Chen Ying-Ping, Peng Wen-Chih, Jian Ming-Chung
Department of Computer Science, National Chiao Tung University, Hsinchu 300, Taiwan, ROC.
IEEE Trans Syst Man Cybern B Cybern. 2007 Dec;37(6):1460-70. doi: 10.1109/tsmcb.2007.904019.
In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkage identification technique called dynamic linkage discovery to address the linkage problem in real-parameter optimization problems. Dynamic linkage discovery is a costless and effective linkage recognition technique that adapts the linkage configuration by employing only the selection operator without extra judging criteria irrelevant to the objective function. Moreover, a recombination operator that utilizes the discovered linkage configuration to promote the cooperation of particle swarm optimizer and dynamic linkage discovery is accordingly developed. By integrating the particle swarm optimizer, dynamic linkage discovery, and recombination operator, we propose a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL). In order to study the capability of PSO-RDL, numerical experiments were conducted on a set of benchmark functions as well as on an important real-world application. The benchmark functions used in this paper were proposed in the 2005 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. The experimental results on the benchmark functions indicate that PSO-RDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSO-RDL was also used to solve the economic dispatch (ED) problem for power systems, which is a real-world problem and highly constrained. The results indicate that PSO-RDL can successfully solve the ED problem for the three-unit power system and obtain the currently known best solution for the 40-unit system.
在本文中，我们尝试通过引入连锁概念来提高粒子群优化器的性能，连锁概念是遗传算法中的一种基本机制，并设计了一种名为动态连锁发现的新连锁识别技术，以解决实参数优化问题中的连锁问题。动态连锁发现是一种无成本且有效的连锁识别技术，它仅通过选择算子来调整连锁配置，而无需与目标函数无关的额外判断标准。此外，相应地开发了一种重组算子，该算子利用发现的连锁配置来促进粒子群优化器与动态连锁发现的协同作用。通过整合粒子群优化器、动态连锁发现和重组算子，我们提出了一种新的优化方法混合体，称为带重组和动态连锁发现的粒子群优化（PSO-RDL）。为了研究PSO-RDL的性能，我们在一组基准函数以及一个重要的实际应用上进行了数值实验。本文中使用的基准函数是在2005年电气和电子工程师协会进化计算大会上提出的。在基准函数上的实验结果表明，PSO-RDL能够提供与其他先进优化技术相当的性能水平。除了基准测试外，PSO-RDL还被用于解决电力系统的经济调度（ED）问题，这是一个实际问题且约束条件很强。结果表明，PSO-RDL能够成功解决三机组电力系统的ED问题，并获得40机组系统目前已知的最佳解决方案。
