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将服务选择问题建模为带QoS约束的非线性最优化问题,并提出了一种参数自适应的改进遗传算法(IPAGA).构造了基于双曲正切函数的非线性参数变换函数,当迭代次数或种群多样性程度增加时,使遗传算法的交叉和变异概率相应地非线性递减,以保证算法的全局收敛性和收敛速度.实验结果表明:算法能够快速搜索出全局近似最优解,具有很高的有效性和可行性.
The problem of service selection is modeled as a nonlinear optimization problem with QoS constraints, and an improved adaptive genetic algorithm (IPAGA) is proposed.A nonlinear parameter transformation function based on hyperbolic tangent function is constructed.When the number of iterations Or the degree of population diversity increases, the crossover and mutation probabilities of genetic algorithms decrease non-linearly in order to ensure the global convergence and convergence speed of the algorithm.Experimental results show that the algorithm can quickly find the global optimal solution, High effectiveness and feasibility.