Improved Geyser-Inspired Optimization Algorithm with Adaptive Turbulence and Dynamic Pressure Equilibrium for Data Clustering

IF 5.8 3区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY
Laith Abualigah, Saleh Ali Alomari, Mohammad H. Almomani, Raed Abu Zitar, Hazem Migdady, Kashif Saleem, Aseel Smerat, Vaclav Snasel, Absalom E. Ezugwu
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Abstract

While Metaheuristic optimization techniques are known to work well for clustering and large-scale numerical optimization, algorithms in this category suffer from issues like reinforcement stagnation and poor late-stage refinement. In this paper, we propose the Improved Geyser-Inspired Optimization Algorithm (IGIOA), an enhancement of the Geyser-Inspired Optimization Algorithm (GIOA), which integrates two primary components: the Adaptive Turbulence Operator (ATO) and the Dynamic Pressure Equilibrium Operator (DPEO). ATO allows IGIOA to periodically disrupt stagnation and explore different regions by using turbulence, while DPEO ensures refinement in later iterations by adaptively modulating convergence pressure. We implemented IGIOA on 23 benchmark functions with both unimodal and multimodal contours, in addition to eight problems pertaining to cluster analysis at the UCI. IGIOA, out of all the tested methods, was able to converge most accurately while also achieving a stable convergence rate. The mitigation of premature convergence and low-level exploitation was made possible by the turbulence and pressure-based refinements. The findings from the tests confirm that the adaptation of baseline strategies by IGIOA helps deal with complex data distributions more effectively. However, additional hyperparameters which add complexity are introduced, along with increased computational cost. These include automatic tuning of parameters, ensemble or parallel variations, and hybridization with dedicated local search strategies to extend the reach of IGIOA for general optimization while also specializing it for clustering focused tasks and applications.

Abstract Image

基于自适应湍流和动态压力平衡的间歇泉优化算法的数据聚类
虽然元启发式优化技术在聚类和大规模数值优化方面工作得很好,但这类算法存在强化停滞和后期细化差等问题。在本文中,我们提出了改进的间歇泉启发优化算法(IGIOA),它是对间歇泉启发优化算法(GIOA)的改进,它集成了两个主要组件:自适应湍流算子(ATO)和动态压力平衡算子(DPEO)。ATO允许IGIOA通过使用湍流周期性地破坏停滞并探索不同的区域,而DPEO通过自适应调制收敛压力确保在以后的迭代中进行改进。我们在23个具有单峰和多峰轮廓的基准函数上实现了IGIOA,此外还有8个与UCI聚类分析相关的问题。在所有测试的方法中,IGIOA能够最准确地收敛,同时也实现了稳定的收敛速率。通过湍流和基于压力的改进,可以减少过早收敛和低水平开采。测试结果证实,IGIOA对基线策略的调整有助于更有效地处理复杂的数据分布。然而,引入了额外的超参数,增加了复杂性,同时增加了计算成本。其中包括参数的自动调优、集成或并行变化,以及与专用本地搜索策略的混合,以扩展IGIOA的范围,以进行一般优化,同时还将其专门用于以集群为重点的任务和应用程序。
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来源期刊
Journal of Bionic Engineering
Journal of Bionic Engineering 工程技术-材料科学:生物材料
CiteScore
7.10
自引率
10.00%
发文量
162
审稿时长
10.0 months
期刊介绍: The Journal of Bionic Engineering (JBE) is a peer-reviewed journal that publishes original research papers and reviews that apply the knowledge learned from nature and biological systems to solve concrete engineering problems. The topics that JBE covers include but are not limited to: Mechanisms, kinematical mechanics and control of animal locomotion, development of mobile robots with walking (running and crawling), swimming or flying abilities inspired by animal locomotion. Structures, morphologies, composition and physical properties of natural and biomaterials; fabrication of new materials mimicking the properties and functions of natural and biomaterials. Biomedical materials, artificial organs and tissue engineering for medical applications; rehabilitation equipment and devices. Development of bioinspired computation methods and artificial intelligence for engineering applications.
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