Open Access

ARTICLE

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Fadl Dahan^1,2,*, Abdulelah Alwabel³

1 Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
2 Department of Computer Sciences, Faculty of Computing and Information Technology Alturbah, Taiz University, Taiz, 9674, Yemen
3 Department of Computer Sciences, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia

* Corresponding Author: Fadl Dahan. Email:

Intelligent Automation & Soft Computing 2023, 35(3), 3385-3402. https://doi.org/10.32604/iasc.2023.030651

Received 30 March 2022; Accepted 30 May 2022; Issue published 17 August 2022

Abstract

In recent years, cloud computing has provided a Software As A Service (SaaS) platform where the software can be reused and applied to fulfill complicated user demands according to specific Quality of Services (QoS) constraints. The user requirements are formulated as a workflow consisting of a set of tasks. However, many services may satisfy the functionality of each task; thus, searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem. This work will introduce a hybrid Artificial Bee Colony (ABC) with a Cuckoo Search (CS) algorithm to untangle service composition problem. The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance. However, the ABC suffers from a slow convergence problem. Therefore, the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum. The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms (ABC and CS) and three state-of-the-art swarm-based composition algorithms. In addition, extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters. The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria (best fitness value, average fitness value, and average execution time) overall datasets in 30 different runs. Furthermore, the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.

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Keywords

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