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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 8
2408-2415

Improved genetic algorithm incorporating profit-loss picking strategy for solving TTP

Jiang Xiaojua,b,c
Tan Dailuna,b,c
Feng Shiqianga,b,c
a. School of Mathematics & Information, b. Sichuan Colleges & Universities Key Laboratory of Optimization Theory & Applications, c. Institute of Nonlinear Analysis & Applications, China West Normal University, Nanchong Sichuan 637009, China
DOI: 10.19734/j.issn.1001-3695.2024.12.0489

Abstract

TTP is a new type of combinatorial optimization problem, which is composed of TSP and KP. Its optimization mo-del covers the constraints of the two kinds of problems and also inherits the computational difficulty of the two kinds of problems. To solve the TTP, this paper proposed an improved genetic algorithm incorporating profit-loss picking strategy. For the list of items on the road of any traveler, it defined the value items and adopted the must-take strategy, defined the loss items for the remaining items and eliminated them, introduced the double score calculation formula for the eliminated remaining items, and conducted comprehensive sorting according to the mixed sorting strategy, and then selected them into the backpack in order. The whole processing process constituted the profit-loss taking strategy. For the genetic algorithm, this paper designed the strategy of population initialization based on nearest neighbor search and truncation exchange to improve the quality of the initial population. It used the stochastic universal sampling selection operator, the partial matching crossover operator and the secondary mutation operator to strengthen survival of the fittest and maintain the diversity of the population. It added reinsertion operators to keep the population stable. The simulation results show that the improved strategy can obviously improve the performance of the algorithm, and the result of the example reaches the expectation. The improved algorithm has good optimization ability and stability.

Foundation Support

国家自然科学基金资助项目(42204123)
四川省自然科学基金资助项目(2022NSFSC0558)
教育部产学合作协同育人项目(202102454008)
四川省教育厅重点教改项目(JG2021-959)
西华师范大学研究生教育改革研究项目(2022XM24)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.12.0489
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 8
Section: Algorithm Research & Explore
Pages: 2408-2415
Serial Number: 1001-3695(2025)08-021-2408-08

Publish History

[2025-03-13] Accepted Paper
[2025-08-05] Printed Article

Cite This Article

江晓菊, 谭代伦, 冯世强. 融合盈亏拿取策略的改进遗传算法求解TTP [J]. 计算机应用研究, 2025, 42 (8): 2408-2415. (Jiang Xiaoju, Tan Dailun, Feng Shiqiang. Improved genetic algorithm incorporating profit-loss picking strategy for solving TTP [J]. Application Research of Computers, 2025, 42 (8): 2408-2415. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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