Wu Guohua, Pedrycz Witold, Ma Manhao, Qiu Dishan, Li Haifeng, Liu Jin
Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, Hunan 410073, China ; Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB, Canada T6R 2V4.
Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB, Canada T6R 2V4 ; Warsaw School of Information Technology, Newelska, 01-447 Warsaw, Poland ; Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
ScientificWorldJournal. 2014 Jan 23;2014:713490. doi: 10.1155/2014/713490. eCollection 2014.
Although Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL) is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL) strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.
尽管粒子群优化算法（PSO）在解决全局优化问题时展现出了具有竞争力的性能，但在处理高维且景观复杂的优化问题时，它存在一些局限性。在本文中，我们将一些面向问题的知识融入到某种PSO变体的设计中。由此产生的具有内部变量学习策略的新型PSO算法（PSO-IVL）在优化具有对称变量的函数时特别有效。优化函数的对称变量必须满足一定的定量关系。基于这一知识，内部变量学习（IVL）策略帮助粒子检查其内部变量之间的关系，确定所有其他变量的范例变量，然后使每个变量根据它们的定量关系从范例变量学习。此外，我们设计了一种新的陷阱检测和跳出策略，以帮助粒子逃离局部最优。陷阱检测操作在单个粒子层面进行，而陷阱跳出策略本质上是自适应的。针对一些代表性优化函数完成的实验模拟证明了PSO-IVL的优异性能。PSO-IVL的有效性强调了通过面向问题的领域知识增强进化算法的有用性。
