Neighborhood Search Based Improved Bat Algorithm for Web Service Composition
Fadl Dahana b c d✉
- aDepartment of Computer Sciences , Faculty of Computing and Information Technology Alturbah , Taiz University , Taiz , 9674 , Yemen
- bDepartment of Information System , College of Computer Engineering and Sciences , Prince Sattam bin Abdulaziz University , Al-Kharj , 11942 , Saudi Arabia
- cDepartment of Computer Sciences, Faculty of Computing and Information Technology Alturbah, Taiz University, Taiz, 9674, Yemen
- dDepartment of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
- ✉Corresponding author
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Abstract
Web services are provided as reusable software components in the services-oriented architecture. More complicated composite services can be combined from these components to satisfy the user requirements represented as a workflow with specified Quality of Service (QoS) limitations. The workflow consists of tasks where many services can be considered for each task. Searching for optimal services combination and optimizing the overall QoS limitations is a Non-deterministic Polynomial (NP)-hard problem. This work focuses on the Web Service Composition (WSC) problem and proposes a new service composition algorithm based on the micro-bats behavior while hunting the prey. The proposed algorithm determines the optimal combination of the web services to satisfy the complex user needs. It also addresses the Bat Algorithm (BA) shortcomings, such as the tradeoff among exploration and exploitation searching mechanisms, local optima, and convergence rate. The proposed enhancement includes a developed cooperative and adaptive population initialization mechanism. An elitist mechanism is utilized to address the BA convergence rate. The tradeoff between exploration and exploitation is handled through a neighborhood search mechanism. Several benchmark datasets are selected to evaluate the proposed bat algorithm’s performance. The simulation results are estimated using the average fitness value, the standard deviation of the fitness value, and an average of the execution time and compared with four bat-inspired algorithms. It is observed from the simulation results that introduced enhancement obtains significant results.