School of Science, Engineering & Technology, RMIT University Vietnam, Ho Chi Minh City, 700000, Vietnam.
School of Industrial Engineering and Management, International University, Vietnam National University, Ho Chi Minh City, 700000, Vietnam.
Sci Rep. 2024 Nov 16;14(1):28260. doi: 10.1038/s41598-024-70462-y.
The development of new algorithms can aid researchers and professionals in resolving problems that were once unsolvable or discovering superior solutions to problems that were already settled. By recognizing the importance of continuous research on creating novel algorithms, this paper introduced a hybrid metaheuristic algorithm-h-PSOGNDO, which is a combination of Particle Swarm Optimization (PSO) and Generalized Normal Distribution Optimization (GNDO). The proposed algorithm utilizes the Particle Swarm Optimization's strategy for exploitation and the Generalized Normal Distribution Optimization's global search strategy for exploration. Through this combination, h-PSOGNDO is believed to be an effective algorithm that can promote the advantages of its parents' algorithms. Different assessment methods are used to assess the proposed novel algorithm. First, the h-PSOGNDO is set to conduct experiments on two sets of mathematical functions, including twenty-eight IEEE CEC2017 and ten IEEE CEC2019 benchmark test functions, respectively. Then, the h-PSOGNDO algorithm is applied to a case study on the prediction of antimicrobial peptides' toxicity to evaluate its performance on real-life problems. The statistical findings collected from both the test function sets and the case study show that the h-PSOGNDO algorithm works effectively, proving its astonishing ability to yield highly competitive outcomes for complex problems.
新算法的发展可以帮助研究人员和专业人士解决曾经无法解决的问题，或者发现已经解决问题的更优解决方案。通过认识到不断研究创造新算法的重要性，本文引入了一种混合元启发式算法 h-PSOGNDO，它是粒子群优化（PSO）和广义正态分布优化（GNDO）的组合。所提出的算法利用了粒子群优化的开发策略和广义正态分布优化的全局搜索策略进行探索。通过这种组合，h-PSOGNDO 被认为是一种有效的算法，可以促进其父母算法的优势。使用不同的评估方法来评估所提出的新型算法。首先，将 h-PSOGNDO 设置为在两组数学函数上进行实验，分别是二十八组 IEEE CEC2017 和十组 IEEE CEC2019 基准测试函数。然后，将 h-PSOGNDO 算法应用于抗菌肽毒性预测的案例研究中，以评估其在实际问题上的性能。从测试函数集和案例研究中收集的统计结果表明，h-PSOGNDO 算法的效果显著，证明了它在处理复杂问题时能够产生极具竞争力的结果的惊人能力。
