Whale algorithm based on circle search and multiple opposition-based learning

In order to improve the population diversity and insufficient exploration and exploitation capacity of whale optimization algorithm, a multi-inverse composite whale optimization algorithm (CSOWOA) based on circular search mechanism is proposed. First, improvements were made for population diversity. The initial population is initialized by refractive backward learning through refraction, so as to find more hidden space and strengthen the diversity of the initial population. In the optimization process of the algorithm, the dominant population and the inferior population are divided by the size of the fitness value, and the multi-inverse composite method of refractive backward learning and random backward learning is applied to them respectively to ensure the diversity of the population in the process of algorithm seeking. Secondly, to improve the exploration and exploitation ability of the algorithm, adaptive weights combined with the population success rate are used to strengthen the bracketing search ability of algorithm, while the exploration and exploitation ability of the algorithm is strengthened by two circular search mechanisms in the bracketing search process to improve the convergence speed and the accuracy of the algorithm. Finally, normal variation is added to perturb the position of elite individuals to drive the whale populations that may fall into stagnation to avoid the algorithm from falling into local optimum. The simulation experiments are compared with several well-known improved whale algorithms and classical intelligent optimization algorithms in 13 benchmark test functions, and the comparison results show that CSOWOA has a significant enhancement effect.t.