基于差分进化的水波优化算法

Water Wave Optimization Algorithm Based on Differential Evolution

水波优化(WWO)算法是一种基于浅水波理论的新兴智能优化算法.为提高算法的收敛速度和精度,提出一种基于差分进化的水波优化(DEWWO)算法,在传播、折射、碎浪过程之后进行变异、选择、交叉操作,通过差分进化算法的过程有效地增加种群多样性,一定程度上避免算法陷入局部最优,提高算法搜索的效率和精度.对6个常用的基准函数进行测试,结果表明改进算法相对于基本水波优化算法在函数优化方面具备一定的优势,是对基本水波优化算法的有力补充.

Water wave optimization(WWO)algorithm is a novel evolutionary algorithm inspired based on the shallow wave theory.In order to improve the convergence speed and accuracy of the algorithm,the water wave optimization algorithm based on differential evolution(DEWWO)algorithm was represented.The improved algorithm perform mutation,selection,crossover after propagation,wave and refraction process,and effectively increase the diversity of the population through the process.To a certain extent,the algorithm falling into local optimum was avoided,and the efficiency and accuracy of the search algorithm were improved.Six commonly used benchmark functions were tested,and the results indicate that the improved algorithm has certain advantages with respect to the basic wave optimization algorithm in terms of function optimization,and the improved algorithm is a powerful supplement to the basic water wave optimization.