Predatory Search-based Chaos Turbo Particle Swarm Optimisation (PS-CTPSO): A new particle swarm optimisation algorithm for Web service combination problems

Xu, Xiaolong; Rong, Hanzhong; Pereira, Ella; Trovati, Marcello

doi:10.1016/j.future.2018.07.002

Cited by 20 publications

(12 citation statements)

References 21 publications

(32 reference statements)

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“…Furthermore, Gharbi and Mezni [151] proposed a method called relational concept analysis (RCA) to reduce search space. Heuristics techniques such as clonal selection algorithm [152] and harmony search [153] incorporated with skyline operator and a predatory search strategy [144] to enhance search capacity of traditional PSO. Da Silva et al [154] implemented a fully automatic service composition using a planning algorithm.…”

Section: Classification Of Hybrid Metaheuristicmentioning

confidence: 99%

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

et al. 2022

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With the advent of Service-Oriented Architecture (SOA), services can be registered, invoked, and combined by their identical Quality of Services (QoS) attributes to create a new value-added application that fulfils user requirements. Efficient QoS-aware service composition has been a challenging task in cloud computing. This challenge becomes more formidable in emerging resource-constrained computing paradigms such as the Internet of Things and Fog. Service composition has regarded as a multi-objective combinatorial optimization problem that falls in the category of NP-hard. Historically, the proliferation of services added to problem complexity and navigated solutions from exact (none-heuristics) approaches to near-optimal heuristics and metaheuristics. Although metaheuristics have fulfilled some expectations, the quest for finding a high-quality, near-optimal solution has led researchers to devise hybrid methods. As a result, research on service composition shifts towards the hybridization of metaheuristics. Hybrid metaheuristics have been promising efforts to transcend the boundaries of metaheuristics by leveraging the strength of complementary methods to overcome base algorithm shortcomings. Despite the significance and frontier position of hybrid metaheuristics, to the best of our knowledge, there is no systematic research and survey in this field with a particular focus on strategies to hybridize traditional metaheuristics. This study's core contribution is to infer a framework for hybridization strategies by conducting a mapping study that analyses 71 papers between 2008 and 2020. Moreover, it provides a panoramic view of hybrid methods and their experiment setting in respect to the problem domain as the main outcome of this mapping study. Finally, research trends, directions and challenges are discussed to benefit future endeavours.

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Section: Classification Of Hybrid Metaheuristicmentioning

confidence: 99%

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

et al. 2022

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“…Halfaoui et al [30] proposed a self-organized migration algorithm for selecting a web service as well as a fuzzy Pareto advantage to improve the algorithm and validate its superiority. Xu et al [31] proposed a chaotic turbo particle swarm optimization algorithm based on a predatory search to obtain candidate services. Ghobaei-Arani et al [32] and Dahan et al [33] proposed the cuckoo algorithm and the artificial bee colony algorithm to search for the best combination of web services.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Microservice Composition Based on Artificial Immune Algorithm Considering Fuzziness and User Preference

et al. 2020

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Microservices is a new paradigm in cloud computing that separates traditional monolithic applications into groups of services. These individual services may correlate or cross multi-clouds. Compared to a monolithic architecture, microservices are faster to develop, easier to deploy, and maintain by leveraging modern containers or other lightweight virtualization. To satisfy the requirements of end-users and preferences, appropriate microservices must be selected to compose complicated workflows or processes from within a large space of candidate services. The microservice composition should consider several factors, such as user preference, correlation effects, and fuzziness. Due to this problem being NP-hard, an efficient metaheuristic algorithm to solve large-scale microservice compositions is essential. We describe a microservice composition problem for multi-cloud environments that considers service grouping relations and corresponding correlation effects of the service providers within intra-or inter-clouds. We use the triangular fuzzy number to describe the uncertainty of QoS attributes, the improved fuzzy analytic hierarchy process to calculate multi-attribute QoS, construct fuzzy weights related to user preferences, and transform the multi-optimal problem into a single-optimal problem. We propose a new artificial immune algorithm based on the immune memory clone and clone selection algorithms. We also introduce several optimal strategies and conduct numerical experiments to verify effects and efficiencies. Our proposed method combines the advantages of monoclone, multi-clone, and co-evolution, which are suitable for the large-scale problems addressed in this paper. INDEX TERMS Fuzzy analytic hierarchy process (FAHP), microservice, microservice group, quality of service (QoS), QoS attribute, the parallel cooperative short-term memory injection multi-clone clonal selection algorithm (ParaCoSIMCSA).

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“…There are many works in the area of swarm optimization which is a nature-inspired algorithm where each member from a group of birds searches for food in different places and finally converges when food is found. The same algorithm is used to classify the web documents by searching for links across the documents in the group of web pages, and finally, all the partial solutions are merged to form a super optimal solution [6,7].…”

Section: Related Workmentioning

confidence: 99%

An Embedded-Based Weighted Feature Selection Algorithm for Classifying Web Document

Shankar

Palanivinayagam

VinayakumarR.

et al. 2020

Wireless Communications and Mobile Computing

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With the exponential increase in a number of web pages daily, it makes it very difficult for a search engine to list relevant web pages. In this paper, we propose a machine learning-based classification model that can learn the best features in each web page and helps in search engine listing. The existing methods for listing have lots of drawbacks like interfacing the normal operations of the website and crawling lots of useless information. Our proposed algorithm provides an optimal classification for websites which has a large number of web pages such as Wikipedia by just considering core information like link text, side information, and header text. We implemented our algorithm with standard benchmark datasets, and the results show that our algorithm outperforms the existing algorithms.

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Predatory Search-based Chaos Turbo Particle Swarm Optimisation (PS-CTPSO): A new particle swarm optimisation algorithm for Web service combination problems

Cited by 20 publications

References 21 publications

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

Optimization of Microservice Composition Based on Artificial Immune Algorithm Considering Fuzziness and User Preference

An Embedded-Based Weighted Feature Selection Algorithm for Classifying Web Document

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