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To address the problems of the butterfly optimization algorithm, which include vulnerability to local optima,low optimization accuracy and slow convergence speed. This paper proposed a hybrid optimization algorithm for salp swarm and butterfly with reverse mutation towards optimization learning. The algorithm introduced Cauchy mutation to disturb the optimal butterfly individual to avoid the algorithm falling into local optimization. Embedding the improved salp swarm algorithm (SSA) into BOA adjust the proportion of global exploration and local mining, thereby enhancing the algorithm’s convergence speed. Using the reverse mutation towards optimization learning strategy enhanced the algorithm’s search space and improved the quality of solutions, consequently bolstering its overall optimization accuracy. The experimental results, obtained from conducting simulations on 10 benchmark functions,showcase the exceptional optimization performance and robustness of the improved algorithm. [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]

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 traditional immune network optimization algorithm had the shortcomings of weak local searching ability, low precision and premature convergence. Inorder to improve the algorithm performance, this paper proposed an artificial-immune-network-based memetic algorithm for dynamic optimization problems. Based on the framework of memetic algorithm, an artificial immune network algorithm served as the global search algorithm, and a tabu search algorithm served as the local search operator. At the same time, the algorithm introduced the Cauchy variation to improve global searching ability and prevent premature convergence . The experimental results show that the improved algorithm has better search precision and convergence speed compared with other algorithms. [ABSTRACT FROM AUTHOR]

To solve the problem of being slow convergence and prone to premature in the classical bat algorithm，this paper proposed an elites cross binary bat algorithm (ECBBA). This algorithm carried out the elites strategy and the cross-fertilize， which selected some elites in the bats group to make cross firs tly，and then it chose the bats with higher fitness from the offspring and parents as the new generation bats. The algorithm could keep multiformity and superiority of the bat group and improve the ability of getting the best solution in global. To improve ability of the local searching, ECBBA took the greedy strategy in computing the fitness of every bat. On the other hand，through monitoring the change of the global best bat，the algorithm would make Cauchy mutation in some bats which could help the algorithm to jump the local optima. Simulation experiment on 50-1 KP shows that the proposed ECBBA has faster convergence rate，higher precision and stronger capability of searching global optimal solution than the GMOGAS，GBBA and VEBA algorithms. [ABSTRACT FROM AUTHOR]