Differential disturbed heap-based optimizer

doi:10.11772/j.issn.1001-9081.2021061104

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (8): 2519-2527.DOI: 10.11772/j.issn.1001-9081.2021061104

• Advanced computing • Previous Articles Next Articles

Differential disturbed heap-based optimizer

Xinming ZHANG¹^,²(), Shaochen WEN¹, Shangwang LIU¹^,²

^1.College of Computer and Information Engineering，Henan Normal University，Xinxiang Henan 453007，China
^2.Engineering Lab of Intelligence Business and Internet of Things of Henan Province （Henan Normal University），Xinxiang Henan 453007，China

Received:2021-07-01 Revised:2021-09-11 Accepted:2021-09-28 Online:2022-08-09 Published:2022-08-10
Contact: Xinming ZHANG
About author:ZHANG Xinming， born in 1963， M. S.， professor. His research interests include intelligent optimization algorithm， image segmentation.
WEN Shaochen， born in 1997， M. S. candidate. Her research interests include intelligent optimization algorithm， image segmentation.
LIU Shangwang， born in 1973， Ph. D.， associate professor. His research interests include image processing， computer vision.
Supported by:
Key Scientific Research Project of Higher Education Institutions of Henan Province(19A520026)

差分扰动的堆优化算法

张新明¹^,²(), 温少晨¹, 刘尚旺¹^,²

^1.河南师范大学计算机与信息工程学院, 河南新乡 453007
^2.智慧商务与物联网技术河南省工程实验室(河南师范大学), 河南新乡 453007

通讯作者: 张新明
作者简介:张新明（1963—），男，湖北孝感人，教授，硕士，CCF会员，主要研究方向：智能优化算法、图像分割；
温少晨（1997—），女，河南濮阳人，硕士研究生，主要研究方向：智能优化算法、图像分割；
刘尚旺（1973—），男，河南新乡人，副教授，博士，主要研究方向：图像处理、计算机视觉。
基金资助:
河南省高等学校重点科研项目(19A520026)

Abstract

Abstract:

In order to solve the problems， such as insufficient search ability and low search efficiency of Heap-Based optimizer （HBO） in solving complex problems， a Differential disturbed HBO （DDHBO） was proposed. Firstly， a random differential disturbance strategy was proposed to update the best individual’s position to solve the problem of low search efficiency caused by not updating of this individual by HBO. Secondly， a best worst differential disturbance strategy was used to update the worst individual’s position and strengthen its search ability. Thirdly， the ordinary individual’s position was updated by a multi-level differential disturbance strategy to strengthen information communication among individuals between multiple levels and improve the search ability. Finally， a dimension-based differential disturbance strategy was proposed for other individuals to improve the probability of obtaining effective solutions in initial stage of original updating model. Experimental results on a large number of complex functions from CEC2017 show that compared with HBO， DDHBO has better optimization performance on 96.67% functions and less average running time （3.445 0 s）， and compared with other state-of-the-art algorithms， such as Worst opposition learning and Random-scaled differential mutation Biogeography-Based Optimization （WRBBO）， Differential Evolution and Biogeography-Based Optimization （DEBBO）， Hybrid Particle Swarm Optimization and Grey Wolf Optimizer （HGWOP）， etc.， DDHBO also has significant advantages.

Key words: optimization algorithm, meta-heuristic algorithm, Heap-Based Optimizer (HBO), global best solution, differential disturbance

摘要：

针对堆优化算法（HBO）在解决复杂问题时存在搜索能力不足和搜索效率低等缺陷，提出一种差分扰动的HBO——DDHBO。首先，提出一种随机差分扰动策略更新最优个体的位置，以解决HBO没有对其更新从而导致的搜索效率低的问题；其次，使用一种最优最差差分扰动策略更新最差个体的位置，以强化其搜索能力；然后，采用一种多层差分扰动策略更新一般个体的位置，以强化多层个体之间的信息交流，并提高搜索能力；最后，针对原更新模型在搜索初期获得有效解概率低的问题，提出一种基于维的差分扰动策略更新其他个体的位置。在大量CEC2017复杂函数上的实验结果表明，与HBO相比，DDHBO在96.67%的函数上具有更好的优化性能，更少的平均运行时间（3.445 0 s）；与WRBBO（Worst opposition learning and Random-scaled differential mutation Biogeography-Based Optimization）、DEBBO（Differential Evolution and Biogeography-Based Optimization）和HGWOP（Hybrid PSO and Grey Wolf Optimizer）等先进算法相比，DDHBO也具有显著的优势。

关键词: 优化算法, 元启发式算法, 堆优化算法, 全局最优解, 差分扰动

CLC Number:

TP181

Xinming ZHANG, Shaochen WEN, Shangwang LIU. Differential disturbed heap-based optimizer[J]. Journal of Computer Applications, 2022, 42(8): 2519-2527.

张新明, 温少晨, 刘尚旺. 差分扰动的堆优化算法[J]. 《计算机应用》唯一官方网站, 2022, 42(8): 2519-2527.

Figures/Tables 9

References 15

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算法	提出年份	参数设置
DDHBO	2021	N=40， T=7 500， γ=［0，2］， λ=［-1，1］
HBO^［7］	2020	N=40， T=7 500， γ=［0，2］， λ=［-1，1］
WRBBO^［13］	2019	N=100， T=3 000， I=1
DEBBO^［2］	2018	N=100， T=3 000， I =0.99， E=0.99， m_max=0.006
HGWOP^［3］	2021	N=100， T=3 000， 0 < a < 2， 0 < c_r < 1
MEGWO^［14］	2019	N=100， T=3 000， GR = 0.8， 0.6 < SR< 1， 0 < DR< 0.4
MPSO^［15］	2020	N=50， T=6 000， w=1， φ₁=φ₂=2.5

算法	提出年份	参数设置
DDHBO	2021	N=40， T=7 500， γ=［0，2］， λ=［-1，1］
HBO^［7］	2020	N=40， T=7 500， γ=［0，2］， λ=［-1，1］
WRBBO^［13］	2019	N=100， T=3 000， I=1
DEBBO^［2］	2018	N=100， T=3 000， I =0.99， E=0.99， m_max=0.006
HGWOP^［3］	2021	N=100， T=3 000， 0 < a < 2， 0 < c_r < 1
MEGWO^［14］	2019	N=100， T=3 000， GR = 0.8， 0.6 < SR< 1， 0 < DR< 0.4
MPSO^［15］	2020	N=50， T=6 000， w=1， φ₁=φ₂=2.5

算法	平均排名	总排名
HBO	4.70	5
ESHBO	3.87	4
EBHBO	2.93	3
WEHBO	2.13	2
DDHBO	1.37	1

算法	平均排名	总排名
HBO	4.70	5
ESHBO	3.87	4
EBHBO	2.93	3
WEHBO	2.13	2
DDHBO	1.37	1

函数	ESHBO vs HBO	EBHBO vs ESHBO	WEHBO vs EBHBO	DDHBO vs WEHBO	函数	ESHBO vs HBO	EBHBO vs ESHBO	WEHBO vs EBHBO	DDHBO vs WEHBO
F₁	-	+	+	+	F₁₇	+	+	+	+
F₂	+	+	+	+	F₁₈	+	+	+	+
F₃	+	+	+	+	F₁₉	-	+	+	+
F₄	+	+	+	+	F₂₀	+	+	+	+
F₅	+	+	+	+	F₂₁	+	+	+	+
F₆	-	-	-	+	F₂₂	+	+	-	+
F₇	+	+	+	+	F₂₃	+	+	+	+
F₈	+	+	+	+	F₂₄	+	+	+	+
F₉	+	-	+	+	F₂₅	+	+	-	+
F₁₀	+	+	+	+	F₂₆	+	+	+	-
F₁₁	+	+	+	-	F₂₇	-	+	+	+
F₁₂	+	+	+	+	F₂₈	+	-	+	-
F₁₃	+	+	+	+	F₂₉	+	+	+	+
F₁₄	+	+	+	+	F₃₀	+	+	+	+
F₁₅	-	+	+	+	n/+/≈/-	30/25/0/5	30/27/0/3	30/27/0/3	30/27/0/3
F₁₆	+	+	+	+	n/+/≈/-	30/25/0/5	30/27/0/3	30/27/0/3	30/27/0/3

Differential disturbed heap-based optimizer

差分扰动的堆优化算法

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Figures/Tables 9

References 15

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Metrics

函数		DDHBO	HBO	WRBBO	DEBBO	SaDE	SE04	HGWOP	MEGWO	MPSO
F₁	Mean	3.148 7E-14	2.917 8E+02	3.018 6E+03	2.784 9E+03	3.071 4E+03	3.293 0E+03	1.936 1E+03	4.551 7E+03	4.753 9E+07
	Std	1.538 8E-14	7.396 1E+02	3.698 1E+03	4.036 4E+03	3.507 2E+03	4.232 8E+03	1.965 7E+03	1.067 7E+03	1.082 2E+08
	Rank	1	2	5	4	6	7	3	8	9
F₂	Mean	3.842 4E-07	1.876 5E+20	1.893 9E+10	8.268 8E+17	8.627 5E-01	3.080 2E+13	3.721 9E-04	2.888 4E+08	4.099 8E+16
	Std	5.144 9E-07	8.717 9E+20	5.612 3E+10	2.405 4E+18	4.935 7E+00	1.169 4E+14	1.306 9E-03	8.057 1E+08	1.991 8E+17
	Rank	1	9	5	8	3	6	2	4	7
F₃	Mean	1.326 3E-13	2.817 3E+03	4.625 8E+03	3.677 2E+04	3.004 5E+02	9.797 4E+03	7.314 3E-05	2.263 3E+02	1.682 4E-01
	Std	6.693 7E-14	1.941 4E+03	1.303 5E+03	5.839 4E+03	7.301 7E+02	3.437 7E+03	2.521 1E-04	1.703 1E+02	8.724 6E-01
	Rank	1	6	7	9	5	8	2	4	3
F₄	Mean	3.468 0E+01	9.078 2E+01	8.782 8E+01	8.485 1E+01	6.042 3E+01	8.588 1E+01	9.399 3E+01	2.481 5E+01	6.625 9E+01
	Std	3.005 7E+01	1.756 6E+01	7.191 2E+00	2.284 8E-01	2.982 5E+01	1.125 1E+01	1.065 3E+01	2.899 5E+01	3.288 8E+01
	Rank	2	8	7	5	3	6	9	1	4
F₅	Mean	5.164 5E+01	1.219 6E+02	1.436 5E+02	5.821 6E+01	5.619 2E+01	4.168 8E+01	2.684 4E+01	5.691 2E+01	6.924 4E+01
	Std	1.271 9E+01	1.045 2E+01	1.222 4E+01	6.595 7E+00	1.421 6E+01	8.154 5E+00	9.639 3E+00	1.072 5E+01	1.516 7E+01
	Rank	3	8	9	6	4	2	1	5	7
F₆	Mean	3.549 2E-08	1.136 9E-13	1.114 6E-13	1.136 9E-13	8.931 7E-02	7.548 1E-06	3.575 1E-06	2.447 0E-01	7.279 1E-02
	Std	1.563 3E-07	0	1.591 9E-14	0	1.395 5E-01	3.988 0E-05	7.608 8E-06	8.162 0E-02	2.706 7E-01
	Rank	4	2	1	2	8	6	5	9	7
F₇	Mean	8.725 2E+01	1.587 0E+02	1.861 4E+02	9.972 5E+01	9.494 5E+01	7.244 8E+01	4.565 3E+01	8.910 6E+01	9.923 0E+01
	Std	1.466 9E+01	8.904 8E+00	9.520 3E+00	6.428 5E+00	1.987 9E+01	7.349 5E+00	8.393 2E+00	1.093 5E+01	2.284 6E+01
	Rank	3	8	9	7	5	2	1	4	6
F₈	Mean	5.396 4E+01	1.282 6E+02	1.484 9E+02	5.929 9E+01	5.394 2E+01	4.419 4E+01	2.668 8E+01	5.939 8E+01	6.350 1E+01
	Std	1.239 6E+01	1.138 0E+01	1.079 4E+01	6.078 8E+00	1.279 2E+01	6.583 4E+00	7.426 2E+00	1.066 3E+01	1.585 1E+01
	Rank	4	8	9	5	3	2	1	6	7
F₉	Mean	1.093 6E-01	9.492 3E-01	1.755 5E-03	4.012 5E-14	8.355 6E+01	3.083 9E-01	1.241 9E-02	7.826 7E+00	1.130 3E+02
	Std	1.740 0E-01	1.290 0E+00	1.253 6E-02	5.487 0E-14	6.264 3E+01	8.413 9E-01	6.551 0E-02	1.181 5E+01	1.011 7E+02
	Rank	4	6	2	1	8	5	3	7	9
F₁₀	Mean	2.553 2E+03	4.439 6E+03	6.785 2E+03	3.291 1E+03	2.325 3E+03	2.326 7E+03	1.971 7E+03	2.436 9E+03	3.283 7E+03
	Std	4.880 5E+02	2.200 9E+02	3.086 3E+02	2.728 4E+02	4.924 7E+02	2.845 7E+02	6.104 3E+02	4.454 2E+02	5.998 8E+02
	Rank	5	8	9	7	2	3	1	4	6
F₁₁	Mean	2.853 7E+01	5.334 7E+01	5.331 9E+01	3.743 0E+01	1.003 2E+02	4.134 3E+01	6.979 7E+01	2.961 2E+01	1.079 6E+02
	Std	2.041 6E+01	3.327 5E+01	3.000 7E+01	2.367 2E+01	4.310 1E+01	2.799 4E+01	2.794 5E+01	1.034 7E+01	4.660 0E+01
	Rank	1	6	5	3	8	4	7	2	9
F₁₂	Mean	8.512 7E+03	2.098 7E+06	7.184 9E+04	1.386 6E+05	6.862 9E+04	1.114 3E+06	2.064 1E+04	1.598 3E+04	5.157 4E+05
	Std	9.175 4E+03	1.400 7E+06	4.456 8E+04	9.209 7E+04	3.825 2E+04	8.142 2E+05	8.867 3E+03	4.043 4E+03	6.505 1E+05
	Rank	1	9	5	6	4	8	3	2	7
F₁₃	Mean	5.010 8E+01	1.030 8E+04	1.110 8E+04	8.126 5E+03	1.121 1E+04	4.606 3E+03	1.155 2E+04	2.045 0E+02	1.286 5E+04
	Std	2.426 2E+01	9.148 1E+03	1.387 6E+04	7.806 6E+03	1.053 5E+04	4.859 0E+03	1.100 2E+04	2.702 8E+01	9.090 5E+03
	Rank	1	5	6	4	7	3	8	2	9
F₁₄	Mean	5.499 5E+01	4.081 1E+04	3.449 2E+03	4.924 0E+03	4.323 8E+03	7.120 4E+04	1.863 3E+03	6.198 5E+01	5.316 5E+02
	Std	1.467 5E+01	3.111 7E+04	2.274 2E+03	3.290 2E+03	5.715 9E+03	5.932 3E+04	1.553 0E+03	8.664 7E+00	7.877 1E+02
	Rank	1	8	5	7	6	9	4	2	3
F₁₅	Mean	4.210 7E+01	1.519 0E+03	5.852 8E+03	4.994 4E+03	2.167 6E+03	2.201 3E+03	2.506 0E+03	5.163 4E+01	1.340 9E+03
	Std	3.254 0E+01	1.346 3E+03	5.709 0E+03	6.646 8E+03	3.017 8E+03	1.975 6E+03	3.159 9E+03	1.071 3E+01	1.638 5E+03
	Rank	1	4	9	8	5	6	7	2	3
F₁₆	Mean	4.516 2E+02	7.471 4E+02	8.411 8E+02	3.964 3E+02	5.607 2E+02	4.939 2E+02	5.143 2E+02	4.482 3E+02	8.792 4E+02
	Std	1.913 8E+02	1.433 0E+02	1.918 0E+02	1.193 2E+02	2.085 0E+02	1.730 9E+02	1.621 2E+02	1.344 3E+02	3.267 9E+02
	Rank	3	7	8	1	6	4	5	2	9
F₁₇	Mean	5.885 0E+01	1.725 5E+02	1.586 9E+02	8.164 2E+01	8.768 4E+01	1.411 6E+02	1.751 5E+02	6.954 4E+01	3.789 9E+02
	Std	2.351 5E+01	7.985 9E+01	3.909 2E+01	2.203 7E+01	9.128 9E+01	8.502 6E+01	6.268 8E+01	1.729 6E+01	2.372 5E+02
	Rank	1	7	6	3	4	5	8	2	9
F₁₈	Mean	8.919 1E+02	5.058 8E+05	4.251 1E+05	3.222 5E+05	1.003 4E+05	2.136 1E+05	6.702 6E+04	2.050 5E+02	2.719 7E+04
	Std	1.519 9E+03	2.393 0E+05	1.659 3E+05	1.219 7E+05	1.101 9E+05	1.326 1E+05	2.724 7E+04	4.753 6E+01	2.121 0E+04
	Rank	2	9	8	7	5	6	4	1	3
F₁₉	Mean	1.983 6E+01	1.201 6E+03	5.896 4E+03	8.368 6E+03	5.961 2E+03	2.072 3E+03	4.236 9E+03	2.997 7E+01	5.149 1E+03
	Std	6.642 0E+00	1.171 9E+03	7.992 5E+03	9.279 5E+03	7.111 2E+03	2.168 5E+03	3.223 9E+03	3.389 7E+00	4.760 1E+03
	Rank	1	3	7	9	8	4	5	2	6
F₂₀	Mean	9.986 9E+01	2.051 1E+02	1.542 3E+02	5.520 5E+01	1.298 9E+02	1.730 3E+02	1.960 3E+02	1.136 3E+02	3.029 1E+02
	Std	6.211 9E+01	9.801 8E+01	9.701 1E+01	3.541 3E+01	7.097 0E+01	7.201 5E+01	3.365 7E+01	5.241 1E+01	1.446 3E+02
	Rank	2	8	5	1	4	6	7	3	9
F₂₁	Mean	2.522 7E+02	3.314 2E+02	3.429 9E+02	2.595 0E+02	2.489 6E+02	2.504 7E+02	2.308 5E+02	2.545 8E+02	2.581 5E+02
	Std	1.153 9E+01	1.023 6E+01	1.094 4E+01	7.669 0E+00	1.319 5E+01	8.444 2E+00	9.077 0E+00	3.324 7E+01	1.564 0E+01
	Rank	4	8	9	7	2	3	1	5	6
F₂₂	Mean	1.000 0E+02	2.006 4E+03	1.000 0E+02	1.000 0E+02	1.022 8E+02	1.021 1E+03	1.000 0E+02	1.002 2E+02	1.914 7E+02
	Std	1.960 5E-13	2.233 3E+03	1.004 7E-13	2.310 0E-13	3.227 9E+00	1.287 2E+03	1.827 4E-13	4.391 7E-02	5.626 6E+02
	Rank	3	9	1	4	6	8	2	5	7
F₂₃	Mean	3.961 5E+02	4.716 4E+02	4.839 3E+02	4.032 3E+02	4.147 2E+02	4.024 7E+02	3.765 4E+02	3.895 9E+02	4.337 5E+02
	Std	4.460 9E+01	1.047 0E+01	1.220 9E+01	5.634 8E+00	1.874 2E+01	8.168 7E+00	1.295 4E+01	6.878 7E+01	2.764 0E+01
	Rank	3	8	9	5	6	4	1	2	7
F₂₄	Mean	4.919 9E+02	6.028 4E+02	5.664 8E+02	4.743 0E+02	4.816 9E+02	4.984 0E+02	4.445 8E+02	4.897 2E+02	4.927 7E+02
	Std	2.102 7E+01	1.297 3E+01	1.234 4E+01	6.005 5E+00	2.061 0E+01	1.389 9E+01	1.260 3E+01	1.659 7E+01	2.642 7E+01
	Rank	5	9	8	2	3	7	1	4	6
F₂₅	Mean	3.868 8E+02	3.873 4E+02	3.868 8E+02	3.869 1E+02	4.012 4E+02	3.877 9E+02	3.876 3E+02	3.837 4E+02	4.304 7E+02
	Std	5.332 6E-01	4.910 6E-01	5.092 3E-01	7.552 4E-02	1.948 9E+01	1.131 9E+00	4.084 3E-01	1.824 6E-01	2.850 3E+01
	Rank	3	5	2	4	8	7	6	1	9
F₂₆	Mean	8.693 4E+02	2.186 6E+03	2.006 8E+03	1.482 1E+03	1.734 4E+03	1.533 7E+03	4.335 8E+02	2.505 1E+02	1.401 0E+03
	Std	6.524 9E+02	4.370 8E+02	1.991 4E+02	7.201 5E+01	7.134 7E+02	1.905 1E+02	3.933 7E+02	4.111 2E+01	1.203 2E+03
	Rank	3	9	8	5	7	6	2	1	4
F₂₇	Mean	5.023 9E+02	5.090 4E+02	4.992 2E+02	4.980 7E+02	5.428 9E+02	5.074 4E+02	5.100 2E+02	5.128 6E+02	5.369 5E+02
	Std	1.103 9E+01	4.598 6E+00	7.983 4E+00	4.727 0E+00	1.708 6E+01	3.624 2E+00	1.101 0E+01	6.163 2E+00	1.463 8E+01
	Rank	3	5	2	1	9	4	6	7	8
F₂₈	Mean	3.296 1E+02	3.731 3E+02	3.840 9E+02	3.228 1E+02	3.325 7E+02	4.136 4E+02	3.871 8E+02	3.649 2E+02	5.207 6E+02
	Std	4.869 7E+01	5.294 7E+01	4.324 8E+01	3.788 0E+01	5.216 5E+01	2.557 7E+01	4.719 4E+01	3.247 7E+01	4.453 9E+01
	Rank	2	5	6	1	3	8	7	4	9
F₂₉	Mean	5.079 2E+02	7.727 6E+02	6.200 0E+02	5.185 1E+02	5.582 6E+02	5.477 8E+02	4.943 9E+02	5.438 5E+02	6.830 5E+02
	Std	5.261 8E+01	7.822 4E+01	6.108 3E+01	3.485 9E+01	1.004 0E+02	8.196 0E+01	4.454 2E+01	5.424 1E+01	1.857 9E+02
	Rank	2	9	7	3	6	5	1	4	8
F₃₀	Mean	2.352 6E+03	1.831 0E+04	5.756 5E+03	5.940 5E+03	5.014 7E+03	4.967 1E+03	4.066 7E+03	3.685 5E+03	5.778 6E+03
	Std	3.379 4E+02	1.051 6E+04	2.437 8E+03	2.315 8E+03	1.971 2E+03	2.093 4E+03	1.795 3E+03	3.304 2E+02	1.291 7E+04
	Rank	1	9	6	8	5	4	3	2	7
排名第1 的个数		11	0	2	5	0	0	8	4	0
平均排名		2.37	6.90	6.17	4.77	5.30	5.27	3.87	3.57	6.77
总排名		1	9	7	4	6	5	3	2	8

算法对比	p-value	R⁺	R^-	n/w/t/l
DDHBO vs HBO	0.000 002	464	1	30/29/0/1
DDHBO vs WRBBO	0.000 006	442	23	30/26/1/3
DDHBO vs DEBBO	0.000 660	390	75	30/22/1/7
DDHBO vs SaDE	0.000 053	429	36	30/26/0/4
DDHBO vs SE04	0.000 174	415	50	30/25/0/5
DDHBO vs HGWOP	0.029 770	333	132	30/19/1/10
DDHBO vs MEGWO	0.019 569	346	119	30/22/0/8
DDHBO vs MPSO	0.000 002	465	0	30/30/0/0

[1]	Kaiwen ZHAO, Peng WANG, Xiangrong TONG. Two-stage search-based constrained evolutionary multitasking optimization algorithm [J]. Journal of Computer Applications, 2024, 44(5): 1415-1422.
[2]	Xiuxi WEI, Maosong PENG, Huajuan HUANG. Node coverage optimization of wireless sensor network based on multi-strategy improved butterfly optimization algorithm [J]. Journal of Computer Applications, 2024, 44(4): 1009-1017.
[3]	Yaqin CHEN, Peng WANG. Potential barrier estimation criterion based on quantum dynamics framework of optimization algorithm [J]. Journal of Computer Applications, 2024, 44(4): 1180-1186.
[4]	Lingling GUO, Zhiqiang LI, Menghuan DUAN. Application of quantum approximate optimization algorithm in exact cover problems [J]. Journal of Computer Applications, 2024, 44(3): 849-854.
[5]	Bona XUAN, Jin LI, Yafei SONG, Zexuan MA. Malicious code classification method based on improved MobileNetV2 [J]. Journal of Computer Applications, 2023, 43(7): 2217-2225.
[6]	Zhihui GAO, Meng HAN, Shujuan LIU, Ang LI, Dongliang MU. Survey of high utility itemset mining methods based on intelligent optimization algorithm [J]. Journal of Computer Applications, 2023, 43(6): 1676-1686.
[7]	Heming JIA, Shanglong LI, Lizhen CHEN, Qingxin LIU, Di WU, Rong ZHENG. Remora optimization algorithm based on chaotic host switching mechanism [J]. Journal of Computer Applications, 2023, 43(6): 1759-1767.
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[9]	Haitao TANG, Hongjun WANG, Tianrui LI. Discriminative multidimensional scaling for feature learning [J]. Journal of Computer Applications, 2023, 43(5): 1323-1329.
[10]	Junxing XIANG, Yonghong WU. Hybrid salp swarm and butterfly optimization algorithm combined with neighborhood centroid opposition-based learning [J]. Journal of Computer Applications, 2023, 43(3): 820-826.
[11]	Lei TIAN, Lina GE. Advertising recommendation algorithm based on differential privacy [J]. Journal of Computer Applications, 2023, 43(11): 3346-3350.
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[13]	ZHENG Sai, LI Tianrui, HUANG Wei. Federated learning algorithm for communication cost optimization [J]. Journal of Computer Applications, 2023, 43(1): 1-7.
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算法	平均运行时间/s	排名
HBO	9.500 6	4
DDHBO	3.445 0	2
MEGWO	3.801 6	3
HGWOP	3.287 3	1

算法	平均运行时间/s	排名
HBO	9.500 6	4
DDHBO	3.445 0	2
MEGWO	3.801 6	3
HGWOP	3.287 3	1