Shuffled frog leaping (SFL) algorithm is a recently introduced metaheuristic which mimics the foraging process of frogs. SFL performs exploration as well as exploitation. In SFL algorithm the colony of frogs is divided into several memeplexes. In each memeplexes frog perform independent social cooperative local search and in later stages this information is shared among memeplexes. The process of sharing the information is shuffling process. SFL has been successfully applied to solve various real world optimization problems. In the present study SFL algorithm is implemented on a very interesting and challenging issue of optimization of Air pollution emissions using different control technologies. The nature of the problem is mixed integer linear programming problem. To further validate the efficacy of the algorithm shipping problem is also solved. The simulated results demonstrate the effectiveness of SFL algorithm.

中文翻译：

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改组蛙跳算法控制空气污染排放

随机蛙跳（SFL）算法是最近引入的一种元启发式算法，它模仿了青蛙的觅食过程。SFL既进行勘探又进行勘探。在SFL算法中，青蛙的殖民地被分为几个Memeplexes。在每个Memeplexes中，青蛙执行独立的社会合作本地搜索，并在以后的阶段中在Memeplexes中共享此信息。信息共享的过程是改组过程。SFL已成功应用于解决各种现实世界中的优化问题。在本研究中，使用不同的控制技术在优化空气污染排放的一个非常有趣且具有挑战性的问题上实现了SFL算法。问题的性质是混合整数线性规划问题。为了进一步验证算法的有效性，还解决了运输问题。