The human-on-the-loop evaluation of multiobjective optimization algorithms for solving a real-world problem that integrates the food-energy-water nexus security and climate change vulnerability

Human-on-the-loop (HOTL) evaluation enables decision-makers to provide qualitative insights that support standard performance metrics. While these metrics are designed to assess convergence and diversity of solutions using statistical measures, they often fail to grasp problem-specific characteristi...

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Vydáno v:	Results in control and optimization Ročník 20; s. 100606
Hlavní autoři:	Okola, Isaac, Ochieng, Daniel Orwa, Miriti, Evans Kirimi, Ong'isa, Gilbert Ouma
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.09.2025 Elsevier
Témata:	Climate change vulnerability Food-energy-water nexus Human-on-the-Loop evaluation Inter-rater reliability Multiobjective optimization algorithms Performance metric evaluation Performance metric evaluation Inter-rater reliability Food-energy-water nexus Human-on-the-Loop evaluation Climate change vulnerability Multiobjective optimization algorithms
ISSN:	2666-7207, 2666-7207
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Human-on-the-loop (HOTL) evaluation enables decision-makers to provide qualitative insights that support standard performance metrics. While these metrics are designed to assess convergence and diversity of solutions using statistical measures, they often fail to grasp problem-specific characteristics inherent in noisy and uncertain real-world problems, subsequently generating inaccurate or uninterpretable numerical results. Additionally, the lack of qualitative insights and decision-maker preferences in performance evaluation compromises the effectiveness of identifying the appropriate algorithm to solve a specific real-world multiobjective optimization problem (MOP). This research applies graphical analysis, examination of objectives, and decision variable values to complement statistical analysis. The HOTL concept is explored as an “Interaction after a complete run (IAR)” mechanism for evaluating optimization and performance results through human intervention. It is achieved by implementing the inter-rater reliability (IRR) evaluation facilitated by computing Krippendorff’s alpha scores. Based on the results, the Fuzzy Decision Variable (FDV) algorithm is selected as the most suitable algorithm to solve a multiobjective problem that integrates the Food-Energy-Water Nexus (FEWN) and Climate Change Vulnerability (CCV). The findings highlight the HOTL's importance in addressing the shortcomings of the existing performance metrics, such as ambiguous and inaccurate results. Based on the identified knowledge gaps, the findings, and the limitations of this research, we propose future research areas that can be undertaken to improve the performance evaluation of algorithms. Such research areas include incorporating machine learning to predict, using performance data, the most suitable algorithm to solve a specific problem, and advancing interactive learning and user adjustments of the optimization process.
ISSN:	2666-7207 2666-7207
DOI:	10.1016/j.rico.2025.100606