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Aiming at the disadvantages of the HGS algorithm such as slow convergence and the tendency to fall into local optima, this paper presented a hunger games search algorithm based on equilibrium pooling and Levy flight. It employed tent mapping to generate a more diverse initial population. Inspired by the equilibrium optimiser algorithm ( EO), this paper proposed an update formulation based on the dynamic equilibrium pool convergence principle, whose dynamically tuned update strategy strengthened the global searching ability. To step out of the partial optimum furthermore, it performed a mutation operation based on Levy flight on population for certain condition. Simulation experiments were carried out for 23 benchmark functions. The results show that compared with original HGS algorithm, ELHGS has higher accuracy and converges more rapidly, which is obvious for high-dimensional multi-peaked functions. [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]

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 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]

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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers 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.)

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 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]

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]

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]