基于混合智能优化算法的复杂软件可靠性分配

软件可靠性是系统设计、研究和运行过程中必须考虑的关键因素之一.与目前大多数软件可靠性分配的研究主要局限于简单的串并联模型不同，本文将最优化算法应用于大型复杂软件系统的可靠性分配.针对分布估计算法收敛速度快，全局搜索能力强，但是易于陷入局部最优；而差分进化算法局部搜索能力强，但搜索速度略慢的问题，本文提出一种元启发式算法——基于罚函数的混合分布估计和自适应交叉差分进化的优化算法（PHEDA-SCDE），该算法收敛速度快，全局搜索能力强且不易陷入局部最优.基于四种特定的体系结构风格——顺序、并发、循环、容错，对复杂软件可靠性进行评估.为不失算法通用性，本文采用三个仿真算例进行实验，分别为单输入单输出系统，单输入多输出系统和多输入多输出系统.实验结果表明，PHEDA-SCDE算法在软件可靠性分配方面与同类算法相比，具有明显的可行性和有效性.

Complex Software Reliability Allocation Based on Hybrid Intelligent Optimization Algorithm

Software reliability problem is one of the key factors in the process of system design, research and running. Different from most current researches on software reliability allocation limited to series parallel models, an effective optimization algorithm is applied to large complex software reliability allocation in this paper. Estimation of distribution algorithm (EDA) has fast convergence rate and strong global search capability, but is easily trapped in local optimization. Differential evolution (DE) has good local search capability with slower convergence speed. To address the issue, a new penalty guided hybrid estimation of distribution and self-adaptive crossover differential evolution algorithm (PHEDA-SCDE) is proposed in this paper. PHEDA-SCDE has fast convergence rate and strong global search capability. Also, it is not easily trapped in local optimization. In addition, software reliability is estimated based on four specific architecture styles-sequential, parallel, circulation and fault tolerant. To demonstrate the generality of the algorithm, experiments are carried out on three numerical examples including single-input/single-output system, single-input/multiple-output system and multiple-input/multiple-output system. The experimental results show that the PHEDA-SCDE is significantly feasible and efficient in reliability allocation compared with similar algorithms.