Performance Optimization of the Paper Mill using Opposition based Shuffled frog-leaping algorithm

Abstract

Shuffled frog-leaping algorithm (SFLA) is recently introduced memetic algorithm inspired by foraging behavior of frogs. SFLA partially follows particle swarm optimization in local search process and shuffled complex evolution algorithm in performing global search. The key concept about such algorithms is to gain an edge over traditional or deterministic mathematical techniques to achieve comparatively better solutions to the multimodal or multifaceted optimization problems. SFLA embeds the features of both particle swarm optimization (PSO) and shuffled complex evolution (SCE) algorithm. In this study SFLA named as O-SFLA is proposed. In general structure of SFLA, the frogs are divided into memeplexes based on their fitness values where they forage for food. In this study the opposition based learning concept is embedded into the memeplexes before the frog initiates foraging. The proposal is validated on performance optimization of the Paper Mill.