Network Optimization Method Based on Improved Quantum Genetic Algorithm

This chapter investigates a kind of whole new approach to spectrum sharing in cognitive radio (CR) system, quantum-inspired approach. First, we improved Quantum Genetic Algorithm (QGA) by adding quantum crossover operator to overcome the shortcomings of the original QGA, easily falling into a local extremum when used to optimize the continuous functions with many extrema. We then propose a novel spectrum sharing scheme in noncooperative game for CR system based on our improved QGA. We use QGA as competitive strategies and provide simulation results that confirm the proposed schemes have better convergence rate and higher sum capacity than genetic algorithm (GA)-based scheme, namely at most 1 bit/s/Hz increase in capacity. The results demonstrate the effectiveness and the applicability of QGA in spectrum sharing in CR system.

ABSTRACTSelf‐interference (SI) suppression has always been critical for simultaneous transmit and receive (STAR) systems. To address the strong coupling between nearby transmit and receive antennas, this study employs an improved quantum genetic algorithm (IQGA) to eliminate SI by combining digital self‐interference cancellation (SIC) and adaptive beamforming (ABF). Through digital SIC, both transmit noise and transmit signals are suppressed. The objective of the transceiver beamformer is to further reduce SI through SIC while simultaneously achieving high transceiver gain in the desired direction. IQGA represents the weights of feasible transceiver beams using chromosomes. By updating the population through quantum rotation, quantum crossover and quantum mutation strategies, IQGA demonstrates improvements in convergence accuracy, convergence speed, and reliability. In comparison to traditional ABF approaches, IQGA eliminates the need for complex matrix calculations and numerical derivations, thereby simplifying the solution process. The simulation results indicate that by cancelling the SI component through SIC and ABF, an isolation of 159.78 dB can be achieved at a transmit power of 1000 W. This represents an improvement of 40.32 dB compared to SIC alone and 113.87 dB compared to scenarios without SIC and ABF.

In the process of trajectory tracking using the linear quadratic regulator (LQR) for driverless wheeled tractors, a weighting matrix optimization method based on an improved quantum genetic algorithm (IQGA) is proposed to solve the problem of weight selection. Firstly, the kinematic model of the wheeled tractor is established according to the Ackermann steering model, and the established model is linearized and discretized. Then, the quantum gate rotation angle adaptive strategy is optimized to adjust the rotation angle required for individual evolution to ensure a timely jumping out of the local optimum. Secondly, the populations were perturbed by the chaotic perturbation strategy and Hadamard gate variation according to their dispersion degree in order to increase their diversity and search accuracy, respectively. Thirdly, the state weighting matrix Q and the control weighting matrix R in LQR were optimized using IQGA to obtain control increments for the trajectory tracking control of the driverless wheeled tractor with circular and double-shifted orbits. Finally, the tracking simulation of circular and double-shifted orbits based on the combination of Carsim and Matlab was carried out to compare the performance of LQR optimized by five algorithms, including traditional LQR, genetic algorithm (GA), particle swarm algorithm (PSO), quantum genetic algorithm (QGA), and IQGA. The simulation results show that the proposed IQGA speeds up the algorithm’s convergence, increases the population’s diversity, improves the global search ability, preserves the excellent information of the population, and has substantial advantages over other algorithms in terms of performance. When the tractor tracked the circular trajectory at 5 m/s, the root mean square error (RMSE) of four parameters, including speed, lateral displacement, longitudinal displacement, and heading angle, was reduced by about 30%, 1%, 55%, and 3%, respectively. When the tractor tracked the double-shifted trajectory at 5 m/s, the RMSE of the four parameters, such as speed, lateral displacement error, longitudinal displacement error, and heading angle, was reduced by about 32%, 25%, 37%, and 1%, respectively.

E-commerce has become the mainstream consumption mode for people, and with the continuous increase of online shopping business volume, controlling logistics costs has become an urgent problem to be solved. In order to solve the challenges in vehicle scheduling and achieve a scientific and reasonable vehicle scheduling scheme, this research is based on Hadoop Big data platform, introduces the concept of time axis, and builds a vehicle scheduling model based on Hadoop intelligent platform. Based on quantum genetic algorithm and combined with MapReduce model, a quantum improved genetic algorithm is constructed to solve vehicle scheduling optimization problems. The results show that the traditional quantum genetic algorithm converges after 20 iterations, achieving an optimal value of 575 and taking 360 seconds. The improved quantum genetic algorithm converged after 10 iterations and achieved an optimal value of 675, taking 200 seconds. Compared with quantum genetic algorithm, the improved quantum genetic algorithm reduces the time spent by 44.4%. Selecting customer data from a certain logistics company for testing, the improved algorithm shortens the delivery time and achieves the design of the optimal path scheduling plan. This study optimized transportation routes and resource scheduling, reduced transportation costs, and played an important role in optimizing vehicle scheduling in the logistics industry.

Parameters tuning is the key of the design of PID controller, for the PID parameters tuning, a PID parameters tuning method based on improved quantum genetic algorithm is proposed in this study. This study proposes an improved genetic algorithm (IQGA) which introduces a dynamic adjustment strategy of population and search intervals to improve the convergence speed, search ability and stability of quantum genetic algorithm-based PID parameters tuning method. Experimental results show that the proposed algorithm can obtain a good performance in PID parameters tuning problem.

Based on panel data from 31 Chinese provinces (autonomous regions, municipalities) from 2012 to 2023, this study constructs an adaptability index between the specialty structure and industrial structure encompassing three dimensions: scale, structure, and quality. It employs a two-way fixed effects model and a panel threshold model for empirical testing. The findings are as follows: (1) The depth of industry-education integration, the mechanism for dynamic specialty adjustment, and the degree of industry-enterprise participation all have significant positive effects on adaptability, with the dynamic specialty adjustment mechanism having the largest effect size (β=0.287). (2) The impact of industry-education integration exhibits a single threshold effect (threshold value of 0.624). After crossing this threshold, its marginal effect increases from 0.142 to 0.305, effectively doubling.This research provides important empirical evidence and policy implications for deepening industry-education integration and establishing a precise and flexible dynamic specialty adjustment mechanism.

The quantum genetic algorithm is improved for the flexible shop scheduling problem. In this paper, a quantum genetic algorithm adaptively adjusting the rotation angle is designed to solve the flexible shop scheduling problem. The randomness and stability tendency of the cloud model are used to adaptively adjust the rotation angle to improve the optimization ability of the algorithm. At the same time, quantum crossover and quantum variability are used to improve population diversity and prevent premature convergence. Numerical experiments show that the algorithm has good performance.

To overcome the shortcomings of precocity and being easily trapped into local optimum of the standard quantum genetic algorithm (QGA) , Information Technology in An Improved Quantum Genetic Algorithm based on dynamic adjustment of the quantum rotation angle of quantum gate (DAAQGA) was proposed. Mutation operation using the quantum not-gate is also introduced to enhance the diversity of population. Chaos search are also introduced into the modified algorithm to improve the search accuracy. Simulation experiments have been carried and the results show that the improved algorithm has excellent performance both in the preventing premature ability and in the search accuracy.

In recent years, deep neural network algorithm has shown great potential in the fields of economy, industry, finance, academia and other fields. Its deep information processing idea, which highly simulates human brain, has been applied in various fields. In this study, the deep neural network algorithm is used to reveal the operating mechanism of dynamic value co-innovation in the context of shared economy. It provides solutions to the problems of the dynamic fitting relationship between dynamic value creation process and factors in the context of shared economy, the influence of multi-participants in dynamic environment caused by role changes and the construction of dynamic adaptive adjustment mechanism of weight parameters, in order to provide ideas for the construction of a new framework model for the mechanism of sharing dynamic economic value.

In order to optimize large-scale online data stream clustering quality and algorithm performance, a fast clustering optimization method is proposed based on line data flow evolution incentive. Firstly, in the online stage, the high density partition algorithm is used to generate micro cluster set, and the set takes the snapshot window as the time scale. The micro cluster set is updated in real-time. In the offline stage, the micro cluster set is read, and the improved quantum genetic algorithm and the evolution incentive function are used for the iterative optimization in clustering center. Finally, the adaptive mutation operator is taken as the optimal variation operation for the populations. The global search ability of the algorithm is improved. Simulation results show that algorithm has better clustering accuracy, and the convergence speed is high with less memory cost. It has good application value in practice.

Enhancing fuzzy evidential reasoning approach using dynamic adjustment mechanism and new rule-based transformation for engineering emergency response evaluation

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

Current Approximate Graph Pattern Mining (AGPM) systems rely on uniform and static sampler allocation strategies, which result in significant computational redundancy because most regions of the sampling space contribute little to the final estimate. However, existing systems overlook this critical characteristic and fail to account for the hierarchical memory architecture of modern servers, leading to slow convergence and substantial memory access overhead. To address these challenges, we propose HierMine, a novel AGPM system designed to overcome the aforementioned drawbacks. The core contributions of HierMine include: (i) a hierarchical sampling strategy that minimizes redundant computation and accelerates convergence; (ii) a dynamic sampling adjustment mechanism that partitions the sampling space online and reallocates samplers adaptively to enhance the hierarchical strategy; (iii) an online grouping-based convergence detection technique that enables the fine-grained dynamic sampling adjustment mechanism; and (iv) a hierarchical data layout optimized for memory access efficiency. Extensive experiments show that HierMine delivers an average speedup of up to 28.9 × over state-of-the-art AGPM systems such as ScaleGPM, while maintaining strong theoretical guarantees on estimation quality. It also consistently outperforms exact graph pattern mining systems, highlighting the practical benefits of our approximate approach.

This study developed a computer-assisted language learning system based on English syntactic structure using long short-term memory (LSTM) network. Through dynamic learning models and adaptive learning paths, personalized and efficient learning experiences were provided, aiming to address the shortcomings of traditional systems and improve learners’ mastery of syntactic structure. The system processed language sequence data by designing an LSTM model, combined with a dynamic adjustment mechanism and real-time feedback system, to achieve dynamic adjustment of personalized learning content and difficulty. Based on data-driven teaching strategies, teaching methods were continuously optimized and learning outcomes were improved by collecting and analyzing learning data. The experimental results of this study indicated that the system had significant effects in improving learners’ mastery of syntactic structures, overcoming the limitations of traditional computer-assisted language learning systems. In the experiment, 120 English learners were selected and divided into an experimental group and a control group, using the system developed in this study and the traditional system, respectively. After the experiment, learners in the experimental group significantly improved their mastery of syntactic structures, with their scores jumping from 70 to 94, while those in the control group increased from 68 to 85. The percentage of correct learning increased from 75% to 94% in the experimental group and from 73% to 86% in the control group. The study shows that the computer-assisted language learning system is effective in improving the mastery of syntactic structures.

With the emergence of wireless networking paradigm, several optimization problems are appearing in such networks. Such problem are related to optimizing network connectivity, coverage and stability. The resolution of these problems turns out to be crucial for optimized network performance. In the case of Wireless Mesh Networks, such problems include computing placement of mesh router nodes so that network performance is optimized. However, as these optimization problems are known to be computationally hard to solve, Genetic Algorithms (GAs) have been recently investigated as effective resolution methods. Mutation operator is one of the GA ingredients. Unlike crossover operators, which achieve to transmit genetic information from parents to off springs, mutation operators usually make some small local perturbation of the individuals, having thus less impact on individuals. Moreover, crossover is "a must" operator in GA and is usually applied with high probability, while mutation operators when implemented, are applied with small probability. Due to this, mutation operator is usually considered as a secondary operator. However, many studies in the literature have shown that mutation when effectively combined with selection operators can improve the performance of GAs. In this work we present the results of an experimental study on the effect of mutation and selection operators in GA for mesh router nodes placement problem. The study aims to identify the mutation and selection types that work best for instances of different characteristics.