Dahan Fadl
Department of Management Information Systems, College of Business Administration - Hawtat Bani Tamim, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.
Taiz University, Taiz, 9674, Yemen.
Sci Rep. 2024 Sep 30;14(1):22622. doi: 10.1038/s41598-024-73414-8.
With the proliferation of services and the vast amount of data produced by the Internet, numerous services with comparable functionalities but varying Quality of Service (QoS) attributes are potential candidates for meeting user needs. Consequently, the selection of the most suitable services has become increasingly challenging. To address this issue, a synthesis of multiple services is conducted through a composition process to create more sophisticated services. In recent years, there has been a growing interest in QoS uncertainty, given its potential impact on determining an optimal composite service, where each service is characterized by multiple QoS properties (e.g., response time and cost) that are frequently subject to change primarily due to environmental factors. Here, we introduce a novel approach that depends on the Multi-Agent Whale Optimization Algorithm (MA-WOA) for web service composition problem. Our proposed algorithm utilizes a multi-agent system for the representation and control of potential services, utilizing MA-WOA to identify the optimal composition that meets the user's requirements. It accounts for multiple quality factors and employs a weighted aggregation function to combine them into a cohesive fitness function. The efficiency of the suggested method is evaluated using a real and artificial web service composition dataset (comprising a total of 52,000 web services), with results indicating its superiority over other state-of-the-art methods in terms of composition quality and computational effectiveness. Therefore, the proposed strategy presents a feasible and effective solution to the web service composition challenge, representing a significant advancement in the field of service-oriented computing.
随着服务的激增以及互联网产生的海量数据，众多功能可比但服务质量（QoS）属性各异的服务成为满足用户需求的潜在候选者。因此，选择最合适的服务变得越来越具有挑战性。为解决此问题，通过组合过程对多个服务进行综合，以创建更复杂的服务。近年来，鉴于QoS不确定性对确定最优组合服务的潜在影响，人们对其的关注日益增加，其中每个服务都具有多个QoS属性（例如响应时间和成本），这些属性经常主要由于环境因素而发生变化。在此，我们引入一种新颖的方法，该方法依赖于多智能体鲸鱼优化算法（MA-WOA）来解决网络服务组合问题。我们提出的算法利用多智能体系统来表示和控制潜在服务，利用MA-WOA识别满足用户需求的最优组合。它考虑了多个质量因素，并采用加权聚合函数将它们组合成一个连贯的适应度函数。使用真实和人工网络服务组合数据集（总共包含52,000个网络服务）对所提方法的效率进行评估，结果表明其在组合质量和计算效率方面优于其他现有方法。因此，所提出的策略为网络服务组合挑战提供了一种可行且有效的解决方案，代表了面向服务计算领域的重大进步。
