Multi-objective ant colony optimization for the twin-screw configuration problem

Abstract

The Twin-Screw Configuration Problem (TSCP) consists in identifying the best location of a set of available screw elements along a screw shaft. Due to its combinatorial nature, it can be seen as a sequencing problem. In addition, different conflicting objectives may have to be considered when defining a screw configuration and, thus, it is usually tackled as a multi-objective optimization problem. In this research, a multi-objective ant colony optimization (MOACO) algorithm was adapted to deal with the TSCP. The influence of different parameters of the MOACO algorithm was studied and its performance was compared with that of a previously proposed multi-objective evolutionary algorithm and a two-phase local search algorithm. The experimental results showed that MOACO algorithms have a significant potential for solving the TSCP.

Pharmaceutical amorphous solid dispersion: A review of manufacturing strategies

“…Depending on the desired shear level and operating speed, two screws can be designed in different configurations 52 , 291 of co-rotating (rotating in the same direction) or counter-rotating (rotating in the opposite direction), depending on the desired mixing intensity. Co-rotating screws are usually used in pharmaceutical manufacturing because they generate relatively lower shear forces than the counter-rotating 266 .…”

Manufacturing strategies to develop amorphous solid dispersions: An overview

“…Using a specific screw configuration, the group demonstrated that crystallinity of sildenafil citrate could be preserved with successfully processed formulations. In addition, Teixeira et al [45] investigated algorithmic tools that are effective and employed by the pharmaceutical industry. They also determined appropriate screw configurations for specific purposes.…”

Ant Colony Optimization: A Component-Wise Overview

“…When the evaluation of candidate solutions is very costly in terms of computation time, usually only a small number of candidates can actually be evaluated. Costly solution evaluation arises in the case of simulation-based optimization [6] and, frequently, in industrial settings [44,129]. Applications with very small evaluation budgets have been rarely studied in the ACO literature, and preliminary results suggest that typical ACO parameter settings are not appropriate in such context [109].…”