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When the meta-heuristic algorithm solves the joint resource allocation problem of multi-beam satellites, the computational complexity increases and the algorithm is difficult to converge due to the time delay constraint and capacity constraint. This paper introduced a penalty mechanism in the objective function, and added a penalty value to the objective function of the invalid solution, so that the optimized solution adaptively satisfied these two constraints. Based on this, this paper proposed a joint resource allocation algorithm based on quantum-behaved particle swarm optimization. Simulation results show that the introduction of the penalty strategy solves the problem of difficulty in handling the delay constraint and capacity constraint when applying the meta-heuristic algorithm. The quantum-behaved particle swarm optimization algorithm with penalty mechanism outperforms the existing joint allocation algorithm in terms of allocation fairness index and total system capacity. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the seagull optimization algorithm (SOA), which has low solution accuracy, and rela-tively poor population diversity, and is easy to trap in local optimal, this paper proposed an enhanced seagull optimization algorithm (ESOA) that integrated multiple improvement strategies. Firstly, this paper used an improved self-adaptive differential mutation strategy to perform differential mutation operation on a single seagull individual in each iteration process which could expand the global search range of seagulls and improve the diversity of the population.Secondly, it established a processing mechanism for the worst position of individual seagulls based on particle swarm algorithm.Finally, for the optimal position of the seagull, it adopted a dynamic lens mapping strategy to jump out of the local optimum for the optimal position of the seagull. This paper used 14 functions in the CEC2017 test suite as the benchmark function to compare the performance of ESOA, algorithm with sparrow search algorithm (SSA), moth-flame optimization algorithm (MFO), grey wolf optimizer (GWO), and the improved GSCSOA and CCSOA. The experimental results show that ESOA has a statistically significant performance advantage. [ABSTRACT FROM AUTHOR]

Aiming at solving the problems that the existing anti-screening watermarking algorithms cannot take into account the robustness, invisibility and watermark capacity, this paper propose a robust blind watermarking algorithm based on randomly distributed circular templates. Firstly, convert the hexadecimal watermark sequence into a fixed angle value, and then generate a series of circular templates with angle information according to the random line distribution algorithm, Secondly, separate the original host image according to the three primary color channels and take out the blue component. Thirdly, perform integer wavelet transform on blue component and take out low frequency sub-band (LL). Fourthly, divide the LL into blocks and select several watermark embedding blocks with a selected key. Finally, embed the circular templates in the selected blocks. In addition, in order to increase the watermark capacity, choose the hexadecimal watermark password to replace the QR code or barcode; in order to improve the invisibility of the watermark, embed the watermark templates in the low frequency sub-band of the integer wavelet transform; in order to balance the transparency and robustness of the watermark, using the particle swarm optimization algorithm (PSO) to obtain the optimal embedding strength adaptively; in order to solve the image distortion caused by shooting, perform geometric correction on the captured image before watermark extraction. Select images on the data set USC-SIPI to implement experiments related to common attacks and screen capture attacks, and this paper compared the proposed algorithm with other six algorithms with this image data set. The experimental results show that the algorithm can not only ensure the invisibility of watermark but also resist various types of common attacks. And within a certain range of shooting distance and shooting angle, the algorithm can effectively extract the watermark password, it also improves the watermark capacity and has good robustness against screen shots. [ABSTRACT FROM AUTHOR]

This paper considers the physical-layer security of information transmission in an unmanned aerial vehicle (UAV) multi-user communication system, where a UAV communicates with multiple users on the ground, with the presence of an eavesdropper on the ground trying to intercept the confidential information sent from the UAV to the users. The channels between the UAV and all ground nodes are modeled as more accurate probabilistic LoS (line-of-sight) channels. This paper studies maximizing the minimum average secrecy rate of all users by jointly designing the three-dimensional (3D) trajectory of the UAV and the user scheduling, under the constraints of UAV fight conditions and the discrete user scheduling parameters. To solve the optimization design problem for the UAV secure communication system under the probabilistic LoS channel model, it first decoupled it into three sub-problem, then solved the three sub-problems one by one, by using the integer relaxation and successive convex approximation methods, and finally obtained the 3D flight trajectory of UAV by using the block coordinate descent method. Based on the instantaneous channel state information obtained during the UAV’s flight, it further proposed a real-time optimization algorithm for UAV speed and user scheduling. This algorithm formulated the optimization problem into a linear programming problem and solved it by using the interior point method. Simulation results show that compared to the existing benchmark algorithms, the trajectory optimization of UAV based on the probabilistic LoS channel model can significantly improve the secrecy rate of the system, and the proposed real-time optimization algorithm for speed and user scheduling can further improve the secrecy rate performance. [ABSTRACT FROM AUTHOR]

To improve the local mining and global search ability of quantum-behaved particle swarm optimization algorithm ( QPSO), this paper proposed an improved QPSO algorithm( DSQPSO). DSQPSO algorithm introduced the double strategies co-evolution to adjust the particle position update formula. Firstly, in order to fully reflect the advantage of individual exploration and the characteristic of collective guidance, this paper put forward two kinds of ideas of attraction points to achieve better integration of individuals and the swarm as well as information exchange. Secondly, it redefined the search scope of the particle through considering the relationship between the optimal average position and the global optimum and individual's historical optimum respectively. Moreover, in the iterative process, DSQPSO used the random perturbation mechanism to adjust the global optimal position in order to help the diversity of the swarm to be preserved. Based on 18 test functions, this paper compared DSQPSO with PSO, QPSO, RQPSO and LQPSO in convergence accuracy and robustness. Furthermore, in terms of the optimization results, it compared the improved algorithm with eight intelligent algorithms on two practical engineering optimization problems. Experiments indicate that whether in benchmarking or in engineering application, DSQPSO has obvious advantages in calculation precision and convergence effect. [ABSTRACT FROM AUTHOR]

Aiming at the problems that the sparrow search algorithm (SSA) is prone to fall into local optimum and the optimization accuracy is low in the optimization process, this paper proposed a hybrid strategy improved sparrow search algorithm (MSSA). Firstly, in order to enable individual sparrows to fully search in the search space and increase the accuracy of algorithm optimization, it introduced an archiving stage in the algorithm optimization process to receive the residual position information that might be captured by sparrow producers when they moved to the safe area. Secondly, in the iterative process of the algorithm, it performed an adaptive neighborhood search operation on the current optimal individual, and fully explored the location information around the high-quality individual to enhance the algorithm's ability to jump out of the local optimum. This paper used 9 benchmark functions to evaluate the MSSA with SSA, and other four improved SSA, which were chaotic sparrow search optimization algorithm (CSSOA), mixed strategy improved sparrow search algorithm (MSSSA), improved sparrow search algorithm(ISSA), and enhanced sparrow search algorithm (ESSA). Experiment result illustrates that MSSA can achieve better convergence accuracy and stability on nearly 80% of the benchmark functions, and MSSA is also ranked first in the Friedman test. This paper also applied MSSA to optimize wireless sensor network( WSN) coverage in an obstacle environment. Compared with other five compared algorithms, MSSA can increase the coverage rates of WSN up 9. 77%, 4. 25%, 6. 62%, 3. 02%, and 7. 38%, respectively. [ABSTRACT FROM AUTHOR]

Whale optimization algorithm is a new swarm intelligence optimization algorithm inspired by humpback whale hunting behavior, and its performance is far better than the traditional algorithms. In order to track the latest research results and promote the research in the field of swarm intelligence optimization algorithm, this paper firstly introduced the basic principle of whale optimization algorithm, briefly described its implementation steps, and compared and analyzed its pe1formance characteristics. In view of its shortcomings of slow convergence speed and low convergence accuracy, this paper summarized and analyzed the improved methods including other intelligent optimization algorithms, Levy flight strategy, chaos strategy, as well as their advantages and limitations. Then, it described its applications and effect in combinatorial optimization, image segmentation, data prediction, and so on. Finally, this paper summarized and prospected the future development trend. [ABSTRACT FROM AUTHOR]

For Reconfigurable Intelligent surface (RIS) -assisted secure wireless communication systems in the secrecy rate optimization problem, there were problems such as continuous variation of channel space and difficulty in approximating the optimal solution by traditional mathematical optimization methods. This paper proposed a SECDDPG (security deep deterministic policy gradient) algorithm based on deep reinforcement learning. By modeling the RIS communication system as a Markovian decision process in a continuously variable space, the transmission beamforming and reflection beamforming are jointly optimized to maximize the user secrecy rate. The simulation experimental results showed that the SEC-DDPG algorithm obtained the optimal secrecy rate in both active and passive RIS systems with 15%-20% improvement over the conventional alternating optimization algorithm for different transmission power and the number of reflective elements. The study results show that the security in the active RIS scenario is better than that in the passive RIS, and the proposed SEC-DDPG algorithm can significantly improve the user secrecy rate of the secure wireless communication system with robustness and close to the optimal secrecy performance of the system compared with the alternating optimization algorithm. [ABSTRACT FROM AUTHOR]

In order to improve the convergence accuracy and iteration speed of Harris Eagle optimization algorithm(HHO), this paper proposed a multi-strategy cooperative optimization improved HHO algorithm(MSHHO). First, this paper used a Latin hypercube sampling to initialize the population to enhance the uniformity of individual distribution in the solution space. Secondly, it introduced the adaptive Archimedes spiral mechanism based on Lévy flight in the local search stage to improve the mining mechanism of the algorithm and effectively enhance the search rigor of individual neighborhood to improve the convergence accuracy of the algorithm. Finally, considering that the algorithm is prone to fall into the local extremum situation at the later stage of iteration, it adopted the hybrid mutation strategy of Cauchy mutation and reverse learning to alternate the optimal individuals to help them escape from the local extremum region quickly and accelerate the iteration speed of the algorithm. Through the comparative analysis of the solution of benchmark function, Wilcoxon rank sum test and the comparative analysis of CEC2014 complex functions, which confirmed the excellent optimization performance and robust robustness of the improved algorithm. [ABSTRACT FROM AUTHOR]

To solve the problems of low convergence accuracy, slow convergence speed and easy to fall into local optimum of traditional particle swarm algorithm, this paper proposed an improved particle swarm algorithm with multistrategy fusion. Firstly, in order to accelerate the convergence speed of free particles, the improved algorithm used a method of updating the position of free particles based on the midperpendicular algorithm. secondly, the improved algorithm designed a strategy of generating exploding particles near the optimal particles to enhance the optimizationseeking accuracy and optimization-seeking speed of the algorithm, and the improved algorithm also designed a particle velocity updating strategy relying only on the global optimal particle position to accommodate the first two strategies. Finally, The algorithm also used the inertia weights and particle position update methods of the simplified particle swarm optimization algorithm based on probabilistic hierarchy. This paper designed a few comparison experiments with other five improved particle swarm algorithms, and the results show that the improved algorithm in this paper has a greater advantage and better performance whether dealing with low-dimensional problems or high-dimensional problems. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the basic Gravity search algorithm（GSA）, such as prone to premature maturity, easy to fall into local optimum, and lack an effective acceleration mechanism, this paper proposed a GSA（IABHGSA） based on the improved adaptive black hole mechanism. The algorithm used the improved Tent mapping to initialize the population, which makes the distribution of the initial population more random, uniform and traversal, and it enhanced the global exploration capability of the algorithm; The algorithm introduced an improved adaptive black hole mechanism and selected the position update strategy, according to the evolution of the particles, which makes the position update more reasonable, Effectively reduce the possibility of particles falling into local optima; Through the optimal and worst particle update strategy based on learning ideas, enhanced the algorithm’s ability to escape from local optima and improved the algorithm’s optimization speed; The algorithm introduced group migration to provide effective accelerated convergence mechanism for the algorithm. Finally, this paper selected 8 benchmark functions to test IABHGSA and compared with the experimental results of related algorithms, the results show that IABHGSA has better optimization performance. [ABSTRACT FROM AUTHOR]

The U-turn task is one of the contents of autonomous driving research, and most of the solutions under the standard roads in cities cannot be implemented on non-standard roads. Aiming at solving this problem, this paper establishes a vehicle U-turn dynamical model and designs a multi-scale convolutional neural network to extract feature maps as the input of the agent. In addition, for the sparse reward problem in the U-turn task, this paper proposes a hierarchical proximal policy optimization algorithm that combines hierarchical reinforcement learning and proximal policy optimization algorithm. In experiments with simple and complex scenarios, this algorithm learns policies faster and has a higher success rate of U-turn compared to other algorithms. [ABSTRACT FROM AUTHOR]

Classic flower pollination algorithm（FPA） can be easily exposed to the shortcomings of local optimal solution and slow convergence velocity. In view of these shortcomings, this paper proposed an FPA with an enhanced lens imaging strategy and random neighborhood-based mutation strategy. The lens imaging strategy can help the algorithm avoid the shortcoming of local optimal solution by expanding the search space of FPA to increase the diversity of the solution. The introduction of random neighborhood-based mutation strategy can enhance the convergence accuracy and search speed of the algorithm by guiding algorithm search with information in the neighborhood. A comparison of the improved FPA with four other improved algorithms on CEC2013 test function found that the improved multi-strategy FPA performs better than the comparison algorithms in both convergence accuracy and search speed. To study its practical utility, this paper applies the multi-strategy FPA into the automobile transmission parameter model and the results indicate that multi-strategy FPA is better than the comparison algorithm in optimization of automobile transmission parameters. [ABSTRACT FROM AUTHOR]

In order to overcome the drawbacks of Archimedes optimization algorithm( AOA), such as weak global search ability, easily trapping into local optimum and prematurely converge, this paper put forward Archimedes optimization algorithm improved by multi-strategy collaborative( MAOA ) . Initially, it used the random Gaussian mutation strategy to increase the diversity of the population in the iterative process and strengthen the global search ability. Then, based on the randomness, ergodicity and diversity of multiple chaotic models, it introduced the local chaotic search strategy to expand the scope range of the chaotic space and enhance the local development capabilities of the algorithm. At the same time, it proposed a nonlinear dynamic density decreasing factor to coordinate the global exploration ability and local development ability of the algorithm. Finally, for the sake of increasing the diversity of the population during the iteration process, this paper adopted the golden sine strategy of the Levy flight guidance mechanism to perturb the population position and improve the ability of the algorithm to jump out of the local optimum. Through simulation experiments on 12 benchmark functions and part of the CEC2014 function set, the results show that the proposed algorithm can overcome the shortcomings of AOA' s weak global exploration ability and easy to fall into local optimality, and improve the accuracy and stability of AOA . In addition, the introduction of mechanical optimization design cases for testing and analysis will further verify the feasibility and applicability of MAOA on practical issues. [ABSTRACT FROM AUTHOR]

According to the characteristics of energy limitation in wireless sensor networks, this paper proposed a clustering protocol based on spatiotemporal correlation weighted objective function particle swarm optimization algorithm (SC-WOFPSO). Firstly, this protocol used Kohonen neural network to extract the data similarity between nodes. In the process of clustering, this protocol comprehensively considered the data similarity between nodes, the distance between nodes and the residual energy of cluster head nodes, and used PSO algorithm for iterative optimization to find the optimal cluster head set. In the process of cluster, the non cluster head nodes in the network calculated the goal function value for each cluster head, and chose to join the cluster head with the maximum function value. Finally, this paper started from the three performance indicators of total network energy consumption, network life and network throughput to verify the protocol. The result shows that this protocol can effectively reduce network energy consumption, increase network life and network throughput. [ABSTRACT FROM AUTHOR]

How to dispatch emergency supplies timely and efficiently and reduce the damage caused by emergencies has become the focus of social attention. On the premise of considering the characteristics of special emergencies such as the epidemic situation of COVID-19, this paper constructed a kind of emergency supplies scheduling network of multi-supply points and multimodal transportation. Taking the lowest transportation cost, the least time penalty and the minimum risk of infection of dispatchers as the optimization objectives, it established a kind of multi-objective optimal scheduling model. In view of the limitation of the optimization algorithm based on clustering in solving multi-supply points, especially multi-objective scheduling optimization problems, the paper proposed a kind of hybrid niche genetic algorithm for variable length genotypes considering the idea of full feasible regions, which could avoid the problem above by restoring the search range of the solution to the fully feasible regions. The experiment results of 23 benchmark instances show that the optimization performance of the algorithm is stronger and it can search better solutions than best-known solutions of some examples. On this basis, the simulation results of four kinds of genetic algorithms in emergency supplies scheduling examples of multi-supply points and multimodal transportation come to a conclusion that the improved strategies such as hybrid niche are superior. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the sparrow search algorithm such as slow convergence speed, insufficient optimization accuracy and easy to fall into the local optimum, this paper proposed an improved sparrow search algorithm that combined the sine search strategy and the diversity mutation processing strategy. Through the introduction of a sine search strategy, adaptive adjustment of individual weights improved the convergence speed of the algorithm. Aiming at the problem of excessive individual aggregation, this paper adopted diversity mutation processing, introduced the concept of population aggregation degree in biology and Cauchy mutation to disturb the optimal solution, and improved the possibility of the algorithm escaping from the local optimal. By testing 9 benchmark functions with different characteristics, the test results show that the improved algorithm can converge to the optimal value faster, with better average and standard deviation, indicating that it has better convergence speed, convergence stability, and the ability to escape local optimal values. By applying the improved optimization algorithm to the parameter tuning of the fractional PID controller, the experimental results further verify the effectiveness and feasibility of the improved strategy. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of arithmetic optimization algorithm (AOA) in the search process, such as local extreme points, slow convergence speed and low solution accuracy, this paper proposed a multi-strategy fusion arithmetic optimization algorithm (MFAOA). Firstly, the algorithm used the Sobol sequence to initialize the AOA population, which increased the diversity of the initial individuals and laid the foundation for the global optimization of the algorithm. Then, it reconstructed the mathematical optimizer acceleration function (MOA) to weigh the proportion of the global search and the local development process. Finally, it used the chaotic elite mutation strategy to improve the algorithm's over-dependence on the current optimal solution and enhanced the algorithm's ability to jump out of the local extremum. The paper used 12 general benchmark functions and part of CEC2014 test functions for experimental simulation, and the results show that MFAOA has a significant improvement in solution accuracy and convergence speed. Besides, the algorithm introduced two engineering examples for optimization, which verifies the feasibility of MFAOA in engineering optimization problems. [ABSTRACT FROM AUTHOR]

For coyote optimization algorithm's global search ability was insufficient, easily plunged into local optimum, this paper proposed an improved coyote optimization algorithm. In the optimization process of coyote optimization algorithm, this improved algorithm introduced a deformed elite retention strategy and added the environmental impact factor in the growth process of coyote, then brought the grown coyotes into the Kent mapping to traverse the search space to strengthen the mining ability and search performance of the algorithm. Numerical experiments show that the improved algorithm has better perfor-mance. Taking the whole social and economic costs and carbon emissions as the decision-making objectives, this paper constructed a bi-level programming model for location and capacity of electric vehicle charging station, and used the improved coyote optimization algorithm to solve the programming model to verify the feasibility and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

After in-depth research on the path planning algorithm of the AGV transportation system and the port layout of the automated container terminal, in view of the possible conflicts and unequal task allocation in the path planning between the quay crane and the yard, this paper proposed an automatic terminal multi-ACV path optimization algorithm based on a dynamic balance strategy. By improving the Dijkstra algorithm, considering the distance of the path, and at the same time introducing the number of AGVs expected to pass on the path, which realized the dynamic balance of path allocation. By proposing an improved speed control strategy and a re-planning path control strategy, which effectively reduced the number of conflicts and the road congestion rate. This paper also established adynamic balance strategy and a MAS-based control method comparison experiment. The results show that the algorithm can effectively solve large-scale task assignment problems with more than 500 tasks and significantly reduce the average blockage rate 0.000 8 0.005 5. This algorithm can also be applied to other types of terminal layouts to further improve the horizontal transportation efficiency of the container terminal. [ABSTRACT FROM AUTHOR]

In order to solve the problem of premature convergence and falling into local optimum in biogeography-based optimization algorithm, this paper proposed an improved biogeography-based optimization algorithm introducing species evolution (SEBBO). The algorithm divided all habitats into three regions according to the number of species, established a co-evolution relationship, and reasonably adopted interval invasion and regional cooperation/competition strategies to meet diversity while avoided premature convergence. It defined two mutation strategies of species change and species development, and designed the dual-strategy cooperative mutation operator to solve the damage of the mutation operator to the better solution. Then, compared with standard BBO and other related improved algorithms on 8 benchmark functions from CEC2017, SEBBO algorithm is superior to BBO and other BBO variants in terms of algorithm performance and stability, and local optimal values is not easily limit the algorithm. Finally, this paper applied the SEBBO algorithm to flexible Job-Shop scheduling with maximum completion time as the goal to test its practical application value. Experiments show that the algorithm has a certain effectiveness in solving FJSP. [ABSTRACT FROM AUTHOR]

In the scenario that a dedicated energy transmitter serves multiple wireless-powered users, aiming to minimize the task execution latency, this paper proposed a new multi-user cooperative computation scheme and formulated the problems to optimize user matching and resource allocation. For the one-to-one matching case between users, this paper derived a high-performance solution based on the alternating optimization and Hungarian algorithms and a low-complexity solution based on the reformulation-linearization technique. For the one-to-many matching case, this paper derived an improved greedy algorithm. Experimental results show that in the case of one-to-one matching, the proposed scheme can reduce the task processing delay by up to 12. 6% compared with the comparison schemes, for the one-to-many matching case, the proposed scheme saves 5% of the task processing delay. The proposed scheme can meet the task execution latency requirements of the users. [ABSTRACT FROM AUTHOR]

Aiming at the problems in the iterative process of seagull optimization algorithm ( SOA), such as local optimization, slow convergence speed and low optimization accuracy, this paper proposed a golden sine guide and sigmoid continuous seagull optimization algorithm( GSCSOA) . In the seagull migration stage, the algorithm used the sigmoid function as a nonlinear convergence factor to guide the seagull search process, so that the algorithm maintained a stronger global optimization ability in the early stage and converged faster in the later stage. In the seagull rushing stage, it introduced the idea of Taboo search, so that the seagulls always moved to the area with higher confidence, and the optimal position continued to change in one iteration, which improved the optimization accuracy. After that, it used the golden sine mechanism to guide the population position to update, which narrowed the search range, and improved the local optimization ability. Finally, this paper used 12 test functions and CEC2014 function set to test the improved algorithm, and the results prove that the improved seagull algorithm has better convergence speed and accuracy than original algorithm and other algorithms. [ABSTRACT FROM AUTHOR]

To solve the problems of low convergence accuracy and easy to fall into local optimum in the optimization process of the original artificial bee colony algorithm, this paper proposed an enhanced artificial bee colony algorithm with strong and weak ties theory (SWT-ABC). Firstly, it defined the strong and weak ties model in a matrix multi-groups structure and stipulated that the strong ties individuals randomly guided the search process from three directions to improve the convergence speed and accuracy. In order to enhance the ability to jump out of the local optimum, SWT-ABC adopted the interaction of weak ties individuals among subgroups to enhance the population diversity. Finally the algorithm added the reverse learning mechanism of scout bees and determined appropriate upper limit of honey source to improve the utilization efficiency of the evaluation times. Through the numerical experiment of benchmark functions and comparisons with 12 improved intelligent optimization algorithms, it shows that the SWT-ABC has high convergence accuracy and good robustness, and also gets good performance in solving high-dimensional optimization problems. [ABSTRACT FROM AUTHOR]

Nowadays, how to reduce the execution cost of workflow under the premise of meeting the deadline constraint is one of the main problems of workflow scheduling in cloud. A three-step list scheduling algorithm can solve this problem effectively. However, the algorithm can only form static sub-deadline in deadline allocation stage. To provide convenience for users to deploy workflow tasks, cloud service providers provide users with three types of instances. Among them, spot instance has great price advantage and can greatly reduce the execution cost of workflow. To solve the above problems, this paper proposed a cost optimization algorithm based on dynamic allocation of deadline for workflow scheduling (S-DTDA). This algorithm used particle swarm optimization to dynamically allocate the deadline, which made up for the defect of the three-step list scheduling algorithm. In the virtual machine selection stage, the algorithm added the spot instance to the candidate resources, which greatly reduced the execution cost. Experimental results show that compared with other classical algorithms, this algorithm has obvious advantages in experimental success rate and execution cost. In conclusion, S-DTDA algorithm can effectively solve the cost optimization problem of deadline constraint in workflow scheduling. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the original sparrow search algorithm (Sparrow Search Algorithm, SSA) has reduced population diversity and the weak local development ability makes it difficult to jump out of the local optimum under the condition of approaching the global optimum. This paper proposes a sparrow search algorithm based on tangential flight (tangent flight Sparrow Search Algorithm, tanSSA). First of all, using adaptive t-distribution strategy to improve the finder location update formula can improve the individual sparrow’s optimization ability and prevent the algorithm from maturing. Then, the tangent flight strategy in the tangent search algorithm can enhance the search space ability of the algorithm, and can make the algorithm jump out of the advantage of the local optimal solution. In original sparrow search algorithm, using the tangent flight disturbance strategy to disturb the optimal solution. The combination of these two strategies can effectively improve the exploration and development performance of tanSSA algorithm. Finally, using 12 standard benchmark functions to test and verify the optimization performance of tanSSA algorithm combined with Wilcoxon rank sum test, and compared with the original SSA algorithm, whale optimization algorithm, particle swarm optimization algorithm and adaptive t-distribution SSA algorithm. The experimental results show that the optimization ability and convergence speed of the sparrow search algorithm based on tangent flight have a significant improvement. [ABSTRACT FROM AUTHOR]

The three-dimensional path planning problem is a combinatorial optimization problem to find the optimal path be- tween the starting point and the destination in the interference environment. Aiming at the shortcomings of traditional swarm in- telligence algorithm in solving this problem,such as low convergence accuracy and easy to fall into local optimum, this paper proposed an adaptive moth-flame optimization algorithm to solve the problem. By introducing the dynamic adjustment strategy of the flight direction and the position intersection strategy,the improved algorithm continuously generated new individuals while dynamically adjusting the flight direction of the moth, which effectively avoided the algorithm from falling into local optimum ; by adaptively adjusting the number of flames, so as to the algorithm enhanced population diversity in the global exploration stage a- void premature convergence. It used the adaptive moth-flame optimization algorithm and other swarm intelligence algorithms to solve the three-dimensional path planning problem. The experimental results show that the improved adaptive moth-flame opti- mization algorithm has the smallest cost value and the fastest convergence speed among all algorithms. The algorithm has better solution ability in 3D path planning problem. [ABSTRACT FROM AUTHOR]

In order to improve the rationality of the actual base station deployment, for the base station deployment of the heterogeneous cellular network（HetNet), this paper considered the repulsion among MBSs, and the repulsion between MBS and PBS. First, this paper used the approximate SIR analysis method of cellular network to derive the coverage probability of the two-tier non-Poisson network. Next, it analyzed the network energy efficiency and derived the throughput and total network energy efficiency. Finally, it proposed an energy efficiency optimization algorithm for the transmission power, which maximized the network energy efficiency by optimizing the transmission power of the PBS. The simulation results show that, the coverage pro-bability of proposed model is better than that of the two-tier HPPP model, and the energy efficiency optimization algorithm can increase the system energy efficiency by about 30%.In actual deployment, people should set the appropriate value of transmit power for PBSs, and control the distance among base stations. [ABSTRACT FROM AUTHOR]

In order to optimize the performance of the multi-verse algorithm to solve the optimal value of function, this paper proposed a global optimization multiverse algorithm（G-MVO） with an improved search mechanism. Aiming at the defects of the standard algorithm, which was easy to fall into local optimum and premature convergence due to the single search mechanism, this paper proposed three learning strategies to enhance the algorithm performance: through multi-strategy interaction and cooperation to reduce the complexity of the algorithm and improve the accuracy of the solution, designing adaptive parameters to dynamically choose the best strategy and optimizing the performance of the algorithm globally. In order to verify the effectiveness of the G-MVO algorithm, simulation experiments were carried out on 8 benchmark functions of different dimensions. Experimental results show that the proposed algorithm has better solution precision and convergence speed. [ABSTRACT FROM AUTHOR]

Aiming at the problem that Internet of Things devices are easy to be used by attackers to invade the network, this paper designed and implemented an integrated defense mechanism of Internet of Things, which combined network topology shuffling-based moving target defense and cyber deception. And based on this defense mechanism, this paper designed a decoy path-based optimization algorithm to optimize the network topology. With the support of software defined network, changing the network topology of real nodes and decoy nodes actively to realize the network topology shuffling in the Internet of Things. This paper studied the effectiveness of defense mechanism through a graphical security model with security metrics, and used three indicators to measure the security and performance of DPOA. The simulation results show that the defense cost of the scheme based on DPOA is significantly reduced and the security is high, which is more suitable for the Internet of Things. [ABSTRACT FROM AUTHOR]

In order to solve the problems of lack of state labels, lack of data samples and unbalanced distribution in equipment life prediction, this paper proposed an improved K-means algorithm based on PSO and a data optimization scheme based on traditional SMOTE. In the process of optimizing K-means algorithm, it combined the characteristics of particle swarm optimization algorithm, and improved the optimization efficiency of particle swarm optimization algorithm by giving the particle generation range of particle swarm optimization algorithm, so as to quickly judge the working state of the equipment. Then, by comparing the mean distance of samples in the same cluster with the distance from the sample to the center, it established an improved SMOTE algorithm, and avoided the calculation error caused by the imbalance of samples by adding the number of minority samples. Finally, this paper used AdaBoost integrated optimization KNN algorithm to improve the classification effect, and by fitting the equipment life curve, it could better predict the equipment health level and future life. The example shows that the proposed model can effectively predict the health status of equipment under small sample unbalanced data. [ABSTRACT FROM AUTHOR]

In order to solve the shortcoming of meta-heuristic mayfly optimization algorithm ( MOA ), such as low precision, slow convergence velocity and low stability, this paper proposed a new mayfly optimization algorithm, which combined the gold sine and adaptive merge. Firstly, it introduced the adaptive inertia weight factor to enhance the search and development ability of the algorithm to achieve a better balance. Then it introduced the fusion Levy flight strategy and the gold sine factor to further improve the shortcoming of fall into the local optimum, enhanced the population diversity and jumped out of the local optimum. Simulation results show that the solution precision, convergence velocity and optimization ability of the improved algorithms are significantly improved on the test functions. At the same time, in order to verify the reliability and validity of the results, this paper analyzed the data obtained by the improved algorithm on statistical test, mean absolute error and solution success rate. The algorithm has obvious improvement in the stability, reliability and robustness compared with the MOA. Besides, this paper introduced a specific engineer case to verify the applicability of the algorithm in engineering. [ABSTRACT FROM AUTHOR]

Disaster preparedness measures can prepare for disaster relief to ensure that emergency supplies can reach the disaster area in a timely and effective manner. This paper proposed a bi-level programming emergency resource scheduling location-routing model with considering preparedness ( BLERSLR-P) In the model, the upper level aimed to minimize the total cost of supply station construction and operation, while the lower level aimed to minimize the cost of distribution route. This paper designed an improved bi-level salp swarm genetic algorithm (BLSSAGA) to solve this problem, combined the concept of iterative division to update the leader position, used the natural exponential inertia weight strategy to modify the control factor, used the chaotic map to update the follower position, and used Taguchi analysis to obtain reasonable values of parameters. Finally, this paper used bi-level salp swarm genetic algorithm ( BLSSAGA) and genetic particle swarm optimization algorithm ( GAPSO), particle swarm optimization algorithm( PSO), and immune optimization algorithm(IA ) to solve and compare and analyze the LRP data set in OR-Library, which verified the feasibility and effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

The basic seagull optimization algorithm(SOA) has some drawbacks such as low solution accuracy, slow convergence rate, and easy to fall into local optima when solving complex function optimization problems. This paper proposes an improve SOA based on somersault foraging strategy(abbreviated as SFSOA). First, the proposed SFSOA updates the positions of seagull individuals by using nonlinearly decrease strategy of control parameter A based on the inverted sigmoid function. This way improves individual solution quality and accelerates convergence speed of algorithm. Then, a novel learning mechanism based on the somersault foraging strategy is introduced for the global best individual to increase the position diversity of seagull individual and avoid the algorithm falling into a local optimum at the lately stage. The proposed SFSOA is compared against the basic SOA, grey wolf optimizer(GWO), and improved SOA(ISOA) by using eight complex benchmark function optimization problems. Experimental results reveal that the proposed SFSOA has higher solution accuracy, faster convergence rate and stronger global search ability than its competitors and can effectively deal with complex function optimization problems. Finally, SFSOA is applied to solve the feature selection problems and the satisfactory results are obtained. [ABSTRACT FROM AUTHOR]

Pose graph optimization（PGO） is a high dimensional non-convex optimization algorithm widely used in the field of computer vision. It is hard to solve directly and mainly relies on iterative techniques. It requires high quality of initial value which is difficult to be guaranteed in practice. This paper studied PGO problem and proposed a simple closed solution algorithm for pose graph based on eigen decomposition. This algorithm first developed the semidefinite relaxation of maximum-likelihood estimation（MLE） for PGO problems. Then it transformed MLE into eigen decomposition problem, and designed an improved model reduction method to solve the problem by using the sparsity of data, which further improves the computational speed of the algorithm. The algorithm has the advantages of scalability, low computational cost and high precision. Finally, experimental evaluation on simulated and real-world pose-graph datasets shows that the proposed algorithm can optimize the pose graph quickly without compromising accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of aquila optimization algorithm（AO）, this paper proposed an aquila optimization algorithm using hybrid search strategy. Firstly, the algorithm introduced dynamic adjustment function to balance global exploration and local exploitation; Secondly, it introduced chaotic adaptive weights to enhance the global search capability of the algorithm and accelerate the convergence speed of the algorithm; Thirdly, it introduced a new individual mutation probability coefficient and an improved differential mutation strategy, and used individuals with better fitness values to lead other individuals in the population to carry out search activities, which maintained the diversity of the population and enhanced the ability of the algorithm to jump out of the local optimum. Through the numerical experiment simulation of 8 benchmark test functions, 10 CEC2019 test functions and 1 engineering application problem, The experimental results show that the algorithm has a significant improvement in global convergence speed and optimization accuracy, and has a better ability to jump out of the local optimum. [ABSTRACT FROM AUTHOR]

In the large data environment, there are many problems in the parallel deep convolution neural network（DCNN） algorithm, such as excessive data redundancy, slow convolution layer operation and poor convergence of loss function. This paper proposed a parallel deep convolution neural network optimization algorithm based on the Im2 col method. First, the algorithm proposed a parallel feature extraction strategy based on Marr-Hildreth operator to extract target features from data as input of convolution neural network, which can effectively avoid the problem of excessive data redundancy. Secondly, the algorithm designed a parallel model training strategy based on the Im2 col method. The redundant convolution kernel is removed by designing the Mahalanobis distance center value, and the convolution layer operation speed is improved by combining the MapReduce and Im2 col methods. Finally, the algorithm proposed an improved small-batch gradient descent strategy, which eliminates the effect of abnormal data on the batch gradient and solves the problem of poor convergence of the loss function. The experimental results show that IA-PDCNNOA algorithm performs well in deep convolution neural network calculation under large data environment and is suitable for parallel DCNN model training of large datasets. [ABSTRACT FROM AUTHOR]

Heap based optimizer (HBO) is a novel and good intelligent optimization algorithm (IOA) proposed recently, and it needs improving and applying extensively. In view of its defects such as little information interchange resulting in poor search ability, this paper proposes an improved HBO, Information interchange Strengthened HBO (ISHBO). First, it uses a self-interchange strategy to update the state of the best agent to improve the efficiency of HBO since the best agent does not participate in the search process. Secondly, it presents an elite opposition interchange and an elite guiding free exchange, which breaks through the shackle of the individual’s communication only with its direct leader and colleagues, to improve the search ability. Finally, it integrates the two strategies are cleverly to update the states of 3 random agents in HBO to form ISHBO. A lot of experimental results on functions with diverse types and different dimensions show that compared with HBO and other state-of-the-art algorithms, the search ability of ISHBO is much stronger. Moreover, the results on IOAs’ application to robot path planning also indicate that ISHBO is more competitive in solving practical optimization problems. [ABSTRACT FROM AUTHOR]

In order to solve the problems of BBO such as insufficient search scope in the early stage and easy to fall into local optimization in the later stage, this paper proposed an improved El-BBO which based on El. Firstly, the algorithm searched for new habitat around the original habitat, it enhanced the diversity of the initialization population. Secondly, the algorithm made improved habitat expansion, it improved the convergence efficiency of the algorithm. Finally, the algorithm used gradient de scent to make quadratic convergence in the field of optimal solution, which improved the convergence accuracy of the algorithm. This paper carried out 50 Monte Carlo experiments on 12 optimized test functions commonly used in CEC2014, the experimental results show that the overall performance of El-BBO is better than the other three intelligent optimization algorithms in terms of optimal fitness value, average fitness value and standard deviation. It shows that El-BBO can improve the ability to find the optimal solution and enhance the search stability. [ABSTRACT FROM AUTHOR]

This paper proposed a two-stage emergency logistics transportation and redistribution strategy for the problems con- sidering the transportation timeliness and the supplies distribution fairness in emergency logistics. Firstly, This paper proposed the capacitated vehicle routing problem with maximum time satisfaction( CVRP-MTS) for emergency vehicle transport schedu- ling and the emergency supplies distribution problem with minimum weighted standard deviation of satisfactions ( EMDP-MWS- DS) based on the time satisfaction, the supplies satisfaction and the comprehensive satisfaction defined in this paper. Then this paper designed a hybrid grey wolf optimization algorithm( HGWOA) to solve the CVRP-MTS in the first stage. The HGWOA adopted the optimal split procedure decoding strategy, and adopted the chaotic search strategy, the 2-0pt and 1-1 local search strategy to improve the search ability of the algorithm. And this paper implemented the chaotic grey wolf optimization algorithm ( CGWOA) to solve the EMDP-MWSDS in the second stage. The experimental results show that in the first stage, overall time satisfaction of the CVRP-MTS is 39. 31 % higher than that of the CVRP model. In the second stage, when 80% of the actual material quantity and 0. 75 for the weight coefficient, the standard deviation of the comprehensive satisfaction degree is 0. 061 9, and the standard deviation of the supplies satisfaction degree is 0. 097 4. The distribution results effectively balance the victims' psychological demands and supplies utilization efficiency. The two-stage emergency logistics transportation and supplies redistribution distribution strategy can obtain high time satisfaction and good supplies distribution results, which prove the effectiveness of the proposed models and algorithms. [ABSTRACT FROM AUTHOR]

This paper proposed a new graph-oriented consistent multi-view sparse clustering framework. This method firstly decomposed multi-view data into consistency and inconsistency parts, used similarity measurement method and KNN (K-nearest neighbor) algorithm to decompose and fuse multi-view data, and then used sparse representation to learn the consistent similarity matrix of multi-view graphs. Finally, it obtained the clustering results through spectral clustering. In addition, this paper designed and implemented an alternate iterative optimization algorithm to solve the objective function. It verifies the effectiveness of the method through comparative experiments on eight multi-view datasets. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication technology and the increasing number of terminal devices, in order to alleviate this phenomenon and increase the system network capacity, aiming at the problem of cellular D2D (device to device) communication resource allocation in the Internet of Vehicles, this paper proposed an allocation algorithm to maximize the utilization of spectrum resources. The algorithm took maximizing the utilization of spectrum resources as the optimization goal, and met the basic quality of service (QoS) for Internet of Vehicles communication, and used V2V (vehicle to vehicle ) and V2P (vehicle to people) share channel resources to improve spectrum resource utilization. Firstly, it used the link gain factor defined by the channel state information to find a set of potential communication links for the end user, then proved that the power allocation problem when end users reused link resources was a convex optimization problem, and used convex optimization theory to obtain the optimal transmiss ion power. Finally, this paper solved the optimal channel matching problem. This problem was a many-to-one weighted matching problem, this paper solved it by KM( Kuhn Munkres) algorithm to reduce the complexity of the algorithm. Simulation results show that, compared with other algorithms, the proposed algorithm can effectively improve system throughput, increase spectrum resource utilization, improve network performance, and optimize vehicle networking communication resource allocation. [ABSTRACT FROM AUTHOR]

Aiming at solving the premature convergence of YYPO algorithm, this paper introduced SA strategy into the switch phase between P, and P2 in YYPO, and proposed a novel hybrid single-object optimization algorithms YYPO-SA ( including YYPO-SAl and YYPO-SA2). YYPO-SA inherited the lightweight characteristics of YYPO, and combined the YYPO' s excellent global search ability with the powerful local search performance of SA. It used the proposed 28 test functions in the single objective practical parameter algorithm competition in 2013 Evolutionary Computing Conference as the benchmark. It empirically studied the performance of YYPO-SA2, YYPO, A-YYPO, IYYPO, and 3 state of the art algorithms-GWO, WOA, and SCA based on experimental results. The experimental res ults show that YYPO-SA can perform more stable and achieve more accurate solutions. At the end, this paper also applied YYPO-SA to solve the spring design task. The experimental results show that YYPO-SA can also solve the constrained optimization task well. [ABSTRACT FROM AUTHOR]

Energy efficiency optimization is a research hot spot in the field of S G communications. First, this paper established an energy efficiency optimization model with regard to transmit power, pilot sequence length, and number of base station ( BS) antennas under user QoS requirements and the system' s tolerable channel effective noise in a single-cell multi-user uplink massive MIMO system. Then, different from the traditional iterative algorithm for solving the optimal number of BS antennas, the method used a closed-form expression to solve the optimal number of BS antennas using Lambert W function. Finally,according to the fractional programming, this paper proposed an iterative optimization algorithm to jointly optimize the system pilot sequence length, transmit power, and number of BS antennas. The simulation results show that the energy efficiency of the algorithm is 11. 2 % higher than that of the existing algorithm, indicating that the algorithm can effectively improve the system performance. [ABSTRACT FROM AUTHOR]

Aiming at the problem of order selections and multi-component equipment maintenance decisions under limited capacity when facing multiple orders with different requirements, the paper proposed a joint optimization model based on the customer satisfaction. Firstly, the method used the proportional risk model to describe the specific deterioration trend of each component and the virtual age to represent the health status of each component. Next, the paper determined the different maintenance methods based on the real-time monitoring results and maintenance threshold. Then, with profit maximization as the optimization goal, combined with limited capacity batch problem model and customer satisfaction model, the paper established the maintenance strategy based on the situation and the multi-order batch production joint optimization model. Finally, the paper used the simulated annealing algorithm and particle swarm optimization algorithm to solve the model, an example verifies the validity of the model in order to let the model have more practical significance, and carried out the sensitivity analysis of the corresponding parameters of the model. [ABSTRACT FROM AUTHOR]

This paper proposed the mathematical model with the goal of minimizing transportation costs and vehicle fixed costs which based on the urban waste classification collection and transportation problem, considering the vehicle loading capacity constraints, hard time window constraints, and the impact of loading rate on cost. Dynamic fusion of an improved ant colony algorithm that considered the time fit factor and the vehicle capacity utilization factor and chaotic electromagnetic field optimization algorithm, combined with 2-opt and two-point exchange local search method, this paper proposed a novel hybrid ant colony algorithm with improved ant colony algorithm as the external framework and chaotic electromagnetic field optimization algorithm as the internal module to solve the urban waste classification collection and transportation problem. According to the idea of complementary advantages between algorithms, using the advantages of the two algorithms to make up for the defects of a single algorithm, it successfully applied to this problem. Finally, it used the vehicle routing problem standard test set and data from Yangpu district, Shanghai as examples to test and compare, verifies the correctness of the model and the effectiveness and optimization capabilities of the algorithm. [ABSTRACT FROM AUTHOR]

GWO algorithm has drawbacks such as the irrationality of convergence. At present, there are few ways to improve the convergence of GWO algorithm. In addition, in the GWO iteration to the later stage, all gray individual wolves approach a wolf, B wolf and 8 wolf, causing the algorithm to fall into a local optimum. In order to address the above problems, this paper proposed an enhanced GWO algorithm DSF-GWO. This algorithm introduced a disturbance factor to balance the mining and exploration capabilities. Secondly, it introduced a somersault foraging strategy to prevent the group from falling into a local optimum, while also making the group's diversity slightly improve at the early stage. This paper verified the optimization per formance of DSF-CWO by 14 unimodal and multimodal objective functions. Under the same parameter settings, the experimental results show that DSF-CWO algorithm has obvious advantages in optimization performance over GWO algorithm. [ABSTRACT FROM AUTHOR]

The selection of build orientation in 3 D printing is one of the key factors that affect processing quality. Aiming at the current algorithm only with consideration of the optimization on the premise of equal thickness layering, this paper proposed a build orientation optimization algorithm in terms of an adaptive layered model. Firstly, this paper analyzed the surface accuracy of products under different build orientation in combination with the characteristics of 3 D printing layer by layer, and established a build orientation dependent optimization function to minimize volume error. Then, it employed the genetic algorithm to search the optimal solution in the global scope to determine optimal build orientation through steps such as STL model rotation transformation and ad aptive layering. Finally, this paper conducted the comparison between proposed and published algorithm. The experimental results show that the proposed algorithm can further reduce the volume error and improve the surface quality. [ABSTRACT FROM AUTHOR]

Aiming at the limitation of electric vehicle power to driving distance, this paper established a user reservation distribution model to obtain the order distribution result when profit was biggest. This kind of problem belongs to NP-hard problem, which is difficult to solve. The refore, this paper designed a new discrete electromagnetic field optimization algorithm to solve it. Base on the basic electromagnetic field optimization algorithm, it used the binary coding mode to change the particle movement mode, and for the negative electromagnetic field it added update process to the electromagnetic particles. Compare with genetic algorithm, binary particle swarm optimization, improved binary cuckoo search algorithm and binary lion swarm optimization, numerical experiments show that the new algorithm has high er computational efficiency. In addition, compare with the traditional distribution model, the new order distribution model can obtain higher profits, indicate the effectiveness of the model. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of overlapping community detection, this paper proposed a LPANNI optimization algorithm CLPANNI (Cycle Label Propagation Algorithm with Neighbor Node influence) based on cycle structure by mining the minimum circle information of nodes, measuring the importance of nodes according to the circle ratio index and updating labels in ascending order, the algorithm increases the stability of label propagation process, and receives the labels transmitted by neighbor nodes according to the influence of neighbor nodes. With four benchmark algorithms in NMI＿ LFK, NMI＿ Through the test and comparison under MGH and Mov indicators, CLPANNI algorithm performs well in the accuracy of community discovery. Experimental results show that the algorithm can effectively detect the overlapping community structure of the network, find the close sub clusters of the network, and the identified community distribution is closer to the real network structure. [ABSTRACT FROM AUTHOR]