Search

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]