Improved Eagle Strategy Algorithm for Dynamic Web Service Composition in the IoT: A Conceptual Approach

Rajendran, Venushini; Ramasamy, R Kanesaraj; Mohd-Isa, Wan-Noorshahida

doi:10.3390/fi14020056

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Teng et al [31] proposed enhancing the whale optimization algorithm based on the logarithmic convergence factor and aggregation potential energy. The improved eagle algorithm is introduced in [32]. Dogani et al [33] introduced a hybrid particle swarm optimization and genetic algorithm where the genetic algorithm is used to enhance the exploration and exploitation of particle swarm optimization.…”

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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“…The authors in [6] applied the mutation, nonlinear convergence factor, and chaos initialization to improve the whale optimization algorithm performance. The improved eagle algorithm is introduced in [24]. Dogani et al [25] introduced a hybrid particle swarm optimization and genetic algorithm where the genetic algorithm is used to enhance the exploration and exploitation of particle swarm optimization.…”

Section: Related Workmentioning

confidence: 99%

Neighborhood Search Based Improved Bat Algorithm for Web Service Composition

Dahan¹

2023

Computer Systems Science and Engineering

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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.

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“…The multi-agent ACO is introduced by Dahan [27]. The improved eagle algorithm is introduced in [28]. Dogani et al [29] enhanced the PSO based on the genetic algorithm.…”

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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Improved Eagle Strategy Algorithm for Dynamic Web Service Composition in the IoT: A Conceptual Approach

Cited by 9 publications

References 26 publications

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Neighborhood Search Based Improved Bat Algorithm for Web Service Composition

Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

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