MOCOVIDOA：一种新的多目标冠状病毒疾病优化算法，用于解决多目标优化问题。

IF 4.5 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Computing & Applications Pub Date : 2023-05-02 DOI:10.1007/s00521-023-08587-w

Asmaa M Khalid, Hanaa M Hamza, Seyedali Mirjalili, Khaid M Hosny

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引用次数: 1

摘要

提出了一种新的多目标冠状病毒疾病优化算法（MOCOVIDOA），用于解决多达三个目标函数的全局优化问题。该算法在优化过程中使用了一个档案来存储非支配POS。然后，轮盘选择机制通过模拟冠状病毒颗粒用于复制的移帧技术来选择有效的存档解决方案。我们通过解决27个多目标（21个基准和6个真实世界的工程设计）问题来评估效率，其中将结果与五种常见的多目标元启发式方法进行比较。比较使用了六个评估指标，包括IGD、GD、MS、SP、HV和Δp。所获得的结果和Wilcoxon秩和检验表明了该新算法相对于现有算法的优越性，并揭示了其在解决多目标问题中的适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

MOCOVIDOA: a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems.

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MOCOVIDOA: a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems.

A novel multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) is presented to solve global optimization problems with up to three objective functions. This algorithm used an archive to store non-dominated POSs during the optimization process. Then, a roulette wheel selection mechanism selects the effective archived solutions by simulating the frameshifting technique Coronavirus particles use for replication. We evaluated the efficiency by solving twenty-seven multi-objective (21 benchmarks & 6 real-world engineering design) problems, where the results are compared against five common multi-objective metaheuristics. The comparison uses six evaluation metrics, including IGD, GD, MS, SP, HV, and delta p ( $Δ P$ ). The obtained results and the Wilcoxon rank-sum test show the superiority of this novel algorithm over the existing algorithms and reveal its applicability in solving multi-objective problems.

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来源期刊

Neural Computing & Applications 工程技术-计算机：人工智能

CiteScore

11.40

自引率

8.30%

发文量

1280

审稿时长

6.9 months

期刊介绍： Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.