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This paper proposed an optimization method for majority consensus voting algorithm to improve the security of mimic adjudicators . This mimic ruling method based on executive outliers used anomaly detection to directly quantify data reliability to improve voting accuracy. Through constructing the executive body output data set and training the deep learning model, it quantified the abnormal value of the executive body output data. Then, it used the weight optimization algorithm to optimize the weighted distribution of the two parameters. Finally, it selected the optimal weighted result as the voting output result during voting. The experimental results show that the proposed method can improve the accuracy of mimic voting output and has a certain common mode escape detection capability. The optimization model can significantly improve the safety performance of the system. [ABSTRACT FROM AUTHOR]

Aiming at the defects of butterfly optimization algorithm, such as low accuracy and easy to fall into local optimum, this paper proposed an improved butterfly optimization algorithm for mixed strategy. Firstly,in order to increase the diversity of initial individuals, this algorithm used Circle map to initialize the position of butterfly individuals. Secondly,in the phase of local search, it used dynamic switching probability to control the conversion between sine and cosine algorithm and butterfly optimization algorithm, so that it fully utilized a small number of butterflies to enhance the local development ability of the algorithm. Then, it introduced adaptive cotangent weight coefficients at global and local position updates to control the movement direction and distance of the next generation of butterflies and improve the convergence speed and accuracy of the algorithm. Finally, it introduced a dimensional-by-dimension mutation strategy to mutate the global optimal position,guide the population to evolve to the optimal position and avoid falling into the local optimum. The simulation results of eight benchmark functions show that the improved algorithm has better convergence performance. Compared with other improved algorithms, it has a certain competitiveness. [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]

In order to solve the problem that users deep interests and preferences cannot excavate in the recommendation. algorithm, which leads to low extraction accuracy and high time complexity of similar users clustering accuracy, this paper proposed a clustering optimization recommendation algorithm (MT-QRPD) based on the evaluation quantization model to optimize differential evolution. Firstly, it used the feature timing of BiGRU network and the strong local feature effectiveness of CNN network to extract the comment depth features, and used the multi-dimensional semantic feature screening of multi-head attention mechanism to mine the comment depth semantic features. Then, through the nonlinear transformation of the multi layer perceptron carried out the multi-feature fusion to achieve accurate quantification. Finally, the algorithm optimized the clustering of similar users with PCA, and completed the project recommendation. The result of experiments shows that the pro posed algorithm can improve the accuracy, time complexity and recommendation performance. [ABSTRACT FROM AUTHOR]

Aiming at the problems of unreasonable division of data gridding, poor parameter optimization ability and low efficiency of parallelization in big data density-based clustering algorithm, this paper proposed a density-based clustering algorithm by using improve fruit fly optimization based on MapReduce, named MR-DBIFOA. Firstly, based on KD-Tree, it designed a division strategy to divide the cell of grid adaptively. Secondly, this method proposed an improve fruit fly optimization algorithm which used KLSS and CFF. Then,based on IFOA algorithm, it dynamically selected the optimal parameters of local clustering, which could improve the clustering effect of local clustering. Meanwhile, in order to improve the parallel efficiency, it proposed a density-based clustering algorithm using IFOA to parallel compute the local clusters of clustering algorithm. Finally, based on QR-Tree and MapReduce,it proposed a clusters merging algorithm (MR-QRMEC) to get the result of clustering algorithm more quickly, which improved the core clusters merging efficiency of density-based clustering algorithm. The experimental results show that the MR-DBIFOA has better clustering results and performs better parallelization in big data. [ABSTRACT FROM AUTHOR]

Concerning the high-dimensional biomedical data contains a large number of irrelevant or weak correlation features, affecting the efficiency of disease diagnosis, this paper proposed a high-dimensional biomedical data feature selection method based on improved shuffled frog leaping algorithm. This method introduced chaos memory weighting factor and balance group strategy into the shuffled frog leaping algorithm. The method enhanced the diversity of the algorithm, maintained the balance between global and local optimization of the algorithm, reduced the probability of the algorithm falling into local optimality and further improved the exploring ability of the shuffled frog leaping algorithm in feature space. The simulation results show that compared with the improved GA and PSO algorithm, the improved shuffled frog leaping algorithm feature selection method can significantly enhance the recognition and classification accuracy of related feature subsets. [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]

Black box attack is mainly focuses on the decision information, but there are many query times and the selection of sensitive pixels is difficult. Therefore, this paper proposed a defect sample generation algorithm based on differential evolution. By defining the black box attack as an unconstrained optimization problem, the algorithm used differential evolution to search image sensitive points, and optimized the loss function of decision definition and gradient calculation based on the deep learning model to achieve an effective black box attack. On MNIST and CIF ARlO datasets, the average query times of the proposed algorithm decreased by 28.3% and 14.8% respectively under the same attack success rate. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the WOA was easy to fall into local optimum and low convergence precision, this paper proposed an improved whale optimization algorithm (IWOA). The algorithm initialized the population by quasi-reverse learning methods and improved the diversity of the population. Then the algorithm modified the linear convergence factor to a nonlinear convergence factor, which was beneficial to balance the global search ability and local development ability. In addition, the algorithm improved the local search ability of the whale optimization algorithm by increasing the adaptive weight and imp roved convergence precision. Finally, the algorithm adjusted the whale optimization algorithm in time by a random differential mutation strategy to avoid falling into the local optimum. It selected nine benchmark functions in the experiment, and iterated all the algorithms 30 times. The improved whale optimization algorithm compared to the original whale optimization algorithm and five improved whale optimization algorithms, the results show that the mean and standard deviation of the algorithm are better than other algorithms, the convergence curve of the algorithm is also superior to most other algorithms. It shows that the imp roved whale optimization algorithm has the best convergence accuracy and algorithm stability, and the convergence speed is significantly faster than most other improved whale optimization algorithms. [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]

Butterfly optimization algorithm is a newly natural heuristic algorithm, which simulates the butterfly's behavior of using smell to determine the location of nectar or mating object. To solve the problem that the butterfly optimization algorithm was prone to low accuracy and slow convergence, this paper proposed the crazy butterfly optimization algorithm integrating with adaptive perturbation (CIBOA). Firstly, it introduced the adaptive inertial weights to the self-cognitive flight part to balance the local and global search capabilities of the algorithm. Secondly, it introduced the perturbation strategy to the global optimal position to avoid the algorithm falling into the local optimal. Finally, it introduced the crazy operator to the nectar location to increase the population diversity and obtain better optimal solution. To compare the search performance of five algorithms in 10, 30 and 50 dimensions used eight benchmark functions. The experimental simulation results show that the comprehensive performance of the improved algorithm is better than other algorithms. [ABSTRACT FROM AUTHOR]

The traditional collaborative filtering recommendation algorithm has the problems of low timing and high computational complexity of similar users with sparse data when there are too many users. According to the above problem, this paper proposed a spectral clustering optimization recommendation algorithm combining the minimum hash signature and temporal model prediction. Firstly, it used the MHS and Levenshtein distance measure to extract similar users from user-item scoring matrix. Then it used AIM-RT prediction algorithm with weight fitting to complete sparse matrix of similar users. Finally,it carried out similar user optimization by combining spectral clustering to find the optimal set of similar users and completed the final recommendation. Experimental analysis and verification show that the proposed recommendation algorithm can improve the overall recommendation performance in terms of computational complexity, scoring prediction accuracy, data missing filling and other aspects. [ABSTRACT FROM AUTHOR]

Under the background of big data, K-means has local optimum solution and slow clustering speed caused by choosing centroid. The paper developed a new clustering optimization and parallel algorithm CK-means based on K-means. For algorithm optimization, Canopy algorithm could determine the number of clusters k and select the initial centroid. For parallelization acceleration, F1ink platform could help to design a parallel acceleration strategy for CK-means. It also could analyze the impact of different parallelism degrees on computing time-consuming. Experiments show that CK-means algorithm has higher accuracy and better performance. The performance ratio in the iris data set is increased by 44. 79%, and the performance ratio in the wine dataset is improved by 32. 03%. The clustering time of CK-means algorithm under different parallelism degrees decreases first and then rises, and the minimum clustering time is related to the size of the data set. [ABSTRACT FROM AUTHOR]

Aiming at the problems of easy falling into local optimum and slow convergence speed in basic butterfly optimization algorithm, this paper proposed a global optimization butterlly algorithm. It introduced limit threshold to limit the number of times butterfly optimization algorithm fell into local optimum solution, so as to change the problem that the algorithm was easy to fall into premature. It combined simple strategy to optimize iteration. For the butterfly individuals with poor position in the later stage, the population could converge to the global optimum faster. It used the sine-cosine algorithm as a local operator to improve the shortcomings of population diversity decline in the later stage of iteration and accelerate the algorithm to jump out of the local optimum. Compared with several algorithms in the simulation experiment, the results show that the improved algorithm has better optimization performance. [ABSTRACT FROM AUTHOR]

Aiming at the shortages of the basic whale optimization algorithm with low optimization precision, slow convergence speed and easy to fall into local optimum, this paper proposed a new algorithm based on dynamic search and cooperative learning. Firstly, it used equivalent replacement and Faure sequences to enhance the quality of initial solution. Secondly, it improved the population diversity and enhanced the ability to jump out of local optimal solutions through the division of population. Finally, in order to improve the convergence rate and precision, it dynamically adjusted the search strategy of the algorithms according to the population evolutionary information. The experimental results show that the proposed algorithm is much better than basic whale optimization algorithm and its several improved algorithms in optimization performance. [ABSTRACT FROM AUTHOR]

Aiming at the problem, such as novel COA has slow convergence and insufficient global search ability when solving complex optimization problems, this paper proposed an algorithm called GCCOA. Firstly, during the growth of the coyotes in packs, it embedded a global-best guidance coyote to make the exploitation more powerful and to improve the convergence quality of COA. Then, it adopted a cultural tendency with coyo te interaction, which was impacted by the interaction of the coyotes in the pack, to improve the global search ability of COA. Finally, it applied GCCOA to complex function optimization and image enhancement. A lot of experimental results on 29 complex functions from CEC2017 set show that GCCOA obtains 22 times of the first place and has better optimization ability than COA, HFSPO, CSPSO and 13-GWO. In additional, the experimental results on medical image enhancement show that GCCOA can solve the optimization problem better than comparison algorithms such as COA and so on. So GCCOA is an optimization algorithm. [ABSTRACT FROM AUTHOR]

Based on quantum theory, this paper proposed an improved wolf pack optimization algorithm for the comprehensive optimization of sparse linear arrays with multiple constraints. The new algorithm greatly improved the global optimization ability through the special coding of quantum bits, stagnation detection and selective mutation. It gave the flow chart of the improved wolf swarm algorithm. Given the array aperture and the number of elements, the algorithm could simulate the peak side-lobe level( PSLL) of sparse linear arrays with arbitrary minimum array spacing constraints. By solving the space transformation, it effectively avoided the infeasible solution in the process of updating wolf group position, reduced the judgment steps, and improved the optimization efficiency. The simulation results show that the method is efficient and stable, and has good popularization value. [ABSTRACT FROM AUTHOR]

To solve the problem that the ant lion optimization algorithm was prone to local optimal stagnation, low convergence accuracy and slow convergence speed, this paper proposed the ant lion optimization algorithm integrating with Lévy flight and golden sinusoids ( LGSALO). This algorithm used the variation mechanism of Lévy flight to carry out mutation operation on the position update mode in the optimization process, which could improve the population diversity, make the algorithm jump out of the local optimum, improve the global optimization ability, and avoid the premature convergence of the algorithm to some extent. At the same time, the algorithm introduced the golden sine algorithm to improve the optimization mode of the elite ant lion and coordinate the ability of global exploration and local development. The experimental simulation results show that the improved algorithm has good performance of optimization and strong development ability. [ABSTRACT FROM AUTHOR]

To overcome the local optimum and premature convergence due to loss of population diversity of the particle swarm optimization algorithm, this paper proposed a disruption particle swarm optimization algorithm based on exponential decay weight (EDW -D PSO). Firstly, it semi-uniformly initialized the population to distribute the population in an overall uniform, locally random manner. Secondly, it introduced the dynamic splitting operator to perform splitting operations on particles which satisfying the splitting condition, increasing the diversity of the population and avoiding the particles falling into local optimum. Finally, it used the exponential decreasing inertia weight to balance the global search and local development ability of the particles. The experimental results show that the algorithm has a large search space in the early stage, and the population diversity increases. In the later stage, emphasizing the local development to improve the convergence precision and optimization ability, it can also accelerate particles jumping out of the local extremum and approximate globlal optimum. [ABSTRACT FROM AUTHOR]

In order to improve the performance of whale optimization algorithm for solving complex function optimization problems, this paper proposed an improved whale optimization algorithm based on adaptive parameters and niche technology. Firstly, the algorithm introduced an adaptive probability threshold to coordinate the global exploration and local development abilities. Then, the algorithm used adaptive position weights to adjust the whale position update formula to improve the convergence speed and search precision. Finally, the algorithm used preselection niche technology to avoid premature convergence. The results on 12 typical benchmark functions show that the improved algorithm has faster convergence speed and higher search precision than other comparison algorithms. It proves that the improvement strategy can effectively improve the performance of the whale optimization algorithm for solving complex function optimization problems. [ABSTRACT FROM AUTHOR]

In order to solve the disadvantage of the traditional whale optimization algorithm, which is slow convergence and easy to fall into local optimum, this paper proposed a mixed strategy based whale optimization algorithm. Firstly, it introduced the nonlinear adjustment strategy to modify the convergence factor, balanced the exploration and exploitation capability and accelerated the convergence speed. Then, it introduced an adaptive weighted coefficient into the position update formula of whales to improve the search precision of the algorithm. Finally, it combined the limit threshold idea of artificial bee colony algorithm to effectively jump out of the local optimum and prevent premature convergence. The results show that the proposed algorithm has better search precision and convergence speed through experiments on different dimensions of 14 benchmark functions. [ABSTRACT FROM AUTHOR]

For the reason that the existing algorithm on Hadoop doesn't have a high level of optimization, this paper presented a novel task scheduling algorithm based on data locality speculation. By calculating the time duration ratio of Map and Reduce task on each node combined with the local characteristics of tasks and data on different nodes, the algorithm introduced a more accurate task detection mechanism, and then launched backup tasks of slow tasks on fast nodes. For using computing migration instead of data migration, the algorithm can obtain higher efficiency. Experimental results in Hadoop show that compared with the existing scheduling algorithm, the algorithm proposed in this paper can shorten the task average operation time and reduce the network congestion caused by data exchange between cluster racks. It also can speed up the task execution efficiency. [ABSTRACT FROM AUTHOR]

The traditional particle swarm optimization algorithm uses constant learning constants for social and self -cognition to limit the population’s global coordination ability. In the late convergence of the algorithm, the diversity of the population was lost and all the individuals converge to one point in search space, which could trigger the precocious convergence. In view of this defect, this paper proposed a chaotic map particle swarm optimization algorithm based on variable learning factor. In the early stage of the algorithm, the emphases should focus on the best location of self-recording. At the later period of the algorithm, it should design a coordinated dynamic learning factor to converge on the best position of population. In order to overcome the premature phenomenon and determine the variance of population diversity below the set value, it used chaotic mapping updated the optimal individual location of the generation and utilized a new way to optimized. The experimental show the new algorithm has better performance in convenience speed and precision. [ABSTRACT FROM AUTHOR]

In order to improve the driving effect of the control system based on MAP and reduce the storage capacity of the control system effectively, this paper proposed a new method for selecting the fixed points in the MAP based on the improved particle swarm optimization algorithm. Taking the MAP of hydraulic mechanical continuously variable transmission ratio control system as an example, it divided 2 variables of the abscissa coordinate in its definition domain, and used the improved particle swarm optimization algorithm to select the number and location of coordinate points in each section after equal division. The selection process was based on the principle of multi-objective optimization, which combined the actual values of 100 randomly generated points with the average error of linear interpolation of MAP and the number of selected scalar points. In order to improve the efficiency of the algorithm, it improved the iterative criteria, the inertia weight and the learning factor of the particle swarm optimization. The results show that the improved particle swarm optimization algorithm has the advantages of fast convergence speed and high optimization accuracy, and the MAP with better control effect can be made with less calibration data . [ABSTRACT FROM AUTHOR]

Aiming at the limitation of traditional optimization algorithm; for blind source separation (BSS), this paper proposed a blind speech separation algorithm based on the improved glowworm swarm optimization (IGSO). The new algorithm constructed a right function to adjust adaptively the flight span,so it could not only accelerate the convergence speed,but also avoided the premature convergence. The mixed speech separation experiments show that the proposed algorithm performs better than BSS based on nature gradient algorithm (NGA), GSO and particle swarm optimization (PSO), and improves both convergence rate and separation ability. [ABSTRACT FROM AUTHOR]

This paper proposed two kinds of measurements based on close degree between the cloud models had the same nature concepts. Algorithms were simple, especially still maintained the accuracy in few cloud drops. The experimental results show that the proposed algorithm has obvious optimization accuracy and consumption, meanwhile appeares good feasibility and validity in classification problems based on cloud model. [ABSTRACT FROM AUTHOR]

This paper firstly introduced FastICA and RobustICA algorithms, and then researched the objective functions and optimization algorithms of these two algorithms. Furthermore, it analyzed and compared the performance of these two algorithms about robustness and computation complexity. As a result, the general performance of RobustICA is better than FastICA. [ABSTRACT FROM AUTHOR]