Unifying The Evaluation Criteria Of Many Objectives Optimization Using Fuzzy Delphi Method

Abstract

Many objective optimizations (MaOO) algorithms that intends to solve problems with many objectives (MaOP) (i.e., the problem with more than three objectives) are widely used in various areas such as industrial manufacturing, transportation, sustainability, and even in the medical sector. Various approaches of MaOO algorithms are available and employed to handle different MaOP cases. In contrast, the performance of the MaOO algorithms assesses based on the balance between the convergence and diversity of the non-dominated solutions measured using different evaluation criteria of the quality performance indicators. Although many evaluation criteria are available, yet most of the evaluation and benchmarking of the MaOO with state-of-art algorithms perform using one or two performance indicators without clear evidence or justification of the efficiency of these indicators over others. Thus, unify a set of most suitable evaluation criteria of the MaOO is needed. This study proposed a distinct unifying model for the MaOO evaluation criteria using the fuzzy Delphi method. The study followed a systematic procedure to analyze 49 evaluation criteria, sub-criteria, and its performance indicators, a penal of 23 domain experts, participated in this study. Lastly, the most suitable criteria outcomes are formulated in the unifying model and evaluate by experts to verify the appropriateness and suitability of the model in assessing the MaOO algorithms fairly and effectively.

A Novelty Decision-Making Based on Hybrid Indexing, Clustering, and Classification Methodologies: An Application to Map the Relevant Experts Against the Rural Problem

“…Fuzzy Delphi provides a robust framework that can handle a lack of precision and clarity, as incomplete or inaccurate information is considered a problem in decision-making [26,27]. The performance of the MOO algorithm is assessed based on the balance between convergence and diversity of non-dominated solutions, which are measured using evaluation criteria different from quality performance indicators.…”

تصميم نظام أمثل باستخدام الفاصل التقريبي لبرمجة دي نوڨو متعددة الأهداف

“…The parameters 𝑏 𝑖 (𝑖 = 1,2, … , 𝑚) represent the given available resources as constants. The efficient solution concept results from the solution of the MOLP model see for more details [27][28][29] .…”

“…In our study, rough interval coefficient (RIC) used to develop the Zeleny approach by applying uncertainty conditions. RIC has a main advantage that makes it applicable when data are not available or vague 26,27 .…”

Optimally Enhancement Rural Development Support Using Hybrid Multy Object Optimization (MOO) and Clustering Methodologies: A Case South Sulawesi - Indonesia

“…In this study, the validation process was carried out by evaluating the criteria used as input values using the Multy Objective Optimization (MOO) Fuzzy Delphi Method (FDM). As explained that Fuzzy Delphi provides a robust framework that can handle a lack of precision and clarity, as incomplete or inaccurate information is considered a problem in decision-making [28]. The flowchart of Fuzzy Delphi is shown in Figure 2.…”