Earticle

Co-evolutionary mechanism is now used into evolutionary algorithms and provides these algorithms the power to promote the convergence. In order to promote the performance of the traditional quantum-inspired evolutionary algorithm (QEA), we proposed a novel quantum-inspired co-evolutionary algorithm (NQCEA), in this paper. The quantum state population is firstly divided into multiple sub-populations, which complete the evolution processes independently. In each evolution cycle, every sub-population will produce an elitist individual, which is then selected to construct an elite library. Subsequently, these individuals in this elite library can help the poor sub-population to find the global optimal solution or near-optimal solution. In addition, a diversity indicator is defined for every sub-population and is used to measure the diversity of the corresponding sub-population. As for the sub-population with poor diversity, the mutation strategies are implemented in order to expand its diversity and improve its global search ability. Finally, the NQCEA is compared with the traditional QEA to test their performance. Experiments are performed on the global numerical optimization functions and the simulation results indicate that this new algorithm has the characteristics of good global search capability and more stable performance than the traditional QEA.

Due to the normal forecasting methods for subgrade settlement using observation data have different applicabilities, and the predicting results has bigger volatility and lower accuracy. In view of the above problems, a method based on least square support vector regression (LSTSVR) and real-coded quantum evolutionary algorithm (RQEA) is proposed. Firstly, the LSTSVR parameter is chosen as a combinatorial optimization problem, and determining the objective function of the combinatorial optimization problem, then, using RQEA to solve the combinatorial optimization problem and optimize the LSTSVR parameters, Finaly, LSTSVR-RQEA is used to sovle the prediction of subgrade settlement. The simulation results show that RQEA is an effective method to select LSTSVR parameters, and has excellent performance when applied to the prediction of subgrade settlement.

Due to the normal forecasting methods for subgrade settlement using observation data have different explicabilities, and the predicting results has bigger volatility and lower accuracy. The Combined forecasting model of subgrade settlement based on forecasting availability and real-coded quantum evolutionary algorithm (RQEA) is put forward in this paper. At the first, according to the basic settlement law of subgrade and characteristics of settlement curve, the growth curve with the S-type characteristics are chosen as single forecasting model; Then, to get the weights of each single forecasting model, objective function is build on the basis of standard of forecasting availability maximization, and RQEA is employed to solve the objective function, and to construct the combined forecasting model of subgrade settlement. The result of engineering practice shows that the proposed method has better prediction accuracy and stability, and can meet engineering demand.

Due to the normal forecasting methods for subgrade settlement using observation data have different applicability and disadvantages, The Combined forecasting model is put forward based on support vector machine (SVM) and real-coded quantum evolutionary algorithm (RQEA) in this paper. Its core is that, according to the basic settlement law of subgrade and characteristics of settlement curve, the growth curve which has S-type characteristic are chosen as single forecasting model, then support vector machine is used to combine the predicting results of each single forecasting model, at the same time, RQEA is adopted to optimize support vector machine parameter to improve the SVM’s performance. The analytical result of engineering practice indicates that the proposed combined forecasting model of subgrade settlement base on SVM and RQEA can not only improve the predicting accuracy, but also reduce the predicting risk, and can meet engineering demand.

This paper proposes the application method of an Adaptive Quantum-Inspired Evolutionary Algorithm (AQEA) to Vehicular Ad Hoc Networks (VANETs) for enhancing clustering and routing performance. AQEA integrates quantum-inspired principles, including quantum bits, quantum superposition, and adaptive quantum rotation gates, to effectively navigate the highly dynamic and complex environments characteristic of VANETs. By dynamically balancing exploration and exploitation, AQEA encodes cluster configurations as quantum states and adjusts them using a fitness-driven rotation operator. Comparative simulations reveal that AQEA consistently produces larger, more stable clusters and reduces both reconfiguration overhead and routing costs compared to conventional algorithms such as the Grasshopper Optimization Algorithm (GOA) and Whale Optimization Algorithm (WOA). AQEA consistently achieves larger and more stable clusters, significantly reduces cluster reconfiguration overhead, and minimizes routing costs. Statistically significant improvements were observed: a 59.5% increase in cluster size and a 29.10% reduction in stability penalty relative to WOA, and a 32.99% reduction in routing cost compared to GOA. These results confirm AQEA’s superior adaptability and robustness, positioning it as an effective solution for managing clustering and routing in dynamic VANET environments. These results validate the practical relevance and algorithmic superiority of AQEA, positioning it as a robust and adaptive solution for managing clustering and routing in dynamic VANET scenarios. Also, these results highlight AQEA’s robustness and adaptability, positioning it as an effective solution for managing clustering and routing in dynamic VANET scenarios. Future research directions include real-world validations, expanded performance evaluations, and further refinement of the algorithm's adaptive mechanisms.

Quantum-Inspired Evolutionary Algorithm(QEA)은 알고리즘 자체에 충분한 data-level parallelism이 내재되어 있어 GPU를 이용한 가속에 용이하다. 그러나 효과적인 실행시간의 단축을 위해서는 CPU와 GPU에의 적절한 task-mapping이 필요하다. 이때 단순히 함수 자체의 병렬성만을 고려하는 것이 아니라 CPU와 GPU간의 데이터 전송도 고려하여 task-mapping을 할 필요가 있다. 또한 추가적인 성능향상을 위하여 zero-copy host memory와 적절한 execution configuration의 사용, 그리고 memory coalescing 등을 이용할 수 있다. 그 결과 30,000개의 item수를 가진 0-1 knapsack problem에 대한 QEA의 수행을 multi-threading CPU에 비해 평균 3.69배 빠르게 할 수 있었다.

Quantum-Inspired Evolutionary Algorithm(QEA) contains sufficient data-level parallelism to be naturally accelerated on GPUs. For an efficient reduction of execution time, however, careful task-mapping should be done to properly reflect the characteristics of CPU and GPU. Furthermore, when deciding which part of the application should run on GPU, we need to consider the data transfer between CPU and GPU memory spaces as well as the data-level parallelism. In addition, the usage of zero-copy host memory, proper choice of the execution configuration, and thread organization considering memory coalescing is important to further reduce the execution time. With all these techniques, we could run QEA 3.69 times faster on average in comparison with the multi-threading CPU for the case of 0-1 knapsack problem with 30,000 items.

Null pattern synthesis method using the Quantum-inspired Evolutionary Algorithm(QEA) is described in this study. A $12{\times}12$ planar array antenna is considered and each element of the array antenna is controlled by 6-bit phase shifter. The maximum number of iteration of 500 is used in simulation and the rotation angle for updating Q-bit individuals is determined to make the individual converge to the best solution and is summarized in a look-up table. In this study we showed that QEA can satisfactorily synthesize the null pattern using smaller number of individuals compared with the conventional Genetic Algorithm.