并行智能优化算法研究进展

Survey on parallel intelligent optimization algorithms

中文关键词  大规模优化  智能优化算法  并行计算  并行优化算法

英文关键词  large-scale optimization  intelligent optimization algorithms  parallel computing  parallel optimization algorithms

中文摘要

      基于种群迭代搜索的智能优化算法在农业、交通、工业等很多领域都取得了广泛的应用. 但是该类算法迭 代寻优的特点使其求解效率通常较低, 很难应用到大规模、高维或实时性要求较高的复杂优化问题中. 随并行分布 式技术的发展, 国内外很多学者开始着手研究智能优化算法的并行化. 本文首要介绍了并行智能优化算法的基本概 念; 其次从协同机制、并行模型以及硬件结构3个维度综述了几类常见的并行智能优化算法, 详细分析阐述了它们 优点及不足; 最后对并行智能优化算法的未来研究进行了展望.

英文摘要

      Population based intelligent optimization algorithms have been widely used in a variety of fields such as agriculture, transportation and industry. However, their iterative search based behavior makes them inefficient in addressing large-scale, high-dimensional and complex optimization problems, especially with high real-time requirements. With the development of parallel and distributed technology, many scholars in lots of countries began to study the parallel of intelligent optimization algorithm. In this survey, we first introduce the basic concepts of parallel intelligent optimization algorithms. Second, several types of common parallel intelligent optimization algorithms are summarized from the perspectives of coordination mechanism, parallel models and hardware structure. Also, their advantages and disadvantages are discussed in detail. Finally, some future research on the parallelization of intelligent optimization algorithms is prospected.