A hybrid genetic algorithm to the program optimization model based on a heterogeneous network

doi:10.23919/JSEE.2025.000063

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (4): 994-1005.doi: 10.23919/JSEE.2025.000063

• SYSTEMS ENGINEERING • Previous Articles

A hybrid genetic algorithm to the program optimization model based on a heterogeneous network

Hang CHEN¹(), Yajie DOU¹^,*(), Ziyi CHEN¹(), Qingyang JIA¹(), Chen ZHU²(), Haoxuan CHEN²()

¹ College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
² College of Military Basic Education, National University of Defense Technology, Changsha 410073, China

Received:2022-11-11 Online:2025-08-18 Published:2025-09-04
Contact: Yajie DOU E-mail:hangchen_nudt@163.com;yajiedou_nudt@163.com;chenziyinudt@163.com;cassie_qing@163.com;zhuchen0923@163.com;chx15111159506@163.com
About author:
CHEN Hang was born in 1999. He received his B.S. degree from National University of Defense Technology, Changsha, China, in 2022. He is pursing his M.D. degree in National University of Defense Technology. His research interests are system optimization and comprehensive integration technology. E-mail: hangchen_nudt@163.com

DOU Yajie was born in 1987. He received his Ph.D. degree in management science and engineering from National University of Defense Technology, Changsha, China, in 2016. He is currently an associate professor of College of Systems Engineering, National University of Defense Technology. His research interests are weapon alternative decision and effectiveness evaluation, system-of-systems architecting and engineering management, and portfolio decision analysis.E-mail: yajiedou_nudt@163.com

CHEN Ziyi was born in 1995. He received his Ph.D. degree from National University of Defense Technology, Changsha, China, in 2023. He is currently a lecturer of College of Systems Engineering, National University of Defense Technology. His research interests are complex systems and system portfolio selection and optimization.E-mail: chenziyinudt@163.com

JIA Qingyang was born in 1998. She received her B.M. degree in financial engineering from Hunan University, Changsha, China, in 2020. She is currently pursuing her Ph.D. degree in management science and engineering with National University of Defense Technology. Her research interests is portfolio decision analysis. E-mail: cassie_qing@163.com

ZHU Chen was born in 2003. She is an undergraduate student in National University of Defense Technology, Changsha, China. Her research interests are defense acquisition and systems engineering.E-mail: zhuchen0923@163.com

CHEN Haoxuan was born in 2002. He is an undergraduate student in National University of Defense Technology, Changsha, China. His research interests are defense acquisition and systems engineering.E-mail: chx15111159506@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (7247011890; 72431011).

Abstract

Abstract:

Project construction and development are an important part of future army designs. In today’s world, intelligent warfare and joint operations have become the dominant developments in warfare, so the construction and development of the army need top-down, top-level design, and comprehensive planning. The traditional project development model is no longer sufficient to meet the army’s complex capability requirements. Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effectiveness. At the same time, when a program consists of large-scale project data, the effectiveness of the traditional, precise mathematical planning method is greatly reduced because it is time-consuming, costly, and impractical. To solve above problems, this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algorithm and verifies the effectiveness and feasibility of the model and algorithm through an example. The results show that the hybrid algorithm proposed in this paper is better than the existing meta-heuristic algorithm.

Key words: program optimization, heterogeneous network, genetic algorithm, portfolio selection

Hang CHEN, Yajie DOU, Ziyi CHEN, Qingyang JIA, Chen ZHU, Haoxuan CHEN. A hybrid genetic algorithm to the program optimization model based on a heterogeneous network[J]. Journal of Systems Engineering and Electronics, 2025, 36(4): 994-1005.

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Instance	Program size	Objective dimension
Instance1	300	4
Instance2	400	4
Instance3	500	2
Instance4	500	3
Instance5	500	4
The planning horizon is 30 years

Method	Parameter	Range	Value
NSGA-II	Size	[100,500]	382
NSGA-II	Mutants	[0.1,0.4]	0.10
MOEA/D	Size	[100,500]	126
MOEA/D	Subproblems	[10,35]	20
BRK-NSGA	Size	[100,500]	151
	Elites	[0.1,0.5]	0.3
	Mutants	[0.1,0.4]	0.10
	Non-elite parent	[1,3]	2
	Elite parent	[2,8]	6

Instance	NSGA-II		MOEA/D		BRK-NSGA
Instance	Effective	Pareto	Effective	Pareto	Effective	Pareto
Instance 1	15.3	49.4	20.2	52.9	17.4	87.1
Instance 2	11.4	31.8	17.4	58.4	15.2	85.4
Instance 3	31.2	50.5	25.0	73.0	24.8	88.6
Instance 4	18.4	29.9	22.5	62.7	18.8	86.7
Instance 5	8.4	18.5	12.3	53.3	13.2	91.4

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A hybrid genetic algorithm to the program optimization model based on a heterogeneous network

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