一种基于标杆管理的优化算法∗

摘要

借鉴标杆管理理念,提出了一种基于标杆管理的优化算法(benchmarking-based optimization algorithm,简称 BOA)。根据标杆管理的核心价值观,设计了一套基于动态小生境的竞争性学习机制,针对常用的编码方案,设计出了具体可行的执行方法。种群内个体执行方向明确的主动学习式搜索,通过对标杆的模仿学习,能够快速搜索到解空间内的目标区域内,具有较好的智能性。此外,整个小生境种群系统通过自组织学习实现与环境的友好交互,较好地解决了保持种群的多样性的难题。分析了 BOA 算法与遗传算法等现代智能优化方法在搜索模式上的重要区别,并通过对比仿真实验,表明算法能够与环境进行稳定而友好的交互,表现出较强的鲁棒性,其搜索速度和寻优能力在实验中均有较好的表现。%Drawing on the benchmarking theory in the business management, a new search method, benchmarking-based optimization algorithm (BOA), is proposed in this paper. BOA provides a competitive learning mechanism based on dynamic niche according to the core values of benchmarking. Through imitation and learning, all the individuals within a population are able to approach to the high yielding regions in the solution space and seek out the optimal solutions quickly. Further, the formidable problem of maintaining the diversity of population is effectively resolved through the self-organizing learning process of the niche system and its friendly interaction with the environment. In this paper, the main differences between BLA and the existing intelligent optimization methods, sush as genetic algorithm (GA), are analyzed. The comparative experiments show that BLA is robust and able to perform friendly interactive learning with the environment, and its search speed and optimization ability is far superior to the existing intelligent optimization methods.