An improved Tasmanian devil optimization algorithm based on sine-cosine strategy with dynamic weighting factors

Abstract

In this paper, aiming at the problem that the balance between exploration and exploitation of traditional Tasmanian devil optimization algorithm is unflexible, and easy to fall into local optimum, an improved Tasmanian devil optimization algorithm (NTDO) based on the sine-cosine strategy of dynamic weighted factors is proposed. The designed method balances the global and local search capabilities of the algorithm, effectively improves the situation that the algorithm falls into local optimum, and integrally improves the optimization performance of the algorithm. Firstly, the good point set theory is used instead of the traditional random method to find the initial individuals, which can render the initial population is more evenly distributed in the search space and the population diversity is improved. Secondly, A sine-cosine strategy based on dynamic weighted factors is proposed to coordinate the global exploration and local optimization capabilities of the algorithm, and enhance the convergence accuracy of the algorithm. Thirdly, since Tasmanian devil is easy to fall into local optimum in the process of hunting prey, a nonlinear decline strategy based on oscillation factor is presented, which increases the search range of the algorithm and improves the ability of the algorithm to jump out of the local optimal value.Finally, 12 evaluation functions, cec2019 and cec2021 test functions commonly used in NTDO and TDO, WOA, DBO, PSO, GWO, DFPSO and PDGWO algorithms are compared and analyzed, and the experimental results show the effectiveness and feasibility of the scheme.