Web Service Selection Using Modified Artificial Bee Colony Algorithm

Chandra, Manik; Niyogi, Rajdeep

doi:10.1109/access.2019.2926155

Cited by 21 publications

(17 citation statements)

References 37 publications

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“…All algorithms are coded and run on MATLAB R2014b software. The meta-heuristic algorithms compared in this paper include: CHHO, HHO [21], ESWOA [35], mABC [36]. The population size of all algorithms is set to 30(P = 30), and the maximum number of fitness function evaluation is set to 15000.…”

Section: Discussionmentioning

confidence: 99%

“…Compared with other meta-heuristic algorithms, it can achieve global optimal solutions more seamlessly. In [36], proposed a modified Artificial Bee Colony(mABC) algorithm by incorporating chaotic-based opposition learning method and differential evolution strategies into the artificial bee colony. Compared with other meta-heuristic algorithms, mABC can find the feasible solution of QWSC faster and has a strong scalability.…”

Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

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A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

Chen

2020

IEEE Access

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Recently, with the rapid increase in the number of web services, QoS-aware Web Service Composition(QWSC) has become a popular topic in both industry and academia. Meta-heuristic algorithm, as an effective way to solve classical optimization problems, has been successfully applied to QWSC nowdays. However, such approach has intrinsic drawbacks and usually lack of good performance in large-scale scenarios. For example, some meta-heuristic algorithms are suitable for continuous search space, while the search space of QWSC is discrete. For solving those problems which were commonly faced when applying meta-heuristic algorithm on QWSC, in this research, we firstly introduce a preprocessing approach for constructing fuzzy continuous neighborhood relations of concrete services, which makes the local search strategy of meta-heuristic algorithms be as effective in discrete space as in continuous space, thus improving the optimization performance. Second, we combine Harris Hawks Optimization (HHO) algorithm and logical chaotic sequence to propose an improved meta-heuristic algorithm named CHHO for solving QWSC. The ergodic and chaotic characteristics of chaotic sequences are used to enhance the ability of the CHHO to jump out of the local optimum for further optimization. Experimental results show that the CHHO has better optimization performance by comparing with the existing mainstream algorithms when solving QWSC problems. Additionally, the preprocessing approach not only greatly improves the optimization performance of the CHHO but also can be freely utilized in other meta-heuristics based approaches. INDEX TERMS Meta-heuristic algorithm, QoS-aware web service composition, Harris hawks optimization, fuzzy continuous, logical chaotic sequence.

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Section: Discussionmentioning

confidence: 99%

Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

Chen

2020

IEEE Access

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“…The aforementioned research has been improved on [16] whereby a second step was added based on the swapping method to exploit the search space knowledge of the best bees. Chandra et al [17] then introduced a modified instance of the ABC with the new search procedure applied to employed bees, and differential evolution instance applied to onlooker bees. The search procedure supports the exploration of the ABC, while exploitation is enhanced by differential evolution.…”

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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“…Chandra and Niyogi [32] proposed a modified artificial bee colony (mABC) algorithm. In this algorithm, a chaotic-based opposition learning method is used to initialize the population, and differential evolution (DE) is used to enhance exploitation.…”

Section: Related Workmentioning

confidence: 99%

Web Service Composition Optimization with the Improved Fireworks Algorithm

Jiang

Qin

Yang

et al. 2022

Mobile Information Systems

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Even though the number of services is increasing, a single service can just complete simple tasks. In the face of complex tasks, we require composite multiple services to complete them. For the purpose of improving the efficiency of web service composition, we propose a service composition approach based on an improved fireworks algorithm (FWA++). First, we use the strategy of random selection to keep N − 1 individuals for the next generation, and the purpose is to speed up the convergence speed of the FWA++ and enhance the search ability. Second, we randomize the total number of sparks and maximum amplitude of sparks for each generation. In this way, the search ability and the ability to jump out of the local optimal solution are dynamically balanced throughout the execution of the algorithm. Our experimental results show that compared with other existing approaches, the approach proposed in this paper is more efficient and stable.

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Web Service Selection Using Modified Artificial Bee Colony Algorithm

Cited by 21 publications

References 37 publications

A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Web Service Composition Optimization with the Improved Fireworks Algorithm

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