algorithm (IAFSA-IHS), artificial fish swarm algorithm (AFSA), harmony search (HS)

Zhanwu LI, Yizhe CHANG, Yingxin KOU, Haiyan YANG, An XU, You LI. Approach to WTA in air combat using IAFSA-IHS algorithm[J]. Journal of Systems Engineering and Electronics, 2018, 29(3): 519-529.

Figures/Tables 15

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References 30

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Algorithm	Parameter
GA	Population ${{{\rm{size}}}} = 50, P_c = 0.8, P_m = 0.05 $
PSO	Particle swarm ${{{\rm{size}}}} = 50, v_m = 0.8, \omega = 0.8$
ACO	Ant swarm ${{{\rm{size}}}} = 50, \alpha = 0.8, \beta = 0.8, \rho = 0.8$
SA	$T_0 = 1\; 000, T_{end} = 0.000\; 1, \lambda = 0.6, L = 100$
AFSA	Fish swarm ${{{\rm{size}}}} = 50, visual = 1, step = 0, $ trynumber = 10, δ= 0.5
HS	${\rm HMS = 50, HMCR = 0.8, PAR} = 0.3, bw = 0.02$
IBAFSA	Fish swarm ${{{\rm{size}}}} = 50, P_m = 0.01, \theta = 0.8, L = 10$
GDHS	${\rm HMS = 50, HMCR = 0.8, PAR} = 0.3, bw = 0.02, $
IABHS	$ {\rm NGC} = 30, C = 20$ Fish swarm ${{{\rm{size}}}} = 50, ingen = 50, H_0 = 0.3, S = 5$,
IAFSA-IHS	$visual = 1.5, \gamma _1 = 0.3, \gamma _2 = 0.5, c_1 = 0.6, c_2 = 0.4, $$\varphi _1 = 0.6, \varphi _2 = 0.4, gen_{switch} = 20, se_{def} = 5$

$T_j$	$F_1$		$F_2$		$F_3$		$F_4$
$T_j$	$M_1$	$M_2$	$M_3$	$M_4$	$M_5$	$M_6$	$M_7$	$M_8$
$T_1$	0.914 7	0.905 7	0.126 9	0.913 3	0.632 3	0.097 5	0.278 4	0.546 8
$T_1$	0.957 5	0.964 8	0.157 6	0.9705	0.957 1	0.485 3	0.800 2	0.141 8
$T_3$	0.421 7	0.915 7	0.902 2	0.959 4	0.655 7	0.035 7	0.849 1	0.933 9
$T_4$	0.678 7	0.757 7	0.743 1	0.392 2	0.655 4	0.171 1	0.706 0	0.831 8
$T_5$	0.276 9	0.046 1	0.097 1	0.823 4	0.694 8	0.317 0	0.950 2	0.034 4
$T_6$	0.438 7	0.981 5	0.765 5	0.795 1	0.186 8	0.509 7	0.445 5	0.646 3

Algorithm	$F_1$		$F_2$		$F_3$		$F_4$		Fitness	Probability of	Cost	$t$/s
Algorithm	$M_1$	$M_2$	$M_3$	$M_4$	$M_5$	$M_6$	$M_7$	$M_8$	value	target survivability	Cost	$t$/s
IAFSA-IHS	$T_2$	$T_6$	$T_4$	$T_1$	$T_4$	$T_5$	$T_5$	$T_3$	0.215 4	0.336	6	2.368 7
GDHS	$T_4$	$T_6$	$T_3$	$T_1$	$T_2$	0	$T_5$	$T_4$	0.247 4	0.326 2	5	2.966 3
IABHS	$T_1$	$T_6$	$T_3$	$T_5$	$T_2$	0	$T_4$	$T_4$	0.326 7	0.470 6	5	2.522 4
IBAFSA	$T_2$	$T_6$	$T_3$	$T_1$	$T_4$	0	$T_5$	$T_4$	0.257 1	0.353 3	5	3.001 6
AFSA	$T_1$	$T_2$	$T_4$	$T_5$	$T_5$	0	$T_3$	$T_6$	0.711 7	0.935 9	5	3.539 1
HS	0	$T_2$	$T_6$	$T_1$	$T_4$	$T_4$	$T_5$	$T_3$	0.568 5	0.757 9	5.4	4.039 1
GA	$T_1$	$T_2$	$T_6$	$T_5$	$T_5$	$T_3$	$T_4$	0	1.584 5	1.6672	5.4	4.437 8
ACO	$T_4$	$T_1$	$T_1$	$T_2$	$T_5$	$T_6$	$T_3$	$T_4$	0.910 8	1.1123	6	4.189
PSO	$T_3$	$T_1$	$T_4$	$T_6$	$T_5$	$T_2$	$T_1$	$T_3$	1.132 2	1.3880	6	3.658 4
SA	$T_5$	$T_1$	$T_4$	$T_2$	$T_5$	$T_6$	$T_1$	$T_3$	0.924 8	1.1315	6	4.672 2

Algorithm	$T_1$	$T_2$	$T_3$	$T_4$	$T_5$	$T_6$
IAFSA-IH	0.086 7	0.042 5	0.066 1	0.088 5	0.034	0.018 5
GDHS	0.086 7	0.042 9	0.097 8	0.030 5	0.049 8	0.018 5
IABHS	0.085 3	0.042 9	0.097 8	0.049 5	0.176 6	0.018 5
IBAFSA	0.086 7	0.042 5	0.097 8	0.058	0.049 8	0.018 5
AFSA	0.085 3	0.035 2	0.150 9	0.256 9	0.053 9	0.353 7
HS	0.086 7	0.035 2	0.066 1	0.285 6	0.049 8	0.234 5
GA	0.085 3	0.035 2	0.964 3	0.294 0	0.053 9	0.234 5
ACO	0.082 3	0.029 5	0.150 9	0.054 0	0.305 2	0.490 3
PSO	0.068 0	0.514 7	0.038 2	0.256 9	0.305 2	0.204 9
SA	0.068 0	0.029 5	0.066 1	0.256 9	0.220 7	0.490 3

Algorithm	$F_1$		$F_2$		$F_3$		$F_4$		Fitness	Probability of	Cost	$t$/s
Algorithm	$M_1$	$M_2$	$M_3$	$M_4$	$M_5$	$M_6$	$M_7$	$M_8$	value	target survivability	Cost	$t$/s
IAFSA-IHS	$T_1$	$T_2$	$T_6$	$T_3$	0	$T_6$	$T_5$	$T_4$	0.393 5	0.525 2	5.4	03479 6
GDHS	$T_4$	$T_2$	$T_3$	$T_1$	0	$T_6$	$T_5$	$T_6$	0.433 6	0.610 1	5.4	0.671 4
IABHS	$T_1$	$T_3$	0	$T_5$	$T_2$	$T_6$	$T_6$	$T_4$	0.532 2	0.756 1	5	0.762 3
IBAFSA	$T_1$	$T_6$	0	$T_5$	$T_2$	0	$T_3$	$T_4$	0.445 8	0.642 4	4	0.869 9
AFSA	$T_1$	$T_6$	0	$T_2$	$T_5$	$T_2$	$T_4$	$T_3$	0.586 4	0.781 3	5	1.124 4
HS	$T_2$	$T_1$	$T_3$	$T_5$	$T_4$	0	$T_5$	$T_6$	0.604 1	0.941 7	5	1.375 1
GA	$T_3$	$T_1$	$T_1$	$T_6$	$T_5$	$T_2$	$T_6$	$T_4$	1.263 3	1.762 4	6	1.967
ACO	0	$T_6$	0	$T_3$	$T_1$	$T_2$	$T_5$	$T_4$	0.713 8	1.156 5	4.4	1.996 3
PSO	$T_1$	$T_2$	$T_3$	$T_6$	$T_4$	0	$T_6$	$T_5$	1.024 4	1.642 1	5	1.035
SA	$T_1$	$T_6$	$T_2$	$T_4$	$T_3$	$T_5$	0	$T_5$	1.869 8	2.554 8	5	2.061 4

Approach to WTA in air combat using IAFSA-IHS algorithm

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$T_j$	$F_1$		$F_2$		$F_3$		$F_4$		$F_5$		$F_6$
$T_j$	$M_1$	$M_2$	$M_3$	$M_4$	$M_5$	$M_6$	$M_7$	$M_8$	$M_9$	$M_{10}$	$M_{11}$	$M_{12}$
$T_1$	0.927 4	0.7593	0.437 2	0.790 1	0.516 1	0.322 9	0.786 1	0.878 6	0.961 7	0.778 4	0.989 4	0.345 9
$T_2$	0.647 0	0.865 6	0.735 7	0.711 6	0.996 2	0.341 4	0.743 1	0.524 5	0.924 5	0.505 9	0.330 1	0.542 2
$T_3$	0.880 2	0.762 9	0.612 6	0.907 5	0.648 9	0.870 7	0.634 4	0.589 9	0.669 8	0.658 3	0.931 6	0.582 8
$T_4$	0.762 9	0.262 1	0.887 7	0.372	0.397 6	0.380 7	0.273 7	0.492 9	0.234 2	0.273 1	0.930 7	0.499 9
$T_5$	0.382 1	0.654 5	0.315 7	0.919 5	0.404 5	0.361 7	0.577	0.400 1	0.468 5	0.466 3	0.735 5	0.819 1
$T_6$	0.717 9	0.330 0	0.465 7	0.988 2	0.995 1	0.266 9	0.648 5	0.237 4	0.324 7	0.510 1	0.209 6	0.377 3
$T_7$	0.292 4	0.725 1	0.947 9	0.556 1	0.376 6	0.251 0	0.990 0	0.594 4	0.399 3	0.985 9	0.417 4	0.262 6
$T_8$	0.645 4	0.878 4	0.645 3	0.603 0	0.572 5	0.903 3	0.601 9	0.590 1	0.314 4	0.364 5	0.365 8	0.282 7
$T_9$	0.578 7	0.904 4	0.946 7	0.261 4	0.747 4	0.481 8	0.372 3	0.817 4	0.279 7	0.357 9	0.446 5	0.844 4
$T_{10}$	0.821 7	0.931 6	0.674 9	0.475 9	0.835 3	0.385 7	0.704 6	0.860 7	0.618 4	0.422 5	0.769 7	0.316 3

Algorithm	$T_1$	$T_2$	$T_3$	$T_4$	$T_5$	$T_6$	$T_7$	$T_8$	$T_9$	$T_10$
IAFSA-IH	0.677 1	0.003 8	0.387 4	0.726`3	0.163 4	0.011 8	0.737 4	0.635 5	0.720 3	0.009 5
GDHS	0.038 3	0.134 4	0.119 8	0.069 3	0.180 9	0.004 9	0.01	0.096 7	0.053 3	0.139 3
IABHS	0.038 3	0.003 8	0.119 8	0.112 3	0.180 9	0.011 8	0.014 1	0.121 6	0.182 6	0.068 0
IBAFSA	0.121 4	0.047 5	0.068 4	0.500 1	0.051 4	0.489 9	0.010 0	0.354 7	0.720 3	0.164 7
AFSA	0.010 6	0.475 5	0.129 3	0.500 1	0.533 7	0.351 5	0.052 1	0.397 0	0.182 0	0.012 2
HS	0.010 6	0.457 8	0.351 1	0.619 3	0.042 8	0.215 1	0.010 0	0.635 5	0.053 3	0.068 4
GA	0.051 5	0.475 5	0.341 7	0.237 1	0.339 3	0.534 3	0.737 4	0.427 5	0.738 6	0.230 3
ACO	0.213 9	0.353 0	0.237 1	0.368 6	0.684 3	0.004 9	0.600 7	0.069 4	0.553 5	0.524 1
PSO	0.010 6	0.117 6	0.129 3	0.765 8	0.684 3	0.004 9	0.010 0	0.354 7	0.311 2	0.577 5
SA	0.677 1	0.457 8	0.091 8	0.237 1	0.080 5	0.533 8	0.014 1	0.398 1	0.252 6	0.139 3