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ARTICLE
An Improved Chaotic Quantum Multi-Objective Harris Hawks Optimization Algorithm for Emergency Centers Site Selection Decision Problem
Yuting Zhu1,*, Wenyu Zhang1,2, Hainan Wang1, Junjie Hou1, Haining Wang1, Meng Wang1
1 China Aerospace Academy of Systems Science and Engineering, Beijing, 100048, China
2 School of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China
* Corresponding Author: Yuting Zhu. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.057441
Received 17 August 2024; Accepted 13 November 2024; Published online 02 December 2024
Abstract
Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urgency of demand at disaster-affected sites. Firstly, urgency cost, economic cost, and transportation distance cost were identified as key objectives. The study applied fuzzy theory integration to construct a triangular fuzzy multi-objective site selection decision model. Next, the defuzzification theory transformed the fuzzy decision model into a precise one. Subsequently, an improved Chaotic Quantum Multi-Objective Harris Hawks Optimization (CQ-MOHHO) algorithm was proposed to solve the model. The CQ-MOHHO algorithm was shown to rapidly produce high-quality Pareto front solutions and identify optimal site selection schemes for emergency resource distribution centers through case studies. This outcome verified the feasibility and efficacy of the site selection decision model and the CQ-MOHHO algorithm. To further assess CQ-MOHHO’s performance, Zitzler-Deb-Thiele (ZDT) test functions, commonly used in multi-objective optimization, were employed. Comparisons with Multi-Objective Harris Hawks Optimization (MOHHO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Grey Wolf Optimizer (MOGWO) using Generational Distance (GD), Hypervolume (HV), and Inverted Generational Distance (IGD) metrics showed that CQ-MOHHO achieved superior global search ability, faster convergence, and higher solution quality. The CQ-MOHHO algorithm efficiently achieved a balance between multiple objectives, providing decision-makers with satisfactory solutions and a valuable reference for researching and applying emergency site selection problems.
Keywords
Site selection; triangular fuzzy theory; chaotic quantum Harris Hawks optimization; multi-objective optimization
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