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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 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]

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]

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]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 problem that Harris hawks optimization algorithm converges slowly and is prone to local optimization, this paper proposes an improved Harris hawks optimization algorithm (IHHO) which combines mutually beneficial symbiosis and lens imaging learning. Firstly, the algorithm uses Tent chaotic map to initialize the population to increase the diversity of the population and improve the optimization performance of the algorithm. Secondly, in the exploration stage, the algorithm integrates the idea of mutually beneficial symbiosis and nonlinear inertia factor to enhance the exchange of population information and accelerate the convergence speed. Then, the algorithm uses lens imaging reverse learning strategy to perturb and mutate the Harris hawks position with a certain probability to improve the ability of the algorithm to jump out of the local optimum. Finally, the simulation results of 16 benchmark test functions show that IHHO has faster convergence speed, higher precision and stronger robustness compared with the other five algorithms. At the same time, IHHO is applied to the problem of image segmentation, and the simulation results verify the feasibility of the algorithm in practical engineering applications. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of particle swarm optimization (PSO) algorithm, which is easy to fall into local optimum and low precision during iteration, this paper proposes a self-conclusion and self-adaptive variation particle swarm optimization (SCVPSO). Firstly, the position of each particle is dynamically updated by nonlinear turning up and then decreasing inertia weight to avoid premature. Secondly, the local particles are searched backward to improve the efficiency of population optimization. Finally, a new parameter scr (self-conclusion rate) is introduced to summarize the recent solution situation of each particle, and the probability directed variation is used to guide the particles to the global optimum to increase the diversity of particles. With the help of 15 test functions, compared with other variant particle swarm optimization algorithm, the results show that the improved algorithm is significantly better than other algorithms in solving performance, which verifies the effectiveness of the strategy. [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]

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]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

Copyright of Oil Drilling & Production Technology / Shiyou Zuancai Gongyi is the property of Shiyou Zuancai Gongyi Bianjibu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Feature selection is a common dimension reduction approach for processing high-dimensional big data, but it often involves multiple conflicting feature subsets evaluation objectives which are difficult to balance . To reach a compromise among various feature subset evaluations in feature selection and optimize the performance of subset, this paper proposed a subset evaluation multi-objective optimization based feature selection framework and focused on the application of multi-objective particle swarm optimization( MOPSO) in feature subset evaluation. The framework used sparsity, classification ability and information loss to design multi-objective optimization functions. Then it optimized the weight vectors of the features based on multi-objective optimization algorithm, and selected the " knee" of Pareto solution set as optimal vector. Finally, the framework realized feature selection based on weight vector ranking. This paper designed experiments to compare the performance of MOPSO based feature selection( FS_MOPSO) with four classical methods. The results on several standard data sets show that, FS_MOPSO shows higher classification accuracy in low dimensional space while ensuring less information loss. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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 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]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of China University of Petroleum is the property of China University of Petroleum and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

In order to improve the efficiency of manufacturing service composition optimization in the cloud manufacturing environment, this paper proposed a composition optimization method based on the modified polar bear algorithm. This method encoded the manufacturing services in real numbers, took the service function and service quality as evaluation indicators, a nd used the modified polar bear algorithm to solve the optimization problem of manufacturing cloud service portfolio, a nd obtained the optimal service portfolio plan. At the same time, this article used the dynamic vision to adjust the local search of the algorithm, and combined it with the mutation strategy in the genetic algorithm. Through these improvements improved the efficiency of the algorithm for solving multi-objective problems and reduced the possibility of the algorithm falling into local optimum due to the influence of initial parameters. Example analysis shows that compared with the original polar bear algorithm, the standard genetic algorithm, the improved gray wolf optimizer and the improved particle swarm optimization algorithm, the modified polar bear algorithm is more efficient in solving the problem of manufacturing cloud service portfolio optimization. [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]

This paper proposed improved particle swarm optimization algorithm( GB_PSO ) for robot path planning to address the problems of easy to fall into local misconceptions when solving robot path planning by a single intelligent optimization algorithm. The algorithm took on the PSO as the main body,due to the GA and the BFO algorithm update strategies was subject to different environmental influences, fitting two environmental parameters. Then the correlation between particles and different environmental parameters calculated to classify the particle swarm into two classes, and local optimization enhanced in parallel by selection, crossover, and variation operators of GA and convergence operations of BFO, respectively . Finally the improved p article swarm update formulation used to achieve the optimization of global and local paths of the robot. The experimental results show that the GB_PSO algorithm for path planning improves the local and overall search capability. The path planning is fast and short distance, while having stronger robustness. [ABSTRACT FROM AUTHOR]

This paper proposed an improved salp swarm algorithm that aimed to solve the problem of low precision and slow convergence speed in solving function optimization problems. For leaders, this algorithm replaced the optimal individual position with weighted centroid to prevent premature aggregation near the optimal individual. It also introduced adaptive inertia weight to the followers to balance the global search and local optimization capabilities of the algorithm. Furthermore, this algorithm contributed to reducing the inter-dimensional interference and improving the diversity of the population by dimensional random difference mutation. The simulation results illustrate that the improved algorithm is better than the standard tympanum group algorithm and other improved algorithms in terms of mean, standard deviation and convergence curve. The results also show that the improved algorithm improves the performance of optimization, and has higher precision as well as faster convergence speed. [ABSTRACT FROM AUTHOR]

Swarm intelligence algorithm has become a hotspot in the field of evolutionary algorithm because of its strong dynamic optimization ability and simple implementation approach. The choice of control parameters has a great influence on the optimization performance of the algorithm. Firstly, this paper studied the particle swarm parameters from the point of mathematical derivation, then proposed an improved algorithm with inverse thinking curve increment strategy, which fitted the evolution formula of the particle itself. Finally, it verified that the algorithm has the following two outstanding advantages. It can effectively avoid the problem of precocity. In dealing with the problem of dimensional disaster, the optimization performance is stronger and has a good balance between global and local optimization performance. The algorithm is simple and can effectively solve the problem of low robustness and tedious manual parameter adjustment. [ABSTRACT FROM AUTHOR]

In order to solve the difficulties in flight control law design of a helicopter caused by its strong coupling characteristics, an improved linear quadratic regulator (LQR) design method based on particle swarm optimization algorithm is proposed. Based on the linearized state space model of a helicopter, a basic algorithm to solve the full state feedback matrix in helicopter flight control law is established based on LQR design method. A cost function for particle swarm optimization, which maximizes the influence factor of main state feedback coefficients and takes the stability of the system as constraint, is designed. The multiparameter synchronous optimization of weighting matrix Q is implemented, and the optimized main state feedback coefficients are used as control law design results of a helicopter. The flight control law of a UH-60A helicopter at hover state is designed and verified. The results show that the control law designed by using this method has good performance and the flying quality of the helicopter is increased considerably. Meanwhile, the difficulty in determining weighting matrix during LQR design procedure is solved systematically based on the optimization method developed in this paper, and the efficiency in control law design is increased. [ABSTRACT FROM AUTHOR]

For the capacitated vehicle routing problem(CVRP),this paper proposed an imperialist competitive algorithm which integrated a split mechanism to solve the problem.Firstly,combined with the characteristics of CVRP,this algorithm used the encoding and decoding strategies based on greedy criteria to switch from the algorithm space to the solution space.Secondly,this algorithm presented an imperialist splitting mechanism to improve the global search ability of the algorithm,and simultaneously combined with the 2-Opt algorithm to enhance the local search ability.Finally,the results of simulation experiments with 25 benchmark examples indicate that:the proposed algorithm can effectively solve CVRP,and the optimization errors of all examples are less than 1.0%.Furthermore,the proposed algorithm is more efficiently than the existing imperialist competitive algorithms,particle swarm optimization algorithm,genetic algorithm and cuckoo search algorithm. [ABSTRACT FROM AUTHOR]

In order to solve the problems of slow search speed, premature convergence and low search accuracy of standard whale optimization algorithm, this paper proposed a hybrid strategy to improve whale optimization algorithm. Firstly, it increased the population diversity by generating the initial population with chaotic map, which laid a foundation for the algorithm global search. Then, by the non-linear strategy, it improved the convergence factor and inertia weight to balance the global exploration, local development ability of the algorithm and accelerated the convergence speed. Finally, according to the variance of the group fitness, it set the threshold performing the mutation operation to avoid the premature convergence of the algorithm. By testing 12 typical benchmark functions in three aspects, the experimental results show that the improved algorithm has a remarkable enhancement in search speed and convergence accuracy. Besides, it has a strong ability to get rid of falling into local optimum. [ABSTRACT FROM AUTHOR]

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