Optimized Web Service Composition Using Evolutionary Computation Techniques

Subbulakshmi, S.; Ramar, K.; Saji, Anvy Elsa; Chandran, Geethu

doi:10.1007/978-981-15-9509-7_38

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…Subbulakshmi et al [22] optimized the service quality using a genetic algorithm, and cuckoo search is used to search for an optimal combination of web services. Ghobaei-Arani et al [1] presented a cuckoo search algorithm while considering a distributed network.…”

Section: Related Workmentioning

confidence: 99%

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Dahan¹,

Alwabel²

2023

Intelligent Automation &Amp; Soft Computing

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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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Section: Related Workmentioning

confidence: 99%

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Dahan¹,

Alwabel²

2023

Intelligent Automation &Amp; Soft Computing

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“…Subbulakshmi et al [30] introduced an enhancement to the CS based on the genetic algorithm. Ghobaei-Arani et al [31] introduced an enhancement to the CS based on the distributed network.…”

Section: Nature-inspired Algorithms Related Workmentioning

confidence: 99%

Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

Ahanger¹,

Dahan²,

Tariq³

2023

Computer Systems Science and Engineering

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In the Internet of Things (IoT), the users have complex needs, and the Web Service Composition (WSC) was introduced to address these needs. The WSC's main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services (QoS) constraints. The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints. In this paper, we introduce an extension of our previous works on the Artificial Bee Colony (ABC) and Bat Algorithm (BA). A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search. The bat agent is used to improve the solution of exhausted bees after a threshold (limits), and also an Elitist Strategy (ES) is added to BA to increase the convergence rate. The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-ofthe-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria (average fitness values, best fitness values, and execution time) that were measured for 30 different runs. These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets. The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors. The Wilcoxon signed-rank significant test is used where the proposed algorithm results significantly differ from other algorithms on 100% of datasets.

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“…The genetic algorithm with CS was proposed by Subbulakshmi et al [28]. The distributed network with CS was proposed by Ghobaei-Arani et al [29].…”

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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Optimized Web Service Composition Using Evolutionary Computation Techniques

Cited by 12 publications

References 13 publications

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

An Improved Whale Optimization Algorithm for Web Service Composition

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