Nature-Inspired Metaheuristic Techniques as Powerful Optimizers in the Paper Industry

Abstract

Nature-Inspired Metaheuristics (NIM) have emerged as a potent tool for solving complex and difficult optimization problems, arising in various industries, which otherwise become quite difficult (if not impossible) to solve by the classical methods based on gradient search. Further, NIM techniques are more likely to obtain a global optimal solution, often desired and sometimes a necessity in several real life situations. In this study we employ SABC, a variant of Artificial Bee Colony (ABC), a relatively newer NIM algorithm, for solving four typical processes of a paper mill where optimization can be applied. We have also considered two chemical process problems that can be related to paper industry. Numerical results show that the proposed SABC scheme is efficient in dealing with these problems.