Comparing Novel Multi-Objective Optimization Methodologies: A Case Study in Botanical Extraction Processes

Momordica charantia (M. charantia), Polyscias fruticosa (P. fruticosa), and Perilla frutescens (P. frutescens) are three plant species widely utilized as traditional medicinal remedies in Vietnam. Extracting these plants to obtain beneficial phytochemicals is of significant importance. However, the yields of these target compounds vary considerably and, at times, show contrasting trends depending on the specific extraction alternative. Consequently, selecting the optimal extraction configuration requires a simultaneous evaluation of all compound concentrations, a task recognized as a multi-criteria decision-making (MCDM) problem. This study performs a comparative evaluation of three novel MCDM methodologies, namely the Integrative Reference Point Approach (IRPA), the Root Assessment Method (RAM), and the Deviation-Based Pairwise Assessment Ratio Technique (DEPART), in the context of extracting these three botanical species. The comparison of these approaches is based on two dimensions: first, their consistency in identifying the same optimal extraction alternative; and second, the stability of the alternative rankings when the significance (or weight) of the active compounds is altered. Regarding the first dimension, all three frameworks proved appropriate for application. Concerning the second dimension, the mean Spearman’s rank correlation coefficient was employed to benchmark the methods under varying criteria weights. For the Momordica charantia extraction process, this coefficient reached 0.950, 0.989, and 0.983 for the IRPA, RAM, and DEPART techniques, respectively. In the case of Polyscias fruticosa extraction, the values were 0.983 for IRPA and 1.000 for both RAM and DEPART. For the Perilla frutescens extraction process, the metrics were 0.935, 0.982, and 0.971 for IRPA, RAM, and DEPART, respectively. Thus, from this perspective, the RAM approach demonstrated superior performance over the other two competitors. Establishing the priority order for employing the IRPA, RAM, and DEPART methods constitutes the primary contribution of this research. Furthermore, for each investigated plant species, this study successfully identifies the precise extraction parameter values required to achieve the optimal solution.