A novel Web service publishing model based on social spider optimization technique

Priyadharshini, V.; Divya, P.; Preethi, D.; Pazhaniraja, N.; Paul, Peter V.

doi:10.1109/iccpeic.2015.7259488

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Also, few researchers introduced the optimization solutions for the selection problem using a social spider algorithm (SSO). Mousa et al [9] proposed SSO as a solution for services selection process-aware QoS constraints. The proposed model introduced in the general web services environment by considering the objective QoS information only.…”

Section: Related Workmentioning

confidence: 99%

“…It can be measures by seconds. To optimize the QoS value, the behavior of factors are varied from one factor to another [9]. Some factors optimized by getting the minimum values; they called Negative QoS factors.…”

Section: Qos Composition Modelmentioning

confidence: 99%

“…[20] to find an optimal solution of complex optimization problems in continuous search space. In this artical the original SSO and proposed RI-SSO used to solve the services selection problem in discrete search space, namely the Nearest Integer method [9]. One of the primary behavior in the SSO colony is mating behavior, females used the vibrations of the male spiders over the web to determine a heavier spider fitness.…”

Section: Fitness Functionmentioning

confidence: 99%

“…The second point, the behavior of attraction or dislike between females and males, has been evolved based on the propagation of the vibrations across the web from males to females. In original SSO, the strength of vibrations intensity sensed based on two properties [22] weight and distance as physical energy phenomena as in equations (8), (9), and (11). The more energetic vibration generated either by large spiders or neighboring members on the web.…”

Section: Proposed Modelmentioning

confidence: 99%

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An Efficient QoS-aware Services Selection in IoT using A Reputation Improved- Social Spider Optimization Algorithm

Abosaif

Elrofai

2020

Preprint

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Internet of Things (IoT) has been grown rapidly over the last years to connect a considerable number of spatially distributed objects or actuators. The connected objects create new functionality and provide various services to enhance and satisfy End-users daily lives. The issue is to provide the End-users with optimal services based on their requirements. The critical challenge is to select the optimal service from similar services functionally and various services non-functionality requirements (Quality of services). To achieve this challenge, this paper proposed a services selection model under QoS constraints in the IoT environment. The introduced model implements a meta-heuristic optimization algorithm with a friendly Likert scale measurement method. It aims to improve the performance of bio-inspired optimizing algorithms, called a Social Spider Optimization (SSO) Algorithm, by adding a reputation value to member's weight. The proposed model used a Likert scale measurement to evaluate the reputation of the services from the End-users. In the experiments, a comparative study was done between an original SSO and the proposed RI-SSO model. The results show the efficiency of the proposed RI-SSO model against the original SSO, in both maximization and minimization problems. It obtains a better outperform in terms of fitness values.

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Section: Related Workmentioning

confidence: 99%

Section: Qos Composition Modelmentioning

confidence: 99%

Section: Fitness Functionmentioning

confidence: 99%

Section: Proposed Modelmentioning

confidence: 99%

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An Efficient QoS-aware Services Selection in IoT using A Reputation Improved- Social Spider Optimization Algorithm

Abosaif

Elrofai

2020

Preprint

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Toward text psychology analysis using social spider optimization algorithm

Hosseinalipour

Gharehchopogh

Màsdàrí

et al. 2021

Concurrency and Computation

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Different nature‐inspired meta‐heuristic algorithms have been proposed to solve optimization problems. One of these algorithms is called social spider optimization (SSO) algorithm. Spiders' natural behaviors have inspired them to find the bait position by detecting vibrations in their web. Although the SSO algorithm has good accuracy in achieving optimal solutions, it suffers from a low convergence rate. In this paper, we attempted to improve SSO by changing its motion and mating parameters. To provide a practical example of using the new proposed algorithm, we based it on multi‐objective opposition‐based SSO, named MOPSSO. We used this algorithm in a feature selection process for analyzing text psychology, which is a multi‐objective problem. Textual psychology analysis is used in various fields, including collecting and analyzing people's views on various products, topics, social and political events. After selecting features, in order to classify the text, we used a new hybrid method that hybrids fuzzy C‐MEANS data clustering technique, a decision tree (DT), and Naïve Bayes (NB). Experimental results show that the improved SSO algorithm performs better than SSO, social spider algorithm, and CMA‐ES algorithms. Additionally, the performance of the proposed hybrid classification method is better than those of NB and DT.

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