Whale optimisation algorithm based on Kent mapping and adaptive parameters

Abstract

Aiming at the shortcomings of whale optimisation algorithm, such as easy to fall into local optimisation and slow convergence speed in the later stage, an optimisation method based on three improved strategies is proposed. Firstly, Kent mapping is introduced to initialise the population and enrich the diversity of the population; Secondly, a nonlinear convergence factor strategy is proposed to improve the global search speed and local optimisation accuracy. Finally, inertia weight is added to maintain the balance between global search and local optimisation. Simulation experiments with 13 standard test functions show that the proposed algorithm has remarkable performance in global search, convergence speed and optimisation accuracy. In addition, through its application in path planning, the feasibility and effectiveness of the algorithm proposed in this paper are further verified.