Modified shuffled frog leaping algorithm by adaptive step size: Applications to constraint engineering design problems

Abstract

Shuffled frog leaping algorithm (SFLA) is an ongoing expansion to the group of evolutionary algorithm that imitates the societal and natural conduct of species. Upsides of particle swarm optimization (PSO) and shuffled complex evolution (SCE) is consolidates in SFLA i.e. local searching and information shuffling respectively. In this paper SFLA is improved to solve equality and inequality based constraint engineering design problems using penalty function. In proposed approach linear decreasing function that is adaptive in nature will be utilized to improve worst frog position for better exploration and convergence speed. The simulation results designate the superiority of present study over SFLA in term of global optimum solution and fast convergence rate.