具有随机分形自适应搜索策略的蚁狮优化算法

Improved ant Lion Algorithm Withstochastic Fractal and Self-adaption Searching Strategy Computer Engineering and Applications

针对蚁狮算法在平衡开发能力与探索能力不足的缺点,提出了具体有随机分形自适应搜索策略的改进算法。在该策略中,蚂蚁利用随机分形搜索方程提高算法的探索能力;蚁狮利用自适应搜索方程在最优位置进行精细搜索,以提高算法的开发能力。对3个单峰、3个多峰标准测试函数进行寻优,仿真结果表明,相比于其他算法,所提出的改进算法很好地平衡了自身的开发能力和探索能力,显著提高了全局优化能力和收敛速率。

For the problems of the unbalanced capability between exploration and exploitation of ant lion optimizer(ALO),an improved ALO algorithm with stochastic fractal and self-adaption searching strategy(SFSALO)is proposed.On the one hand,the stochastic fractal searching function is used for ant so as to improve the capability of exploration.On the other hand,self-adaption searching method is adopted for ant lion at the best position in order to improve the convergence precision and the exploitation ability.The simulation on 3 single-peak and 3 multi-peak benchmark functions shows that the proposed algorithm can fully balances the exploration and exploitation and significantly improves the global optimizationability and the convergence speed.