Centroid Mutation Embedded Shuffled Frog-Leaping Algorithm

Stochastic search algorithms that take their inspiration from nature are gaining a great attention of many researchers to solve high dimension and non – linear complex optimization problems for which traditional methods fails. Shuffled frog – leaping algorithm (SFLA) is recent addition to the family of stochastic search algorithms that take its inspiration from the foraging process of frogs. SFLA has proved its efficacy in solving discrete as well as continuous optimization problems. The present study introduces a modified version of SFLA that uses geometric centroid mutation to enhance the convergence rate. The variant is named as Centroid Mutated – SFLA (CM-SFLA). The proposal is implemented on five benchmark and car side impact problem. Simulated results illustrate the efficacy of the proposal in terms of convergence speed and mean value.