Heuristic Search for Combinatorial Optimization Problems and Symmetry Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 760

Special Issue Editors


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Guest Editor
School of Computer Science and Information Technology, Northeast Normal University, Changchun 130024, China
Interests: artificial intelligence; combinatorial optimization problem solving; path planning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science and Information Technology, Northeast Normal University, Changchun 130024, China
Interests: chip scheduling; broadcast resource scheduling; influence maximization problem; graph theory problem

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Guest Editor
Department of Mathematics, Shanghai University, Shanghai 200444, China
Interests: combinatorial geometry; global optimization; symbolic computation and computer algebra; machine proof; algorithms in artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Symmetry and asymmetry are present in various combinatorial optimization problems. The combinatorial optimization is integral to solving complex decision-making problems that arise in diverse areas such as logistics, telecommunications, manufacturing, and artificial intelligence. This Special Issue focuses on heuristic search techniques, which are essential tools for tackling the computationally intensive nature of combinatorial optimization problems. Unlike exact algorithms, which guarantee an optimal solution but may be impractical for large-scale problems due to their high computational demands, heuristic approaches provide near-optimal solutions within a reasonable timeframe, making them invaluable in real-world applications. This collection of papers explores various heuristic methods, including but not limited to genetic algorithms, simulated annealing, tabu search, and ant colony optimization. These methods are evaluated on a range of classical and emerging combinatorial optimization problems. This Special Issue also highlights advancements in hybrid approaches that combine heuristics with exact methods, offering a balanced trade-off between solution quality and computational efficiency.

Dr. Shuli Hu
Dr. Yupeng Zhou
Prof. Dr. Zhenbing Zeng
Guest Editors

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Keywords

  • heristic search
  • exact method
  • local search
  • complex optimization
  • multi-objective optimization
  • heuristic search algorithm applications

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Published Papers (1 paper)

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Research

19 pages, 897 KB  
Article
The Circle Group Heuristic to Improve the Efficiency of the Discrete Bacterial Memetic Evolutionary Algorithm Applied for TSP, TRP, and TSPTW
by Ali Jawad Ibada, Boldizsár Tüű-Szabó and László T. Kóczy
Symmetry 2025, 17(10), 1683; https://doi.org/10.3390/sym17101683 - 8 Oct 2025
Viewed by 271
Abstract
The quality of the initial population is a critical factor in the convergence speed and overall performance of an optimization algorithm. A well-structured initial population can significantly enhance the exploration capabilities of the algorithm, allowing it to more efficiently traverse the solution space [...] Read more.
The quality of the initial population is a critical factor in the convergence speed and overall performance of an optimization algorithm. A well-structured initial population can significantly enhance the exploration capabilities of the algorithm, allowing it to more efficiently traverse the solution space and converge more quickly and reliably towards optimal or near-optimal solutions. In this paper, we present the Circle Group Heuristic (CGH), a spatially structured initialization method, for generating high-quality initial populations to enhance the convergence speed of the Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) in solving the Traveling Salesman Problem (TSP) and related combinatorial optimization problems. This work extends the CGH beyond the TSP to a broader class of routing problems. The results show that the integration of CGH into DBMEA demonstrated consistent performance improvements on the TSP, the Traveling Repairman Problem (TRP), and the Traveling Salesman Problem with Time Window (TSPTW) instances of varying sizes. In particular, CGH provided high-quality starting points that accelerated convergence and reduced computational cost. In all tested scenarios, DBMEA enhanced with CGH and consistently preserved the best-known solution quality while reducing execution time. Full article
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