Unmanned Aerial Vehicle （UAV） is flexible and easy to deploy， and can assist Mobile Edge Computing （MEC） to help wireless systems improve coverage and communication quality. However， there are challenges such as computational latency requirements and resource management in the research of UAV-assisted MEC systems. Aiming at the delay problem of UAV providing auxiliary calculation services to multiple ground terminals， a Twin Delayed Deep Deterministic policy gradient （TD3） based Task Offloading Algorithm for Delay Minimization （TD3-TOADM） was proposed. Firstly， the optimization problem was modeled as the problem of minimizing the maximum computational delay under energy constraints. Secondly， TD3-TOADM was used to jointly optimize terminal equipment scheduling， UAV trajectory and task offloading ratio to minimize the maximum computational delay. Simulation analysis results show that compared with the task offloading algorithms based on Actor-Critic （AC）， Deep Q-Network （DQN） and Deep Deterministic Policy Gradient （DDPG）， TD3-TOADM reduces the computational delay by more than 8.2%. It can be seen that TD3-TOADM algorithm has good convergence and robustness， and can obtain the optimal offloading strategy with low delay.
To address the problems of high cross-shard transaction proportion and complex cross-shard transaction validation in optimizing blockchain performance， a blockchain sharding method for reducing cross-shard transaction proportion was proposed. Firstly， from the perspective of data sharding， a blockchain transaction sharding model was constructed， and evaluation indicators for sharding performance were provided. Then， for the long-term historical transaction data in blockchain， the sets of transaction frequencies for the sender and the receiver were constructed from the perspective of accounts’ correlation. Finally， a Frequency-considered Blockchain Transcation Sharding algorithm （FBTS） was designed to solve the problem of high cross-shard proportion in transaction sharding. The proposed algorithm was compared with Random Sharding Algorithm （RSA） and Modular Sharding Algorithm （MSA） under the sharding size of 2，3，5，7，15，20，30 and 50. The proposed algorithm outperformed RSA and MSA in terms of performance indicators such as cross-shard transaction proportion， average cross-shard number of accounts， and weighted average cross-shard number of accounts. In addition， the most accounts and transactions were concentrated at low cross-shard number， indicating that the completion of transaction does not involve multiple shards. The experimental results show that proposed algorithm can effectively reduce the cross-shard transaction proportion and shorten the delay of cross-shard transaction.
The optimization of swarm intelligence algorithms is a main way to improve swarm intelligence algorithms. As the swarm intelligence algorithms are more and more widely used in all kinds of model optimization， production scheduling， path planning and other problems， the demand for performance of intelligent algorithms is also getting higher and higher. As an important means to optimize swarm intelligence algorithms， subgroup strategies can balance the global exploration ability and local exploitation ability flexibly， and has become one of the research hotspots of swarm intelligence algorithms. In order to promote the development and application of subgroup strategies， the dynamic subgroup strategy， the subgroup strategy based on master-slave paradigm， and the subgroup strategy based on network structure were investigated in detail. The structural characteristics， improvement methods and application scenarios of various subgroup strategies were expounded. Finally， the current problems and the future research trends and development directions of the subgroup strategies were summarized.
Aiming at the Multi-Depot Vehicle Routing Problem with Time Window （MDVRPTW）， an Improved Adaptive Large Neighborhood Search algorithm （IALNS） was proposed. Firstly， a path segmentation algorithm was improved in the stage of constructing the initial solution. Then， in the optimization stage， the designed removal and repair heuristic operators were used to compete with each other to select the optimal operator， a scoring mechanism was introduced for the operators， and the heuristic operator was selected by roulette. At the same time， the iteration cycle was segmented and the operator weight information was dynamically adjusted in each cycle， effectively to prevent the algorithm from falling into local optimum. Finally， simulated annealing mechanism was adopted as the acceptance criterion of the solution. The relevant parameters of the IALNS were determined by experiments on the Cordeau normative instances， and the solution results of the proposed algorithm were compared with other representative research results in this field. The experimental results show that the solution error between IALNS and Variable Neighborhood Search （VNS） algorithm does not exceed 0.8%， even better in some cases； compared with the multi-phase improved shuffled frog leaping algorithm， the average time-consuming of the proposed algorithm is reduced by 12.8%， and the runtime is shorter for most instances. So the above results verify IALNS is an effective algorithm for solving MDVRPTW.
The purpose of the task scheduling strategy for multiple data centers is to allocate computing tasks to different servers in each data center to improve the resource utilization and energy efficiency. Therefore， a deep reinforcement learning-based integrated scheduling strategy for multiple data center was proposed， which is divided into two stages： data center selection and task allocation within the data centers. In the multiple data centers selection stage， the computing power resources were integrated to improve the overall resource utilization. Firstly， a Deep Q Network （DQN） with Prioritized Experience Replay （PER-DQN） was used to obtain the communication paths to each data center in the network with data centers as nodes. Then， the resource usage cost and network communication cost were calculated， and the optimal data center was selected according to the principle that the sum of the two costs is minimum. In the task allocation stage， firstly， in the selected data center the computing tasks were divided and added to the scheduling queue according to the First-Come First-Served （FCFS） principle. Then， combining the computing device status and ambient temperature， the task allocation algorithm based on Double DQN （Double DQN） was used to obtain the optimal allocation strategy， thereby selecting the server to perform the computing task， avoiding the generation of hot spots and reducing the energy consumption of refrigeration equipment. Experimental results show that the average total cost of PER-DQN-based data center selection algorithm is reduced by 3.6% and 10.0% respectively compared to those of Computing Resource First （CRF） and Shortest Path First （SPF） path selection methods. Compared to Round Robin scheduling （RR） and Greedy scheduling （Greedy） algorithms， the Double DQN-based task deployment algorithm reduces the average Power Usage Effectiveness （PUE） by 2.5% and 1.7% respectively. It can be seen that the proposed strategy can reduce the total cost and data center energy consumption effectively， and realize the efficient operation of multiple data centers.
With the rapid development of Internet of Things （IoT）， a large amount of data generated in edge scenarios such as sensors often needs to be transmitted to cloud nodes for processing， which brings huge transmission cost and processing delay. Cloud-edge collaboration provides a solution for these problems. Firstly， on the basis of comprehensive investigation and analysis of the development process of cloud-edge collaboration， combined with the current research ideas and progress of intelligent cloud-edge collaboration， the data acquisition and analysis， computation offloading technology and model-based intelligent optimization technology in cloud edge architecture were analyzed and discussed emphatically. Secondly， the functions and applications of various technologies in intelligent cloud-edge collaboration were analyzed deeply from the edge and the cloud respectively， and the application scenarios of intelligent cloud-edge collaboration technology in reality were discussed. Finally， the current challenges and future development directions of intelligent cloud-edge collaboration were pointed out.
The difficulty of solving constrained multi-objective optimization problems lies in balancing objective optimization and constraint satisfaction， while balancing the convergence and diversity of solution sets. To solve complex constrained multi-objective optimization problems with large infeasible regions and small feasible regions， a constrained multi-objective evolutionary algorithm based on Two-Stage search and Dynamic Resource Allocation （TSDRA） was proposed. In the first stage， infeasible regions were crossed by ignoring constraints； in the second stage， two kinds of computing resources were allocated dynamically to coordinate local exploitation and global exploration， while balancing the convergence and diversity of the algorithm. The simulation results on LIRCMOP and MW series test problems show that compared with four representative algorithms of Constrained Multi-objective Evolutionary Algorithm with Multiple Stages （CMOEA-MS）， Two-phase （ToP）， Push and Pull Search （PPS） and Multi Stage Constrained Multi-Objective evolutionary algorithm （MSCMO）， the proposed algorithm obtains better results in both Inverted Generational Distance （IGD） and HyperVolume （HV）. TSDRA obtains 10 best IGD values and 9 best HV values on LIRCMOP series test problems， and 9 best IGD values and 10 best HV values on MW series test problems， indicating that the proposed algorithm can effectively solve problems with large infeasible regions and small feasible regions.
To solve the problem of limited computing resources and storage space of edge nodes in the Edge Computing （EC） network， an Edge Computing and Service Offloading （ECSO） algorithm based on improved Deep Reinforcement Learning （DRL） was proposed to reduce node processing latency and improve service performance. Specifically， the problem of edge node service offloading was formulated as a resource-constrained Markov Decision Process （MDP）. Due to the difficulty of predicting the request state transfer probability of the edge node accurately， DRL algorithm was used to solve the problem. Considering that the state action space of edge node for caching services is too large， by defining new action behaviors to replace the original actions， the optimal action set was obtained according to the proposed action selection algorithm， so that the process of calculating the action behavior reward was improved， thereby reducing the size of the action space greatly， and improving the training efficiency and reward of the algorithm. Simulation results show that compared with the original Deep Q-Network （DQN） algorithm， Proximal Policy Optimization （PPO） algorithm and traditional Most Popular （MP） algorithm， the total reward value of the proposed ECSO algorithm is increased by 7.0%， 12.7% and 65.6%， respectively， and the latency of edge node service offloading is reduced by 13.0%， 18.8% and 66.4%， respectively， which verifies the effectiveness of the proposed ECSO algorithm and shows that the ECSO can effectively improve the offloading performance of edge computing services.
The purpose of research on Multi-Robot Task Allocation （MRTA） is to improve the task completion efficiency of robots in smart factories. Aiming at the deficiency of the existing algorithms in dealing with large-scale multi-constrained MRTA， an MRTA Algorithm Combining Genetic Algorithm and Rolling Scheduling （ACGARS） was proposed. Firstly， the coding method based on Directed Acyclic Graph （DAG） was adopted in genetic algorithm to efficiently deal with the priority constraints among tasks. Then， the prior knowledge was added to the initial population of genetic algorithm to improve the search efficiency of the algorithm. Finally， a rolling scheduling strategy based on task groups was designed to reduce the scale of the problem to be solved， thereby solving large-scale problems efficiently. Experimental results on large-scale problem instances show that compared with the schemes generated by Constructive Heuristic Algorithm （CHA）， MinInterfere Algorithm （MIA）， and Genetic Algorithm with Penalty Strategy （GAPS）， the scheme generated by the proposed algorithm has the average order completion time shortened by 30.02%， 16.86% and 75.65% respectively when the number of task groups is 20， which verifies that the proposed algorithm can effectively shorten the average waiting time of orders and improve the efficiency of multi-robot task allocation.
Concerning the trade-off between convergence and diversity in solving the multi-trip pickup and delivery Vehicle Routing Problem （VRP）， a hybrid Non-dominated Sorting Genetic Algorithm Ⅱ （NSGA-Ⅱ） combining Adaptive Large Neighborhood Search （ALNS） algorithm and Adaptive Neighborhood Selection （ANS）， called NSGA-Ⅱ-ALNS-ANS， was proposed. Firstly， considering the influence of the initial population on the convergence speed of the algorithm， an improved regret insertion method was employed to obtain high-quality initial population. Secondly， to improve global and local search capabilities of the algorithm， various destroy-repair operators and neighborhood structures were designed， according to the characteristics of the pickup and delivery problem. Finally， a Best Fit Decreasing （BFD） algorithm based on random sampling and an efficient feasible solution evaluation criterion were proposed to generate vehicle routing schemes. The simulation experiments were conducted on public benchmark instances of different scales， in the comparison experiments with the MA （Memetic Algorithm）， the optimal solution quality of the proposed algorithm increased by 27%. The experimental results show that the proposed algorithm can rapidly generate high-quality vehicle routing schemes that satisfy multiple constraints， and outperform the existing algorithms in terms of both convergence and diversity.
The initial cluster number of the K-means clustering algorithm is randomly determined， a large number of redundant features are contained in the original datasets， which will lead to the decrease of clustering accuracy， and Cuckoo Search （CS） algorithm has the disadvantages of low convergence speed and weak local search. To address these issues， a K-means clustering algorithm combined with Dynamic CS Feature Selection （DCFSK） was proposed. Firstly， an adaptive step size factor was designed during the Levy flight phase to improve the search speed and accuracy of the CS algorithm. Then， to adjust the balance between global search and local search， and accelerate the convergence of the CS algorithm， the discovery probability was dynamically adjusted. An Improved Dynamic CS algorithm （IDCS） was constructed， and then a Dynamic CS-based Feature Selection algorithm （DCFS） was built. Secondly， to improve the calculation accuracy of the traditional Euclidean distance， a weighted Euclidean distance was designed to simultaneously consider the contribution of samples and features to distance calculation. To determine the selection scheme of the optimal number of clusters， the weighted intra-cluster and inter-cluster distances were constructed based on the improved weighted Euclidean distance. Finally， to overcome the defect that the objective function of the traditional K-means clustering only considers the distance within the clusters and does not consider the distance between the clusters， a objective function based on the contour coefficient of median was proposed. Thus， a K-means clustering algorithm based on the adaptive cuckoo optimization feature selection was designed. Experimental results show that， on ten benchmark test functions， IDCS achieves the best metrics. Compared to algorithms such as K-means and DBSCAN （Density-Based Spatial Clustering of Applications with Noise）， DCFSK achieves the best clustering effects on six synthetic datasets and six UCI datasets.
Aiming at the conflict between the makespan and execution cost of cloud workflows with deadlines， a Hybrid Adaptive Particle Swarm Optimization algorithm for workflow scheduling （HAPSO） was proposed. Firstly， a Directed Acyclic Graph （DAG） cloud workflow scheduling model was established based on deadlines. Secondly， through the combination of norm ideal points and adaptive weights， the DAG scheduling model was transformed into a multi-objective optimization problem that weighs DAG makespan and execution cost. Finally， based on Particle Swarm Optimization （PSO） algorithm， the adaptive inertia weight， the adaptive learning factors， the probability switching mechanism of flower pollination algorithm， Firefly Algorithm （FA） and the particle out-of-bound processing method were added to balance the global search ability and the local search ability of the particle swarm， and then to solve the objective optimization problem of DAG makespan and execution cost. The optimization results of PSO， Weight Particle Swarm Optimization （WPSO）， Ant Colony Optimization （ACO） and HAPSO were compared and analyzed in the experiment. Experimental results show that HAPSO reduces the multi-objective function value by 40.9% to 81.1% that weighs the makespan and execution cost of workflow （30~300 tasks）， and HAPSO effectively weighs the makespan and execution cost with the constraints of workflow deadlines. In addition， HAPSO also has a good effect on the single objective of reducing the makespan or execution cost， which verifies the universality of HAPSO.
Two optimization methods for quantum simulator implemented on Sunway supercomputer were proposed aiming at the problems of gradual scaling of quantum hardware and insufficient classical simulation speed. Firstly， the tensor contraction operator library SWTT was reconstructed by improving the tensor transposition strategy and computation strategy， which improved the computing kernel efficiency of partial tensor contraction and reduced redundant memory access. Secondly， the balance between complexity and efficiency of path computation was achieved by the contraction path adjustment method based on data locality optimization. Test results show that the improvement method of operator library can improve the simulation efficiency of the "Sycamore" quantum supremacy circuit by 5.4% and the single-step tensor contraction efficiency by up to 49.7 times； the path adjustment method can improve the floating-point efficiency by about 4 times with the path computational complexity inflated by a factor of 2. The two optimization methods have the efficiencies of single-precision and mixed-precision floating-point operations for the simulation of Google’s 53-bit， 20-layer quantum chip random circuit with a million amplitude sampling improved from 3.98% and 1.69% to 18.48% and 7.42% respectively， and reduce the theoretical estimated simulation time from 470 s to 226 s for single-precision and 304 s to 134 s for mixed-precision， verifying that the two methods significantly improve the quantum computational simulation speed.
To deal with the deficiencies in load balancing and execution efficiency of existing algorithms for cooperative multi-task assignment of multi-Unmanned Aerial Vehicle （UAV）， an Improved Self-Organizing Map （ISOM） algorithm was proposed. In the algorithm， the load balancing degree of UAVs was designed according to the flight time and task execution time in order to improve the efficiency of the task completion. And a novel non-linearly changing learning rate and neighborhood function were designed to ensure the stability and fast convergence of ISOM algorithm. Then， the validity of ISOM algorithm was verified in different task environments. Experimental results show that compared with Particle Swarm Optimization combined with Genetic Algorithm （GA-PSO）， Gurobi and ORTools algorithms， the proposed algorithm has the task completion time reduced by 15.5%， 12.7% and 7.3% respectively. When the effectiveness of track length reduction was verified on KroA100， KroA150， and KroA200 examples of TSPLIB dataset， comparison results with Invasive Weed Optimization （IWO） algorithm， Improved Partheno Genetic Algorithm （IPGA） and Ant Colony-Partheno Genetic Algorithm （AC-PGA） show that ISOM algorithm has the minimum track length when the number of UAVs is 2， 3， 4， 5， 8. It can be seen that ISOM algorithm has a significant effect on solving the problem of multi-UAV cooperative multi-task assignment.
Aiming at the problems of easily falling into local optimum， slow convergence and low solution accuracy of standard Slime Mould Algorithm （SMA）， an Improved Slime Mould Algorithm with Multi-Strategy fusion （MSISMA） was proposed. Firstly， Brownian motion and Levy flight were introduced to enhance the search ability of the algorithm. Secondly， according to different stages of the algorithm， the location update formula of the slime mould was improved to increase the convergence speed and accuracy of the algorithm. Thirdly， the Interval Adaptative Opposition-Based Learning （IAOBL） strategy was adopted to generate the reverse population， with which the diversity and quality of the population were improved， as a result， the convergence speed of the algorithm was improved. Finally， a convergence stagnation monitoring strategy was introduced， which would make the algorithm jump out of the local optimum by re-initializing the positions of some slime mould individuals. With 23 test functions selected，the proposed MSISMA was tested and compared with Equilibrium Slime Mould Algorithm （ESMA）， Slime Mould Algorithm combined to Adaptive Guided Differential Evolution Algorithm （SMA-AGDE）， SMA， Marine Predators Algorithm （MPA） and Equilibrium Optimizer （EO）. Moreover， the Wilcoxon rank-sum test was performed on the running results of all algorithms. Compared with the above algorithms， MSISMA achieves the best average value on 19 test functions and the best standard deviation on 12 test functions， and has the optimization accuracy improved by 23.39% to 55.97% on average. Experimental results show that the convergence speed， solution accuracy and robustness of MSISMA are significantly better.
The K-Means algorithms typically utilize Euclidean distance to calculate the similarity between data points when dealing with large-scale heterogeneous data. However， this method has problems of low efficiency and high computational complexity. Inspired by the significant advantage of Hamming distance in handling data similarity calculation， a Quantum K-Means Hamming （QKMH） algorithm was proposed to calculate similarity. First， the data was prepared and made into quantum state， and the quantum Hamming distance was used to calculate similarity between the points to be clustered and the K cluster centers. Then， the Grover’s minimum search algorithm was improved to find the cluster center closest to the points to be clustered. Finally， these steps were repeated until the designated number of iterations was reached or the clustering centers no longer changed. Based on the quantum simulation computing framework QisKit， the proposed algorithm was validated on the MNIST handwritten digit dataset and compared with various traditional and improved methods. Experimental results show that the F1 score of the QKMH algorithm is improved by 10 percentage points compared with that of the Manhattan distance-based quantum K-Means algorithm and by 4.6 percentage points compared with that of the latest optimized Euclidean distance-based quantum K-Means algorithm， and the time complexity of the QKMH algorithm is lower than those of the above comparison algorithms.
Running a large-scale Spiking Neural Network （SNN） on a distributed computing platform is one of the basic means to improve the level of brain-like computing intelligence. The difficulty lies in how to deploy the SNN to the corresponding number of computing nodes in order to make the overall system run with the best energy efficiency. To solve this problem， on the basis of NEural Simulation Tool-based （NEST-based） Workload Automatic Mapper for SNN （SWAM） proposed by others before， a workload automatic mapper for SNN， named SWAM2， based on precise communication modeling was proposed. In SWAM2， based on the NEST simulator， the communication part of the SNN workload was further accurately modeled； the quantization method of the parameters in the workload model was improved； the maximum network scale prediction method was designed. Experimental results on typical cases of SNN show that， the average prediction errors of SWAM2 were reduced by about 12.62 and 5.15 percentage points respectively compared with those of SWAM in workload communication and computing time prediction. When predicting the optimal mapping of the workload， the average accuracy of SWAM2 reached 97.55%， which was 13.13 percentage points higher than that of SWAM. SWAM2 avoids the process of manual trial and error by automatically predicting the optimal deployment/mapping of SNN workload on computing platform.
