1.Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks, Henan University;2.College of Software, Henan University;3.Institute of Management Science and Engineering, Henan University
the Key R&D and Promotion Projects in Henan Province, China (grant number 182102310886)
Aiming at the disadvantages of whale algorithm, such as poor stability, slow convergence speed and easy to fall into local extremum, a two-population interactive evolutionary whale algorithm with roulette selection and quadratic interpolation mechanism was proposed. The roulette selection mechanism is introduced in the searching and foraging stage, which effectively avoids the problem that the poor solution is selected several times and ensures the convergence performance of the algorithm. In the evolutionary structure and solution process of the algorithm, the population of two different evolutionary mechanisms and the continuous information interaction between them are used to balance and adjust the global search and local search ability of the algorithm effectively. The quadratic interpolation strategy was used to update the position of the whale individuals after the evolution update of the two populations and before the information exchange, which increased the diversity of the population, and then the optimal selection of new positions improved the convergence rate of the algorithm. Then the algorithm flow is given and the convergence of the algorithm is proved by probability measure method.Finally, six representative algorithms are used to simulate different characteristic functions in CEC2017 test function suite in multiple dimensions. The results show that the improved algorithm has better convergence speed, optimization precision and solution stability, and has good convergence performance.