Jul 2026· 2026 3rd World Conference on Computer and Information Security (WCCIS)· pp. 35-39· 0 citations· 33 references
Abstract
The Traveling Salesman Problem (TSP) is a classic NP-hard combinatorial optimization problem where traditional intelligent algorithms often suffer from slow convergence and premature convergence to local optima. This paper proposes a novel hybrid optimization framework (HGA-PSO-ACO) that integrates the global exploration capability of the Genetic Algorithm (GA), the rapid convergence characteristics of Particle Swarm Optimization (PSO), and the positive feedback mechanism of Ant Colony Optimization (ACO) through a three-stage adaptive coordination model. The key design challenge is to determine when each component should dominate the search and how information should be transferred across stages without causing premature homogenization of the population. To address this issue, the algorithm employs fitness variance (FVD) for real-time population state monitoring and implements dynamic algorithm switching strategies. Experimental validation on a 40-city TSP benchmark demonstrates that the hybrid algorithm achieves a 40% faster convergence speed, a 2.1% lower relative error, and a 58.3% reduction in run-to-run standard deviation compared to individual algorithms, indicating improved solution stability together with favorable parameter sensitivity control.
Since the cost function of the Quadratic Assignment Problem (QAP) is quadratic and its solution space expands linearly, this problem is computationally challenging. In other words, any problem that can be solved by an exact method on one medium- to large-scale instance should be practically intractable in general for the robust hybrid optimization algorithm, combining Genetic Algorithm (GA) with Cat Swarm Optimization (CSO), proposed in this study. It has both the characteristics of being aggressive with respect to exploration but conservative once a promising solution region is identified and introducing localized search operations for improving solution quality: A genetic algorithm is utilized to carry out recombination on the population. As for Cat Swarm Optimization, it adopts a two-way search model with the seeking mechanism and tracing mechanism applied. The new alternative structure, delta evaluation, uses permutational expression for the solutions. It can reduce computational complexity. The proposed algorithm was tested by running 30 independent trials on known QAPLIB benchmark instances, and then its results were analyzed using both Wilcoxon signed-rank tests and Friedman ranks. Experimental results confirm that in several cases, the hybrid GA (GA + CSO) framework provides good or even superior solution quality compared to classical metaheuristic methods. Median optimality gaps dropped and robustness increased. But the question of how to judge the overall performance, let alone prove that it’s not just a fluke (something relying on only a few tests), can only be decided after further study. This improvement—it is statistically confirmed at the 0.05 level—is significant. The proposed model is useful for solving large-scale NP-hard permutation-based combinatorial optimization. For similar problems and future research, it provides a flexible and scalable framework.
Firas Abdullah Attia, Iraq T. Abbas, R. A. Rahman· Ibn AL-Haitham Journal For P...· 0 citations
Combinatorial optimization problems (COPs), including the Knapsack Problem (KP), the Traveling Salesman Problem (TSP), and regional variants such as the Close-Enough Traveling Salesman Problem (CETSP), constitute fundamental models for addressing complex decision-making tasks in modern computational systems. Their computational difficulty has motivated the development of a broad range of exact, heuristic, metaheuristic, learning-based, and hybrid optimization approaches. This work presents a structured survey and problem-structure-oriented comparative analysis of these methods, covering Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), Tabu Search (TS), Artificial Bee Colony (ABC), Graph Neural Networks (GNNs), and learning-enhanced hybrid approaches. Particular attention is given to how differences in problem structure, formulation characteristics, feasibility requirements, computational cost, and evaluation settings influence the suitability of different optimization paradigms. The practical relevance of these approaches is discussed through two case studies based on our prior work: Virtual Machine Placement (VMP) in cloud computing, examined through a simplified single-resource 0–1 KP abstraction, and UAV trajectory optimization for data collection in Wireless Sensor Networks (WSNs), examined through TSP-, CETSP-, and neighborhood-based routing models. The reviewed evidence indicates that metaheuristic approaches can provide flexible search mechanisms in computationally challenging settings, while learning-based models may support rapid inference, prediction, initialization, or search guidance when appropriate training data and generalization conditions are available. Hybrid learning–optimization frameworks may be beneficial when their components address complementary limitations of the underlying problem, although their effectiveness remains dependent on problem structure, training requirements, computational budget, feasibility handling, and implementation design. In cloud environments, knapsack-based VMP formulations provide useful abstractions for resource-allocation decisions, whereas in UAV-assisted WSNs, routing models such as the TSP and CETSP provide structured representations of trajectory and data-collection decisions. Overall, this survey highlights the complementary strengths and limitations of modern optimization paradigms and emphasizes that method selection should be guided by problem structure, operational requirements, feasibility considerations, and computational constraints.
S. El Kafhali, Mohamed Abid, Mohamed Hanini· Mathematical and Computation...· 0 citations
Traditional Ant Colony Optimization (ACO) suffers from premature convergence, slow convergence speed, and insufficient path smoothness in three-dimensional (3D) unmanned aerial vehicle (UAV) path planning. This paper proposes a Metropolis-based dynamic exploration ACO algorithm (MACO) that introduces four complementary mechanisms: (1) probabilistic acceptance of inferior solutions via the Metropolis criterion to escape local optima; (2) a linearly decaying dynamic exploration rate to balance global exploration and local exploitation; (3) adaptive step size adjustment to improve late-stage search precision; and (4) adaptive pheromone evaporation (0.7 to 0.3) to regulate convergence. Comparative experiments are conducted in a 250× 250 continuous 3D environment with six algorithms over 30 independent runs. Results show that MACO achieves the best mean fitness of $367.80 \pm 3.12$ and the shortest path length of 338.42m with a 100% collision-free rate. The Wilcoxon rank-sum test confirms MACO's statistically significant superiority $(p<0.001)$.
Gui-Xin Wang, Baolu Gao, Yong-Qi Wang· IEEE Jordan Conference on Ap...· 0 citations
The dynamic scheduling of ready-mixed concrete constitutes a critical bottleneck in construction automation. Following the design science paradigm and informed by a systematic literature review, this study develops the multistrategy ant colony optimization (MSACO) algorithm, which integrates three mechanisms: adaptive pheromone evaporation, elite ant guidance, and genetic mutation. Empirical validation based on the road network of a major Chinese city (involving four batching plants, six customer sites, and a fleet of seven fuel vehicles and five electric vehicles) demonstrates that MSACO significantly outperforms algorithms including the genetic algorithm, ant colony optimization, particle swarm optimization, and multistrategy adaptive ant colony optimization in terms of solution accuracy, convergence speed, and stability. The proposed algorithm achieves an average reduction in distribution costs of 7.06%, with advantages reaching 10.7% under highly constrained conditions (
p
<
10
−
7
). The main contributions are threefold: it proposes a triple adaptive mechanism tailored for dynamic scheduling scenarios; formulates a mathematical model incorporating plant capacity, load limits, and electric vehicle range; and provides a quantifiable basis for the digital transformation of construction logistics.
Yang Guan, Ge Shi, Jie Yang et al.· Journal of construction engi...· 0 citations
Automated Guided Vehicles (AGVs) play a critical role in ensuring efficient and collision-free logistics within modern semiconductor manufacturing systems. However, navigating the highly complex layouts of fabrication plants presents significant challenges for traditional path-planning algorithms. While the standard Ant Colony Optimization (ACO) algorithm is robust, it often suffers from slow early-stage convergence and a tendency to stagnate in local optima due to initial blind searching. To address these limitations, this paper proposes an Improved Ant Colony Optimization (IACO) algorithm tailored for grid-based AGV routing. The proposed IACO enhances search efficiency by introducing a distance-guided heuristic function that exerts a strong directional pull towards the target, thereby minimizing blind exploration. Furthermore, a non-linear adaptive pheromone updating strategy is designed to dynamically balance global search capabilities with local exploitation speed. Simulation experiments conducted in an abstract semiconductor factory environment demonstrate that the IACO algorithm significantly outperforms traditional ACO. The proposed method not only generates shorter and smoother trajectories with minimized redundant turning maneuvers but also achieves an approximately 18% reduction in total path length and a 45% decrease in computational iterations. These improvements confirm that the IACO algorithm is highly effective for the real-time, dynamic scheduling demands of complex industrial logistics.
Xi-Lin Yang, Bo-Yang Zhang, Yan-Ting Ni· World Journal of Advanced En...· 0 citations
A Adaptive Genetic Algorithm (AGA) is designed to solve the Multi-Objective Flexible Job Shop Green Scheduling Problem (MO-FJGSP), which aims to minimize the makespan, total energy consumption, and total carbon emissions.
Ming-Yue Li, Lina Wang, Jun Wang et al.· Journal of Engineering, Proj...· 0 citations
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