Swarm Intelligence for Multiobjective Optimization of Extraction Process

TL;DR

This paper applies and compares three swarm intelligence algorithms, including a novel Hopfield-enhanced PSO, for multiobjective optimization of bioactive compound extraction from Gardenia fruit, providing insights into their effectiveness and the structure of the objective space.

Contribution

It introduces a new Hopfield-enhanced PSO algorithm and compares its performance with GSA and standard PSO in a multiobjective extraction process optimization.

Findings

Hopfield-enhanced PSO outperformed other algorithms in Pareto front quality.

The study reveals the relationship between objective space structure and frontier dominance.

All algorithms effectively generated Pareto optimal solutions for the extraction process.

Abstract

Multi objective (MO) optimization is an emerging field which is increasingly being implemented in many industries globally. In this work, the MO optimization of the extraction process of bioactive compounds from the Gardenia Jasminoides Ellis fruit was solved. Three swarm-based algorithms have been applied in conjunction with normal-boundary intersection (NBI) method to solve this MO problem. The gravitational search algorithm (GSA) and the particle swarm optimization (PSO) technique were implemented in this work. In addition, a novel Hopfield-enhanced particle swarm optimization was developed and applied to the extraction problem. By measuring the levels of dominance, the optimality of the approximate Pareto frontiers produced by all the algorithms were gauged and compared. Besides, by measuring the levels of convergence of the frontier, some understanding regarding the structure of the objective space in terms of its relation to the level of frontier dominance is uncovered. Detail comparative studies were conducted on all the algorithms employed and developed in this work.