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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]

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]

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]

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]

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]

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]

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]

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]

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]

In recent years, with the development of high-throughput chromosome conformation capture (Hi-C) technology and the reduction of high-throughput sequencing cost, the data volume of whole-genome interaction has increased rapidly, and the resolution of interaction maps keeps improving. Great progress has been made in the research of 3D structure modeling of chromosomes and genomes. Several methods have been proposed to construct chromosome structures from chromosome conformation capture data. Based on the Hi-C data, this paper analyzes the relevant literature of 3D structure reconstruction of chromosomes and summarizes the principle of 3DMax, which is a classical algorithm to construct the 3D structure of a chromosome. In this paper, a new gradient ascent optimization algorithm called XNadam is proposed, which is a variant of Nadam optimization method. When XNadam is applied to 3DMax algorithm, the performance of 3DMax algorithm can be improved, which can be used to predict the three-dimensional structure of a chromosome. [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]

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 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]

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]

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 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]

Particle swarm optimization algorithm is a stochastic global optimization algorithm, but it is easy to fall into local optima and premature. In order to overcome its defects, a diversity driven adaptive particle swarm optimization（DDA-PSO） algorithm is proposed in this paper. The algorithm includes the attraction stage and the driving stage. In the attraction stage, the linear decreasing inertia weight mechanism is used to accelerate the particle convergence, and in the driving stage, the diversity driving speed strategy is used to improve the population diversity. The two stages adaptively switch to each other, the particles can jump out of the local optima and prevent premature, the algorithm obtains an adaptive balance between the exploration and exploitation. The experimental results show that DDA-PSO algorithm improves the convergence speed and accuracy, and the global search ability is significantly improved. [ABSTRACT FROM AUTHOR]

Aiming at the problem of "over-exploitation" in dimensional learning strategy (DLS), this paper proposed a multiswarm collaborative particle swarm optimization algorithm with comprehensive dimension learning (CDL-MCPSO). The cluster structure of master-slave paradigm divides the population into a master group and four slave groups, in which the master group executes a comprehensive learning strategy to conduct a large-scale exploration in the search space, and the slave groups execute a comprehensive dimensional learning strategy (CDL) to exploit high-precision solutions near the local optimal. The master-slave groups can effectively achieve the balance between exploration and exploitation by executing algorithms with different functions. At the same time, in order to maintain the diversity of the population, the CDL-MCPSO proposed a new solution exchange mechanism (SEM), By doing this, the algorithm can accomplish information exchange and cooperation after the master-slave groups running their respective algorithms for several generations independently, so as to guide the particles to conduct more accurate searches in the later stage. Finally, for the high randomness of the initialization process, the algorithm adopted latin hypercube sampling to reconstruct the input distribution. In order to verify the effectiveness of CDL-MCPSO, compared with 5 kinds of PSO variants in 10 test functions. the results show that the algorithm can always find better or equivalent solutions. It is feasible and efficient in solving complex functions. [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]

This paper considered a wireless communication network with multiple UAV base stations and multiple users.In the network,due to the limited spectrum resource,the backhaul links of the UAV base stations and the data links of the users shared the same spectrum.In order to use the spectrum resource efficiently and improve the communication performance of the users,this paper jointly optimized the bandwidth and power of the backhaul and data links as well as the flight trajectory of the UAV base stations,to maximize the minimum average rate of all users.This joint optimization was subject to the UAVs’ mobility,the total spectrum bandwidth,and the total transmit power,and the involved joint optimization problem was non-convex,thus it was difficult to find the optimal solution.This paper proposed an efficient algorithm to obtain a high-quality suboptimal solution of it.Simulation results show that the minimum user rate achieved by the proposed joint optimization algorithm is significantly higher than that of the benchmark schemes. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of the traditional cuckoo search algorithm with low convergence accuracy and large limitation of iterative step for complex problems, this paper proposed a dynamic cuckoo search strategy based on rider optimization. The algorithm combined with the idea of rider optimization algorithm, using multiple groups and optimal strategies in a single cycle . The optimal strategy could dynamically enhance the searching and improve the convergence efficiency of the algorithm for complex problems. At the same time, this algorithm adjusted the dynamic parameters of Levy flight motion to improve the early and late performance of the algorithm search. The simulation test results show that the optimization performance of the imp roved algorithm ROCS is better than the comparison algorithms for complex problems, and the optimization efficiency of the algorithm has been significantly improved. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of COA, such as slow convergence speed, low solution accuracy and being easy to fall into local optimum, this paper proposed an improved coyote optimization algorithm based on dual strategy learning mechanism and adaptive chaotic mutation strategy ( DCSCOA ). Firstly, it adopted an oscillatory decline factor to generate diverse individuals for enhancing the global search ability. Secondly, it proposed a dual strategy learning mechanism to appropriately increase the influence of the group head wolf, so as to balance the local search ability and global search ability of the algorithm, and to improve the solution accuracy and convergence speed of the algorithm. Finally,it used an adaptive chaotic mutation mechanism to generate new individuals when the algorithm stagnates, so as to make the algorithm jump out of the local optimum. Through simulation experiments on 20 basic test functions and 11 CEC2017 test functions, the results show that the improved algorithm has higher solution accuracy,faster convergence speed and stronger stability. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of normal SPBO, this paper analyzed the psychological characteristics of students ' learning, and put forward an improved student psychology based optimization algorithm ( HSSPBO) using hybrid strategy . Firstly, it took the inverse value of student ' s test scores as the fitness value of the student, and took the fitness value of the best students in the class as the benchmark, and then divided the students in the class into four categories : the best students, the good students, the ordinary students and the students who tried random improvement. Secondly, it used the dynamic switching probability of sine square and cosine square to balance global exploration and local development, so that the global exploration ability and local exploitation ability of the algorithm were improved effectively. Thirdly, by introducing Cauchy mutation strategy to change the local search step size, it improved effectively the local search ability of the algorithm and enhanced the ability of the algorithm to jump out of the local optimization. Finally, the HSSPBO algorithm used Levy flight strategy to make the individual search step length more random and flexible, which effectively enhanced the individual ' s searching ability, and further improved the optimization speed of the algorithm. Through simulation experiments of 12 benchmark functions and comparison with six optimization algorithms, the results show that HSSPBO ' s global search ability has been significantly improved, and it has faster global convergence speed, better optimization accuracy and stability in function optimization. [ABSTRACT FROM AUTHOR]

The optimization performance of the PSO algorithm is affected by the speed update formula. Too fast convergence speed may cause the algorithm to miss the global optimal solution; too slow convergence speed may cause the algorithm to fall into the local optimal solution. Aiming at this problem, this paper proposes a PSO optimization algorithm based on improved compression factor, namely FPSO. By introducing the compression factor equation, the speed iteration formula is improved, and the influence on the algorithm caused by the improper setting of the learning factor is reduced. The new adjustment mechanism not only ensures the convergence performance of the PSO algorithm, but also weakens the influence of the speed boundary on the algorithm. Finally, five classical functions are selected to test the performance of the algorithm. The test results show that, compared with the traditional PSO algorithm, the proposed algorithm improves the global convergence ability and shortens the convergence time. [ABSTRACT FROM AUTHOR]

Yin-Yang-pair optimization algorithm is a novel lightweight optimization algorithm, which uses the iterative exchange of two points( the global exploration point P2 and the local exploitation point P1 ) to achieve the search of optimal solutions. The user defined parameters of the YYPO directly affect the balance of global exploration and local exploitation of the algorithm and impact the performance of the algorithm. In order to improve the optimization performance of the algorithm, this paper analyred effects of the user-defined parameters( expansion/ contraction factor a) to the performance of the original algorithm, then proposed the IYYPO algorithm. IYYPO adopted linear and non-linear strategy to gradually reduce user-defined parameters. This paper also used 28 test functions, which was the test benchmark of the competition of single objective real parameter algorithm in 2013 Evolutionary Computing Conference,as the testbench to evaluate the performance of three new algorithms. Compared with the original algorithm,experiment results illustrate that the proposed algorithm can archive higher accuracy and faster convergence speed. At last, this paper tested the improved algorithms in a practical optimization problem to show the performance of the improved algorithms. [ABSTRACT FROM AUTHOR]

In order to solve the problem of optimal aHocation of non-paper resource in chemical laboratories, a mathematical model is set up. We abstract the problem to find an optimal solution in a six-dimensional space. A constraint rule and an optimization rule are presnted, cost distance and equilibration value are given, wait time is defined based on open queuing network too. A fitness function is defined, optimal aHocation algorithm based on particle swarm algorithm is established. Simulation experiment on this algorithm shows that it is fast and efficient. [ABSTRACT FROM AUTHOR]

In the view of the problem of project makespan extended randomly which background with specific product moving assembly line under uncertain environment. Firstly, the paper broke an original project into multiple projects virtually by using the idea of project splitting. And it established an optimization mathematical model to make the objective function of minimizing project makespan on the basis of considering the maximum robustness constraints. This paper developed a two-level iterative algorithm. At the project splitting level, it adjusted jobs between projects by a job allocation heuristic algorithm. At the project scheduling level, it applied a self-adaption cuckoo algorithm scheme to generate feasible schedules and returned the result to the last level for iteration. To analyze project outcome, it changed selecting examples of different scales in PSPLIB. The results show that the proposed algorithm can improve the utilization rate of project resources and shorten the activity durations under uncertain environment. [ABSTRACT FROM AUTHOR]

Aiming at the problem of blank and overlapping coverage in node deployment of wireless sensor network, this paper proposed a network coverage optimization based on improved equilibrium optimizer (IEO) algorithm. Firstly, it used surround reverse learning to improve the quality of initial population and enhance the optimization ability of the algorithm. Secondly, it introduced dynamic sine and cosine factors to further balance global search and local development ability, so that particle population could search extensively and dig deeply in the search space. Finally, it added circle chaos map in the concentration updating stage to increase the diversity of population and improve the algorithm's ability to escape local optimum. The experimental results show that the IEO algorithm applied to the coverage optimization experiment of WSN can effectively reduce the deployment cost, show higher network coverage and improve the monitoring quality of the network compared with the standard balance optimizer algorithm and other improved algorithms. [ABSTRACT FROM AUTHOR]

Coyote optimization algorithm (COA) is a swarm intelligent algorithm proposed recently, which has a unique search structure and good optimization performance. In order to improve COA further, this paper proposed a multi-strategy COA (MSCOA). Firstly, the best coyote in a group used a global best coyote-guided growth strategy to improve its social adaptation, the worst coyote in the group used the best coyote guidance reinforcement strategy to strengthen the worst coyote's search ability. Secondly, the other coyotes in the group adopted a group growth strategy that dynamically adjusted information interchange to improve information sharing degree among the coyotes in the group, and MSCOA crossed this growth strategy with an improved migration strategy to further improve search ability. Finally, MSCOA adopted a dynamic grouping strategy to reduce the manual setting of parameters and improve operability. Hybridizing the above strategies can keep better balance exploration and exploitation to maximize the performance. A large number of experimental results on complex functions from the CEC2014 test set show that MSCOA has stronger search ability, faster running speed, and higher search efficiency than COA, and has more obvious advantages over quite a few other excellent algorithms. [ABSTRACT FROM AUTHOR]

The multi-scale quantum harmonic oscillator optimization algorithm(MQHOA)is a natural calculation algorithm based on quantum physics proposed in recent years. Aiming at the problem that the algorithm fails to make full use of the historical information in the iteration, this paper proposes a historical information-driven multi-scale quantum harmonic oscillator optimization algorithm(HI-MQHOA). In the two-step iterative process, HI-MQHOA introduces historical data as a driver to form the parameters of the next generation individual distribution and dynamically adjust the scale of the algorithm. The next generation individual distribution parameters can effectively guide the development and exploration of the algorithm, and dynamic scaling can avoid premature stagnation. Verified by several classical test functions, the algorithm is superior to MQHOA, improved MQHOA and other natural computing algorithms in solution quality, accuracy and scalability. [ABSTRACT FROM AUTHOR]

This paper proposed curve adaptive and simulation annealing grasshopper optimization algorithm to avoid the disadvantages that the grasshopper optimization algorithm was easy to fall into local optimum, slow convergence speed and poor search accuracy. First of all, this paper introduced the adaptive curve to replace the linear adaptation of the grasshopper optimization algorithm key parameter, which improved the global search ability of the algorithm. Then, on this basis, this paper introduced the simulated annealing algorithm, which had a certain probability of receiving the inferior solution of the grasshopper algorithm to jump out of local optimization and achieve global optimization. Adaptive reduction the range of random solutions of grasshopper position in simulated annealing was beneficial to further improve the ability of grasshopper algorithm's exploitation. Through test function, the experimental results show that the improved new algorithm has higher quality and better convergence speed. [ABSTRACT FROM AUTHOR]

In order to solve the shortcomings of seagull optimization algorithm(SOA),such as low precision, slow convergence rate in the later iteration,and fall into premature convergence easily. This paper proposed an improved seagull optimization algorithm( CtSOA), which combined chaotic map strategy and t-distribution mutation strategy. Tent map strategy made the initial seagull population evenly distributed in the search space, and t-distribution mutation strategy balanced the exploration and development capability of the algorithm. Combining the two strategies, CtSOA had higher global search accuracy and stronger ability to jump out of local extremum. Comparing with the SOA, other five meta-algorithms, the single strategy improved SOA, and other scholars improved SOA on 14 test functions, the experimental results show that CtSOA with two improved strategies has better convergence precision and faster convergence speed. [ABSTRACT FROM AUTHOR]

Butterfly optimization algorithm (BOA) is a novel nature-inspired metaheuristics algorithm proposed in recent years. The basic BOA is slow convergence, low accuracy and easy to fall into local optimum. To solve the above problem of basic BOA, this paper proposed an adaptive butterfly optimization algorithm based on mutation strategies ( ABO A-MS) . Firstly, it introduced the strategy of adjust the conversion probability dynamically, which effectively balanced the ability of the global exploration and local search, by means of dynamically adjusting the switching probability of the change information of iteration times and individual fitness. Secondly, it introduced the adaptive inertia weight strategy and local mutation strategy. It applied the inertia weight value and chaotic memory weight factor, which further improved the diversity of this algorithm, and effectively avoided its precocious convergence, as well as accelerated its convergence speed and solving accuracy. In order to verify the optimization performance of the modified algorithm, it carried out the simulation experiments among the modified algorithm, the basic BOA algorithm, the particle swarm optimization algorithm, the salp swarm algorithm, the gray wolf optimization and the others. Simulation results illustrate that the modified algorithm has excellent performance of fast convergence speed, high optimization accuracy and strong stability. [ABSTRACT FROM AUTHOR]

For the problem of multi-objective optimization, this paper proposed a multi-objective particle swarm optimization algorithm based on quasi-circular mapping（qMOPSO). Firstly, qMOPSO projected the solutions in high-dimensional objective space into the divided 2-dimensional quasi-circular space in order to monitor evolutionary status of the particle population. Then, the algorithm adopted quasi-circular dominant and individual distance based on sector blocks to design an archive maintaining algorithm which could balance diversity and convergence of solutions in the archive. In addition, it introduced an adaptive global best selection mechanism based on the solution distribution entropy to analyze the evolutionary tendency. Finally, the algorithm used a general management strategy based on changing dimension and elimination mechanism to enhance population management.qMOPSO was compared with five multi-objective optimization algorithms on three kinds of test suites. The simulation results show the correctness and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In order to find the shortest path and overcome the slow convergence speed and low accuracy of traditional algorithms. This paper proposed a mutation and crossover based on beetle swarm search algorithm（MBSO). Firstly, it transformed individual beetle into group beetle search for optimization. And then incorporation of mutation and crossover in the population evolution process to improve global search and get better results. Finally, it added the adaptive length and step size of beetle antennae to improve the exploration ability of the algorithm. By using MATLAB to simulate the data set in TSPLIB and applied to PON network planning problem, the experimental simulation results show that the improved algorithm has higher convergence speed and solution precision than other algorithms. The average running time of the algorithm is reduced by 0.3 s, the experimental results are closer to the optimal solution. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithm-SVM is a typical framework of multi-classification evaluation model, which has important theoretical and practical significance in multi-classification comprehensive decision-making. Therefore, this paper proposed a multi-classification evaluation algorithm of an improved whale optimization algorithm using Lévy flight and elite opposition-based learning SVM（LFEO-BWOA-SVM）. It used Lévy flight strategy instead of spiral trajectory strategy to update position information effectively, and could overcome the deficiency of whale optimization algorithm which was easy to fall into local optimization. It introduced the elite opposition-based learning mechanism to increase population diversity and improved the global optimization ability of whale optimization algorithm. The simulation results show that the classification accuracy of LFEO-BWOA-SVM algorithm is 17.84% and 4.51% higher than that of traditional SVM and BP neural network, the accuracy is 98.73%, and the training time is 9.34% and 84.94% shorter than that of standard WOA-SVM and PSO-SVM, respectively. The experimental results show that the optimization ability and convergence speed of LFEO-BWOA-SVM algorithm are obviously improved, and the accuracy and rapidity are good. [ABSTRACT FROM AUTHOR]

The HYCOM mode requires higher I/O throughput and less computation, which brings great challenges to the design of high salable parallel algorithm. Therefore, this paper designed a parallel optimization algorithm based on region decomposition for high-throughput ocean data assimilation. First of all, it proposed a flexible file access approach for reading a large number of data from disks. As a result, it avoided the data accessing conflict successfully, and decreased the frequency of disk addressing operations significantly. Next, it designed a communication-avoiding strategy to reduce the communication volume at the cost of some additional computations. Furthermore, it presented a pipe-flow scheme for data exchange to conduct conflict-free message passing. Consequently, it developed a fast data-obtaining algorithm for the data assimilation. The experiments show that the fast data-obtaining algorithm gains a performance of 5 x speedup compared with the original one. More specifically, the new approach achieves 6 x speedup on average for the file reading process and 2.7 x speedup on average for the communication between processors. [ABSTRACT FROM AUTHOR]

Aiming at the problem that imperialist competitive algorithm is weak in local search ability and easy to fall into local optimum in solving traveling salesman, this paper proposed an imperialist competitive algorithm based on adaptive inheritance strategy. The algorithm adopted the heuristic crossover operator of adaptive inheritance strategy, single point local insertion strategy and the fixed adjacent area 2-opt operator to enhance the local optimization ability of the algorithm. Added the imperial elite solution set to maintain the diversity of the population. The standard examples tests verified the effectiveness of the proposed improvement strategies. Compared with other algorithms based on heuristic crossover operator and imperialist competitive algorithm framework, the results show that the algorithm has higher precision and faster convergence speed in solving small and medium-sized traveling salesman problems. [ABSTRACT FROM AUTHOR]