Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Dahan, Fadl; Alwabel, Abdulelah

doi:10.32604/iasc.2023.030651

Cited by 5 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The search for optimal WSs combination to satisfy these needs requires high computation time that increases while the WSC scale increases. In this work, we introduce an extension of our previous works on improving the ABC [5][6][7] and BA [8]. A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search.…”

Section: Figure 1: the Wsc Problem Representative Modelmentioning

confidence: 99%

“…Karthikeyan et al [16] introduced an enactment to ABC based on the genetic algorithm. Dahan et al [7] introduced an enactment to ABC based on the cuckoo search. The ABC with Pareto dominance is used in Zhou et al [17,18].…”

Section: Abc Related Workmentioning

confidence: 99%

“…To ensure a level of unbiased and fair experiments, we implemented all algorithms in Java using Windows 10 with Intel(R) Core(TM) i7-7500U CPU @ 2.70 GHz and 8.0 GB RAM. In addition, four algorithms were used to compare the ABC-BA performance, which are ABC_CS [7], MOHABC [17], SABC [6], and OABC [10]. The parameters setting of these algorithms is set based on their works preferences.…”

Section: Experimental Settingsmentioning

confidence: 99%

See 2 more Smart Citations

Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

Ahanger¹,

Dahan²,

Tariq³

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Figure 1: the Wsc Problem Representative Modelmentioning

confidence: 99%

Section: Abc Related Workmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

See 1 more Smart Citation

Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

Ahanger¹,

Dahan²,

Tariq³

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…The genetic algorithm with ABC was also used in [17]. Another hybrid algorithm was proposed [18] based on the ABC and Cuckoo Search, where the cuckoo agent enhanced the poorer bee.…”

Section: Related Workmentioning

confidence: 99%

“…This strategy introduces a variant of the neighborhood search proposed in our previous works [18,19]. The proposed adaptive search balanced the diversity and concentration of swarm-based algorithms [12].…”

Section: Neighborhood Search Strategymentioning

confidence: 99%

An Improved Whale Optimization Algorithm for Web Service Composition

Dahan

2022

Axioms

Self Cite

View full text Add to dashboard Cite

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.

show abstract