A Hybrid Strategy Improved Whale Optimization Algorithm for Web Service Composition

Ju, Chuanxiang; Ding, Hangqi; Hu, Benjia

doi:10.1093/comjnl/bxab187

Cited by 16 publications

(6 citation statements)

References 43 publications

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“…Ibrahim et al employed a hybrid approach SFGA combining the frog leaping algorithm and genetic programming on top of an energy-aware mechanism to optimize mobile cloud service composition [28]. Ju et al addressed the issue of slow or premature convergence to some extent by integrating the mutation process from genetic programming into the whale algorithm [29].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition

Wang,

Du,

Chen

2024

Applied Sciences

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Service-oriented architectures have become increasingly prevalent in internet application design, leading to a proliferation of services. Managing and composing these web services pose a classic non-deterministic polynomial-time hard problem. The web service composition problem (WSCP) based on the input–output model (IOM) involves the automatic composition of services without relying on a pre-defined workflow. Multi-objective evolutionary algorithms offer a solution that goes beyond simple weighted average calculations for quality of service, catering to diverse user needs in service composition. This paper introduces a multi-objective heuristic algorithm based on the SPEA2 called MSPEA2+. Specific modifications are incorporated in MSPEA2+ to support the breeding, crossover, and mutation processes, tailored for IOM-based WSCP. Validating the algorithm performance through finding suitable combination results for each task in the WSC-08 dataset, our experiments showed that MSPEA2+ has better iterative efficiency than existing multi-objective methods and outperforms them in terms of quality of service. MSPEA2+ achieved the lowest IGD value across all seven tasks, an improvement of 81.7% IGD value compared with the SPEA2 algorithm in WSC-0804.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition

Wang,

Du,

Chen

2024

Applied Sciences

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“…To our knowledge, few researchers used the WOA to address the WSC problem. Ju et al [5] introduced hybrid methods that enhanced the performance of the WOA to overcome its slow convergence using chaos initialization, mutation, and nonlinear convergence factor methods. Jin et al [6] proposed an enhancement for the WOA using two different approaches: the uniform mutation for the Eagle Strategy and a modified WOA.…”

Section: Related Workmentioning

confidence: 99%

An Improved Whale Optimization Algorithm for Web Service Composition

Dahan

2022

Axioms

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In the current circumstance, the Web Service Composition (WSC) was introduced to address complex user needs concerning the Quality of Services (QoS). In the WSC problem, the user needs are divided into a set of tasks. The corresponding web services are retrieved from the web services discovery according to the functionality of each task, and have different non-functional constraints, such as QoS. The WSC problem is a multi-objective optimization problem and is classified as an NP-hard problem. The whale optimization algorithm (WOA) is proven to solve complex multi-objective optimization problems, and it has the advantage of easy implementation with few control parameters. In this work, we contribute to improving the WOA algorithm, where different strategies are introduced to enhance its performance and address its shortcomings, namely its slow convergence speed, which produces low solution accuracy for the WSC problem. The proposed algorithm is named Improved Whale Optimization Algorithm (IWOA) and has three different strategies to enhance the performance of the WOA. Firstly, the Sine chaos theory is proposed to initiate the WOA’s population and enhance the initialization diversity. Secondly, a Lévy flight mechanism is proposed to enhance the exploitation and exploration of WOA by maintaining the whales’ diversity. Further, a neighborhood search mechanism is introduced to address the trade-off between exploration and exploitation searching mechanisms. Different experiments are conducted with datasets on 12 different scales (small, medium, and large), and the proposed algorithm is compared with standard WOA and five state-of-the-art swarm-based algorithms on 30 different independent runs. Furthermore, four evaluation criteria are used to validate the comparison: the average fitness value, best fitness values, standard deviation, and average execution time. The results show that the IWOA enhanced the WOA algorithm’s performance, where it got the better average and best fitness values with a low variation on all datasets. However, it ranked second regarding average execution time after the WOA, and sometimes third after the WOA and OABC, which is reasonable because of the proposed strategies.

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“…The number of existing services is increasing rapidly. Many services have similar functions but different service quality attributes, or there are conflicts between services [ 10 ]. Therefore, it is a dilemma for users to select suitable web services for each subtask to make the whole web service system run optimally.…”

Section: Introductionmentioning

confidence: 99%

MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer

Liu,

et al. 2024

Sensors

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The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple multi-objective test functions. Experimental results demonstrate that MBB-MOGWO exhibits rapid convergence and a reduced likelihood of being trapped in local optima. Furthermore, in the context of the Internet of Things (IoT), the quality of web service composition significantly impacts complexities related to sensor resource scheduling. To showcase the optimization capabilities of MBB-MOGWO in real-world scenarios, the algorithm is applied to address a Multi-Objective Problem (MOP) within the domain of web service composition, utilizing real data records from the QWS dataset. Comparative analyses with four representative algorithms reveal distinct advantages of our MBB-MOGWO-based method, particularly in terms of solution precision for web service composition. The solutions obtained through our method demonstrate higher fitness and improved service quality.

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A Hybrid Strategy Improved Whale Optimization Algorithm for Web Service Composition

Cited by 16 publications

References 43 publications

A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition

A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition

An Improved Whale Optimization Algorithm for Web Service Composition

MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer

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