Introduction to Shuffled Frog Leaping Algorithm and Its Sensitivity to the Parameters of the Algorithm

Abstract

The optimization techniques is an essential tool in many fields of science and engineering. The evolution of humans and several other species teaches us many techniques which can be replicated to solve the nonlinear nonconvex optimization problems in an efficient and faster way. One such evolutionary trait from frogs can be thought of as an efficient optimization algorithm. This algorithm is based on the food search by an army of frogs. An army of frogs in a swamp search for food on the rocks floating around by leaping onto the nearest possible rock and also communicating with the rest of the frogs for improvising the search process. Each individual frog tries to get maximum food as fast as possible and thus developing a strategy to time their leaps in the best possible way. The algorithm developed replicating this process is called the Shuffled Frog Leaping Algorithm. This chapter discusses the basic principle of Shuffled Frog Leaping Algorithm and its efficiency using common benchmark optimization functions.