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Aiming at the issues of ant colony algorithm in solving large-scale traveling salesman problems (TSP), such as easily falling into local optima and slow convergence speed, an ant colony algorithm integrating adaptive clustering and mother ant guidance strategy (AMACS) is proposed. In the adaptive clustering process, firstly, an improved clustering method is used, which leverages the concepts of maximum-minimum distance and class density to obtain the optimal clustering results through an adaptive clustering strategy, and quickly obtains the optimized solution for each cluster. Next, the neighboring clusters are fused using a spider web fusion principle, effectively enhancing the accuracy of the initial solution. Additionally, the initial solution is optimized through the mother ant guidance strategy, which includes two components: path guidance and pheromone optimization. Path guidance sets the initial solution as the solution for the first generation, improving the stability of the algorithm; pheromone optimization involves stimulating the pheromone along the initial solution’s path, thereby enhancing the solution’s accuracy. Finally, a random recombination strategy is employed to reorganize and randomly stimulate the pheromones, helping the algorithm to escape local optima and improving the solution’s accuracy. Experimental results show that the proposed algorithm not only ensures solution accuracy when solving large-scale TSPs but also improves the stability of the algorithm.

In order to solve the problems of slow convergence speed and easy to fall into local optimum of tradi-tional ant colony algorithm in solving traveling salesman problem (TSP), the dual ant colony algorithm based on backtracking migration and matching learning (BMACS) is proposed. Firstly, the population is dynamically graded into exploration ants and tracking ants. The ants with higher fitness are exploration ants and the ones with lower fitness are tracking ants. Secondly, a local feature transfer mechanism is proposed, in which there are two strategies: in the feature transfer strategy, the pheromone on the common path of exploration ants is rewarded, and this path is transferred into the pheromone matrix as a local feature, so as to improve the influence of exploration ants and acce-lerate the convergence speed of the algorithm; in the mutation learning strategy, the tracking ants are responsible for exploring the suboptimal path, and adaptively reconstruct the path of exploration ants to enrich the diversity of the population. Finally, when the algorithm stagnates, the matching learning mechanism is used to communicate and learn between the current optimal individual and the historical optimal individual with the highest similarity, and the pheromone is reorganized to increase the diversity of the population, thus improving the ability of the algorithm to jump out of the local optimal. Simulation experiments are carried out with MATLAB for multiple sets of cases in TSPLIB, and the results show that the improved algorithm balances diversity and convergence speed, and effecti-vely improves the quality of solution.

BACKGROUND/AIM:Cytochrome P450 epoxygenase is a major enzyme involved in the metabolism of ω-3 polyunsaturated fatty acids (PUFAs) to produce biologically active ω-3 epoxy fatty acids (ω-3 epoxides). In general, all epoxy PUFAs including ω-3 epoxides are quickly metabolized/inactivated by soluble epoxide hydrolase (sEH) to form diol products. The aims of this study were to determine the effect and mechanism of fat-1 transgene, and ω-3 PUFA combined with sEH gene knockout or inhibitor on inhibiting pancreatic cancer and the related mechanisms involved. MATERIALS AND METHODS:PK03-mutant KrasG12D murine pancreatic carcinoma cells were inoculated into mouse models including fat-1, sEH-/- and C57BL/6J mice. The mice were fed with AIN-76A diet with or without ω-3 PUFA supplementation or treated with sEH inhibitor. In addition to tumor growth (tumor size and weight), cell proliferation, mutant Kras-mediated signaling, inflammatory reaction and angiogenesis were analyzed immunohisto-chemically and by western blot assay. ω-3 PUFA metabolism, particularly focusing on ω-3 epoxy fatty acids (ω-3 epoxides), was measured using a liquid chromatography with tandem mass spectrometry (LC-MS/MS) approach. RESULTS:Significant decreases of weight and size of the PK03 pancreatic carcinoma were observed in the fat-1 transgenic mice treated with sEH inhibitor compared to those of C57BL/6J control mice fed with AIN-76A diet (weight: 0.28±0.04 g vs. 0.58±0.06 g; size: 187.0±17.5 mm3 vs. 519.3±60.6 mm3). In a separate experiment, sEH-/- mice fed ω-3 PUFA supplement and C57BL/6J mice treated with sEH inhibitor and fed ω-3 PUFA supplement exhibited a significant reduction in the weight and size of the pancreatic carcinoma compared to C57BL/6J control mice (weight: 0.26±.26 g and 0.39±.39 g vs. 0.69±0.11 g, respectively; size: 274.2±36.2 mm3 and 296.4±99.8 mm3 vs. 612.6±117.8 mm3, respectively). Moreover, compared to the pancreatic tumors in C57BL/6J control mice, the tumors in fat-1 transgenic mice treated with sEH inhibitor showed a significant less inflammatory cell infiltrate (62.6±9.2/HPF (high power field) vs. 8.0±1.2/HPF), tumor cell proliferation (48.5±1.7% vs. 16.5±1.6%), and angiogenesis (micro-vessel density (MVD): 35.0±1.0 vs. 11.1±0.5) immunohistochemically, as well as significantly increased caspase-3 labeled apoptosis (0.44±0.06% vs. 0.69±0.06%, respectively). Using western blot approach, significant inhibition of mutant Kras-activated signals including phosphorylated Serine/threonine kinases (cRAF), Mitogen-activated protein kinase kinase (MEK), and extracellular signal-regulated kinase (ERK) were identified in pancreatic carcinoma of fat-1 transgenic mice treated with sEH inhibitor. Eicosanoic acid metabolic profiling of the serum specimens detected a significant increase of the ratios of epoxides to dihydroxy fatty acid (DiHDPE) for docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), and epoxides/dihydroxy octadecenoic acid (DiHOME) for arachidonic acid (ARA) and linoleic acid (LA), as well as a significant increase of epoxy metabolites of DHA, EPA, ARA and LA in fat-1 transgenic mice treated with a sEH inhibitor. CONCLUSION:ω-3 epoxy products from ω-3 PUFA metabolism play a crucial role in inhibiting pancreatic cancer growth, and use of ω-3 PUFAs combined with sEH inhibition is a strategy with high potential for pancreatic cancer treatment and prevention.

In order to solve the problems of traditional ant colony algorithm in traveling salesman problem (TSP), such as easy to fall into local optimum and slow convergence speed, this paper proposes an ant colony algorithm based on price fluctuation strategy and dynamic backtracking mechanism. In the price fluctuation strategy, the complete iteration period of ant colony algorithm is classified according to the idea of time series. According to the balance of price fluctuation, the supply-demand relationship that affects the price fluctuation is matched. By analyzing different demands of the algorithm in different classifications, the pheromone volatilization factor is adaptively and dynamically supplied to accelerate the convergence speed of the algorithm and ensure the diversity of the algorithm solution. When the supply relationship of the price fluctuation strategy can not be balanced, the algorithm will face local optimization problem. In this case, the dynamic backtracking mechanism is introduced to trace the path pheromone to the period of significant similarity difference based on the individual similarity of iterative optimal ant, which can effectively jump out of the local optimum while ensuring the convergence speed. Different test sets in TSP are simulated by MATLAB. The results show that the algorithm can effectively improve the quality of solution and balance the relationship between diversity and convergence speed on the basis of ensuring the convergence speed in medium and large scale city set.

AbstractThis article presents the application of the Carrera Unified Formulation (CUF) for dynamic analysis and structural integrity assessment of advanced nanocomposite-reinforced tunnel structures. CUF offers a generalized framework that simplifies the modeling of complex structures, providing an efficient approach to address the dynamic behavior of nanocomposites. Advanced nanocomposites, which enhance mechanical performance and durability, are increasingly used in critical infrastructure such as tunnel systems. Traditional methods often fall short in capturing the intricate interactions within these materials, particularly under dynamic loads. CUF overcomes these challenges by incorporating higher-order theories and multi-scale analysis, allowing for accurate prediction of displacements, stresses, and potential failure modes. To further validate the results, an Artificial Neural Network (ANN) model is trained using simulated data, ensuring robust predictions for various loading conditions. The ANN assists in approximating the dynamic response and integrity of the structure, enabling real-time assessments with minimal computational expense. The combined CUF-ANN approach demonstrates high accuracy and efficiency, making it a reliable tool for the structural integrity assessment of nanocomposite-reinforced tunnels. This study highlights the significant potential of integrating CUF and ANN for the analysis and design of advanced engineering structures subjected to dynamic environmental conditions. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the ant colony algorithm has slow convergence rate and poor diversity in solving traveling salesman problem (TSP), double ant colony algorithm based on collaborative mechanism and dynamic regulation strategy is proposed. Firstly, the ant colony is dynamically divided into guide ants and cooperative ants according to fitness value, so as to form a heterogeneous double ant colony. Secondly, the heterogeneous double ant colony adopts the collaborative mechanism to balance the diversity and convergence rate of the algorithm: the guide ant introduces the propagation factor in the path construction, which increases the probability of the ant choosing a new path, expands the search range, and improves the diversity of the algorithm. The cooperative ant is guided by the optimal path of the guide ant. When the path similarity reaches the threshold, the cooperative operator is started to accelerate the convergence speed. Finally, the dynamic regulation strategy is introduced, the adaptive control operator is introduced when the global pheromone is updated, and the pheromone of the global optimal path is positively stimulated or reverse-penalized, so as to accelerate the convergence speed and avoid the algorithm falling into the local optimal. The experimental results of solving the TSP test set show that the improved algorithm not only improves the quality of solutions, ensures the diversity of algorithms, but also speeds up the convergence speed of the algorithm, especially in large-scale urban problems.

In order to solve the problems of low precision and premature convergence of traditional ant colony system in solving traveling salesman problem (TSP), this paper proposes a dynamic hierarchical ant colony system which integrates reward and punishment learning strategy (DHL-ACS). Firstly, the ant colony is dynamically divided into empire ants, colony ants and king ant, in which empire ants and colony ants perform local pheromone update, and king ant performs global pheromone update. In local pheromone update, the empire ants perform larger weight coefficient, which is responsible for the development of better solution to enhance the guidance of algorithm, and the colony ants perform small weight coefficient to explore the suboptimal solution and to ensure the diversity of algorithm. In addition, the method of exchanging high-quality solutions between empire ants and colony ants is used to improve the accuracy of the solutions. Secondly, an improved learning strategy is proposed, which realizes the assimilation of the better solution by rewarding the common path of empire ants and colony ants, and improves the convergence speed of the algorithm. Furthermore, when the algorithm stops, the feedback operator is introduced to reduce the pheromone concentration on the path of king ant, which can punish the path of higher pheromone, so as to improve the diversity of the population and enhance the ability of the algorithm to jump out of the local optimum. Through the experiments of several sets of TSP instances, it is shown that the improved algorithm balances the relationship between convergence rate and diversity, especially for large-scale TSP problems, which can effectively improve the accuracy of solutions.

Aiming at the problems of traditional ant colony algorithm in solving traveling salesman problem (TSP), such as slow convergence speed and easy to fall into local optimum, an ant colony optimization algorithm (RCACO) based on dynamic recombination and cooperative communication strategy is proposed. Firstly, the ant colony is divided into greedy ant colony and exploratory ant colony. The two ant colonies implement different path construction rules and pheromone update strategies to balance the convergence speed and diversity of the algorithm. Secondly, a new dynamic recombination operator based on the clew binary tree is adopted, and the solution set is dynamically recombined according to different strategies to improve the diversity of the algorithm. Furthermore, a collaborative communication strategy based on similarity and potential value is proposed. From a global perspective, the path with the most potential to become the optimal solution is found, and pheromone rewards are given to these paths to improve the convergence speed of the algorithm. Finally, the algorithm also adds a stagnation avoidance strategy to help the ant colony jump out of the local optimum and improve the accuracy of the algorithm. Through the Matlab simulation experiment of multiple groups of cases in TSPLIB, compared with the traditional ant colony algorithm and other optimization algorithms, the simulation results show that the improved ant colony algorithm significantly improves the convergence speed and solution accuracy.

When solving traveling salesman problem (TSP), the ant colony algorithm is easy to fall into local optimum and convergence speed is slow, a double-type ant colony algorithm considering dynamic guidance and neighborhood interaction is proposed. Firstly, the algorithm combines the dynamic guidance strategy to increase the dynamic pheromone belonging to the path of the largest spanning tree in the early iteration, thereby effectively increasing the diversity of the population; adding the dynamic pheromone belonging to the path of the minimum spanning tree in the late iteration to accelerate the convergence speed. Furthermore, the ants are divided into two categories, which are integrated into the neighborhood interaction strategy, and the second type of ants improve the state transition and the local pheromone update formula through the attraction factors, and use the MMAS (max-min ant system) pheromone restriction strategy, which not only improves the convergence, but also prevents the algorithm from early stagnation. The experimental results of solving the TSP test set and compared with other improved ant colony algorithms show that the improved algorithm can effectively accelerate the convergence speed and avoid falling into local optimum, thus obtaining a more accurate solution, especially in the case of large-scale TSP problems, the effect is more significant.

Aiming at the problems that the ant colony algorithm is easy to fall into local optimum and the convergence speed is slow in traveling salesman problem (TSP), dynamic grouping ant colony algorithm combined with cat swarm optimization is proposed. Firstly, the ants are distributed manually in different cities evenly when the population is initialized. Secondly, based on the division of labor in the cat swarm optimization, a dynamic grouping mechanism is introduced into the ant colony system to classify the ants into two types: search ants and tracking ants. Search ants increase the algorithm??s diversity in the early stage by improving the path construction rules, tracking ants use pheromone diffusion mechanism to adaptively update local pheromone to highlight the role of superior sub-paths, and avoid the algorithm falling into local optimum. Finally, the convergence speed is accelerated by the pheromone global update mechanism. Simulation experiments are carried out on multiple sets of instances in TSPLIB through Matlab. The simulation results show that the improved algorithm balances the diversity and conver-gence speed, and effectively imporves the quality of the solutions.

A heterogeneous two-population ant colony algorithm based on heuristic reinforcement learning is pro-posed to solve the problem that traditional ant colony algorithm is prone to fall into local optimum and convergence speed is slow when solving TSP. Ant colony can be divided into main population and sub-population.The main population is responsible for solution construction and pheromone update. The sub-population replaces the solution set of the main population while constructing the solution. At the beginning of the algorithm, heuristic operator is used to control the communication frequency of two populations adaptively, and the solution exchange mode is controlled by the deviation degree coefficient. At the early stage, the optimal solution of the subpopulation is allowed to replace the random solution of the main population, increasing the diversity of solutions. Meanwhile, the reinforcement learning mechanism is introduced to reward the pheromones in the optimal path of the main population adaptively, so as to increase the probability of the optimal public path selected later. In the later stage, the optimal solution of the sub-population is controlled to replace the worst solution of the main population, the quantity of pheromones in the optimal path is strengthened, and the pheromones in the optimal path of the main population are rewarded, so as to further improve the convergence speed of the algorithm. The simulation results show that the algorithm can effectively jump out of the local optimum, and the quality of the solution is obviously improved in the large-scale test set.

Due to importance of exploring novel ways to protect systems from detrimental influences of thermal shock phenomenon, this study aims at analyzing the transient coupled poro/thermoelasticity response of functionally graded CNT reinforced (FG-CNTR) nanocomposite panels surrounded by non-polynomial kind of elastic foundation. In addition to thermal shock, the mentioned nanocomposite structure is subjected to initial mechanical loads in different orientations. As the accuracy of the results is of high prominence to guarantee reliability of the practical designs, the governing equations are developed on the basis of the exact type of elasticity theory known as three-dimensional form. Also, analytical solution is determined by applying Navier approach to solve the governing differential equations for simply-supported boundaries. Energy balance equation is utilized to determine the temperature gradient of the cylindrical panel considering the assumptions that the internal surface is fully isolated and the external one is subjected to thermal shock loading. Modified type of Dunber and Abate's approach is implemented to inverse the Laplace transform in order to map the response of the system from Laplace into time domain. Convergence of the determined deflection, shear stresses, and temperature versus passing time is analyzed and confirmed. The acquired numerical results elucidate the role of different factors such as mid-radius to thickness ratio, volume-fraction, and scattering pattern of CNT in the transient coupled poro/thermoelasticity response of the FG-CNTR nanocomposite cylindrical panel. As a valuable result, it is found that increasing the volume-fraction of CNT applies negative impacts on the variation of the transverse shear stress. [ABSTRACT FROM AUTHOR]

This paper proposes an improved ant colony system with adaptive strategies, called α