Abstract: In 2016, a novel naturally simulated optimization algorithm, termed the sine cosine algorithm (SCA), was proposed by Seyedali Mirjalili from Australia. This algorithm uses the sine cosine mathematical model to solve optimization problems and has attracted extensive attention from numerous scholars and researchers at home and abroad over the last few years. However, similar to other swarm intelligence optimization algorithms, SCA has numerous shortcomings in optimizing some complex function problems. To address the defects of basic SCA, such as low optimization precision, easy dropping into the local extremum, and slow convergence rate, a sine cosine algorithm based on differential evolution (SCADE) was proposed. First, the search capabilities of the new algorithm was improved by adjusting parameter r₁ in a nonlinear manner and ensuring that each individual adopts the same parameters r₁, r₂, r₃, and r₄. Then, differential evolution strategies, including crossover, variation, and selection, were adopted to fully utilize the leading role of the globally optimal individual and information of other individuals in the population. This approach balanced the global exploration and local development abilities and accelerated the convergence rate of the algorithm. Next, using the reconnaissance bees’ strategy, random initialization was performed on individuals whose fitness values showed no improvement in continuous nlim times, which increased the population diversity and improved the global exploration ability of the algorithm. Moreover, the globally optimal individual variation strategy was used to conduct a fine search near the optimal solution, which enhanced the local development ability and optimization accuracy of the algorithm. Based on the above optimization strategies, the algorithm exhibits improvements and its excellent performance is validated by the result analysis of a simulation experiment.