一种群体智能算法——狮群算法

基于狮群中狮王、母狮及幼狮的自然分工,模拟狮王守护、母狮捕猎、幼狮跟随3种群智能行为,提出群体智能算法——狮群算法.算法中不同种类的狮子位置更新方式不同.遵循自然界生物“适者生存”的竞争法则,狮王守护领土,优先享用食物,母狮合作捕猎,幼狮分为学习捕猎、饥饿进食和成年被驱逐.狮子位置更新方式的多样化保证算法快速收敛,不易陷入局部最优.最后,将算法应用于6个标准测试函数优化问题,并对比粒子群算法、骨干粒子群算法,测试结果表明,文中算法收敛速度较快,精度较高,能较好地获得全局最优解.

A Swarm Intelligence Algorithm-Lion Swarm Optimization

Based on the natural division of labor among lion king, lionesses and cubs in a lion group, a swarm intelligent algorithm, loin swarm optimization(LSO), is proposed. LSO is inspired by intelligent behaviors of three populations including lion guarding, lioness hunting, cubs following. In LSO, policies of location updating are different for three populations. LSO follows the biological competition law of “survival of the fittest” in nature world, i.e., the lion king guards territory and possesses the priority of food, lionesses cooperate in hunting, and lion cubs fall into eating, learning to hunt, and being expelled after entering adulthood. The diversity of lion location updating guarantees that LSO converges fast and is not easily trapped into a local optimal solution. LSO is compared with the particle swarm optimization and the bare bones particle swarm optimization on six optimization test functions. Results show that LSO produces fast convergence and high precision, and it obtains a better global optimal solution.