作　　者： ; ; ; ;

机构地区： 广州大学华软软件学院游戏系

出　　处： 《小型微型计算机系统》 2014年第1期185-188,共4页

摘　　要： 约束优化问题是工程应用领域经常出现的一类数学规划问题,近年来,约束优化问题求解已成为进化计算研究的一个重要方向.而一系列新型的进化计算方法 ,如人工神经网络、模拟退火、遗传算法、进化规划、进化策略、粒子群、人工鱼群等获得了极其迅速的发展和广泛的应用.本文提出了一种基于人工萤火虫群优化算法求解约束优化问题的新方法.该方法在求解约束优化问题的过程中,不断地寻找更优可行解,逐渐达到搜索全局最优解.通过标准测试函数和工程实例仿真表明,该方法能很好的求解约束优化问题,精度高、适应性强,在工程实际中有较大的应用价值. Constrained optimization problems are mathematical programming problems frequently encountered in the discipline of engineering application. Solving constrained optimization problems has become an important research area of evolutionary computation in recent years. And a series of new type of evolutionary computation methods, such as artificial neural network, simulated annealing, genetic algorithm, evoluntionary programming, evolution strategy, particle swarm optimization and artificial fish swarm algorithrn has been extremely rapid development and extensive application. In this paper, a new method for solving constrained optimization problems based on artificial glowworm swarm optimization （GSO） algorithm is presented. The GSO algorithm can keep looking for more optimal feasible solution, and gradually to search the global optimal solution. This algorithm was validated by the simulation examples of numerical integration. Simulation results based on well-known constrained engineering design problems demonstrate the effective- ness.

关 键 词： 人工萤火虫群优化算法 约束优化问题 群智能优化算法

领　　域： [自动化与计算机技术] [自动化与计算机技术]