模拟植物生长算法的原理及应用

为解决国外主流智能算法普遍存在的参数选择难题,模拟植物生长算法(PGSA)以植物独有的向性运动为启发式准则,将优化问题的解空间当做植物的生长环境,通过生长信息和分枝模式(L-系统)构造出依赖优化环境而不断生长的人工植物分枝生长模式,建立了跳出局部最优解并迅速求出全局最优解的遍历搜索机制和智能算法体系.模拟植物生长算法开拓了"无参数智能优化算法"研究的新领域,本文从该算法的思想源头进行阐述,对国内外学者近年来在不同研究领域应用该算法的情况进行了分析和评述,指出了植物生长原理对智能优化领域的影响以及未来的发展方向.

The theory and application of plant growth simulation algorithm

In order to solve the problem of parameter selection commonly existing in the mainstream intelligent algorithms abroad, plant growth simulation algorithm (PGSA) looks plants unique tropism as the heuristic rules, looks the solution space of optimization problem as plant growth environment. By means of growth information and branching pattern (L-system), PGSA constructs an artificial plant branching growth pattern depending on optimized environment, then establishes a traversal search mechanism and an intelligent algorithm system, which can jump out of the local optimal solution and quickly find the global optimal solution. PGSA has opened up a new field of "parameter-free intelligent optimization algorithm". This paper expounds the idea source of the algorithm, analyzes and comments on the application of the algorithm in different research fields by scholars at home and abroad in recent years, and points out the influence of plant growth principle on intelligent optimization field and its future development direction.