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【目的】社会蜘蛛群优化算法(SSO)是一种新颖的元启发式优化算法,自从它被提出之后就受到该领域学者的广泛关注,并且也被成功应用到许多领域。但是由于社会蜘蛛群优化算法还处在算法的研究初期,该算法的收敛速度与收敛精度还需要进一步提高。【方法】将差分进化算子引入到社会蜘蛛群优化算法(SSO-DM)中,并将改进的算法应用于函数优化问题中,通过5个标准测试函数来验证基于差分进化算子的社会蜘蛛群优化算法(SSO-DM)的优化性能。【结果】差分进化算子增强了社会蜘蛛群优化算法的收敛速度与收敛精度。【结论】本研究中所提出的算法能够获得精确解,并且它也具有较快的收敛速度和较高的算法稳定性。
【Objective】 The social spider group optimization algorithm (SSO) is a novel meta-heuristic optimization algorithm, which has been widely concerned by scholars in this field since it was proposed and has been successfully applied in many fields. However, due to the social spider group optimization algorithm is still in the early stages of the algorithm, the convergence speed and convergence accuracy of the algorithm needs to be further improved. 【Method】 The differential evolution operator was introduced into the social spider group optimization algorithm (SSO-DM), and the improved algorithm was applied to the function optimization problem. Five standard test functions were used to verify the social spider based on the differential evolution operator Optimization Performance of Swarm Optimization Algorithm (SSO-DM). 【Results】 The differential evolution operator enhances the convergence speed and convergence accuracy of the social spider group optimization algorithm. 【Conclusion】 The algorithm proposed in this study can obtain exact solution, and it also has faster convergence speed and higher stability of the algorithm.