一种新的启发式优化算法:学习搜索算法

New Meta-heuristic Optimization Algorithm:Learning Search Algorithm

基于学习的积极和消极现象,提出一种新的启发式智能优化算法:学习搜索算法(LSA).该算法设计了两种学习模式,一是积极模式,充分发挥当前最优学生的引导作用,使所有学生进行积极地学习,不断提升学识水平;二是消极模式,有效地吸收最差学生所具有的优点,增强学习的全面性.利用这两种模式的结合,有效地均衡学习搜索算法的全局搜索能力和局部搜索能力.为了验证学习搜索算法的有效性,对几个经典基准函数进行了测试,结果表明,算法在整体上优于其他几个有发展潜力的启发式算法,具有更好的优化潜力.

Based on positive and negative phenomena of leaming,a new met,a-heuristic intelligent optimization algorithm is proposed and named learning search algorithm（ LSA ）. Two learning patterns are designed in the proposed algorithm. One is positive pattern, which could give full play to the guiding function of the current excellent student, and all the students could positive learn and promote their knowledge level. The other is negative pattern. Every student absorbs the merit of the worst student to enhance the comprehen- siveness of study. These two patterns are combined to tradeoff the global searching ability and local searching ability of learning search algorithm effectively. In order to verify the effectiveness of LSA algorithm,several classic benchmark functions are carded out to be tested. Results demonstrated that the proposed algorithm is superior to the other some promising algorithms, and it has better optimization potential.