摘要
Abstract
In order to settle the problem that dragonfly algorithm(DA)is easy to sink into local optimum and poor convergence accuracy,a dragonfly algorithm based on Golden sine strategy and random difference mutation(GMDA)is proposed.Firstly,in the initial stage of the algorithm,the elite reverse learning strategy is adopted to initialize the dragonfly population position and improve the search efficiency of the algorithm.Secondly,the golden sine strategy is used to update the position to fully search the range of high-quality solutions.Global search and local development capabilities are balanced by adaptive inertial weights.Finally,in the later stage of the algorithm,random difference mutation is used to avoid sink into local optimum.The performance of algorithms is verified with eight benchmark functions,an ablation experiment is set up to evaluate the effectiveness of each strategy,and the re-sults show that GMDA improves the optimization accuracy,the ability to jump out of local optimum and the convergence ability.Ab-lation experiments are performed to verify the effectiveness of each strategy.关键词
蜻蜓优化算法/黄金正弦策略/自适应权重/随机差分变异/消融实验Key words
dragonfly optimization algorithm/golden sine strategy/adaptive weight/random difference mutation/ablation experiments分类
计算机与自动化
