A parallel refined probabilistic approach for QoS-aware service composition

Wang, Hongbing; Peng, Shunshun; Yu, Qi

doi:10.1016/j.future.2019.03.053

Cited by 8 publications

(2 citation statements)

References 42 publications

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“…Game theory and a fctitious play process are combined to help improve performance. Peng et al [30] and Wang et al [31] used a restricted Boltzmann machine to learn the probability information of the global optimization contribution of concrete service. Te information helps guide the search for solutions.…”

Section: Utility-based Multi-objective Service Compositionmentioning

confidence: 99%

Multi‐Objective Service Composition Using Enhanced Multi‐Objective Differential Evolution Algorithm

Peng

Guo

2023

Computational Intelligence and Neuroscience

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In recent years, the optimization of multi-objective service composition in distributed systems has become an important issue. Existing work makes a smaller set of Pareto-optimal solutions to represent the Pareto Front (PF). However, they do not support complex mapping of the Pareto-optimal solutions to quality of service (QoS) objective space, thus having limitations in providing a representative set of solutions. We propose an enhanced multi-objective differential evolution algorithm to seek a representative set of solutions with good proximity and distributivity. Specially, we propose a dual strategy to adjust the usage of different creation operators, to maintain the evolutionary pressure toward the true PF. Then, we propose a reference vector neighbor search to have a fine-grained search. The proposed approach has been tested on a real-world dataset that locates a representative set of solutions with proximity and distributivity.

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Section: Utility-based Multi-objective Service Compositionmentioning

confidence: 99%

Multi‐Objective Service Composition Using Enhanced Multi‐Objective Differential Evolution Algorithm

Peng

Guo

2023

Computational Intelligence and Neuroscience

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“…Besides, this method selects services with better QoS to avoid exhaustive compositions while finding a set of optimal solutions. The work of [36] proposes a parallel refined probabilistic approach to improve performance. Multiple agents work in parallel to speed up the convergence and an elegant probabilistic model is constructed to guide the optima.…”

Section: B Qos-aware Service Compositionmentioning

confidence: 99%

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

Peng

Wang

2020

IEEE Access

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Service composition provides an effective means to fulfill users' personalized requirements and complex business applications. With the number of web services rapidly growing, finding the best combination among services based on quality of service (QoS) poses a critical computational challenge due to the exponential growth of alternative composite services. Some efforts are developed to find the near-optimal service combination within an acceptable time range. They fall into two categories of solutions: exploring partial combinations in the service space and downsizing the optimization problem in scale. Although they solve the scalability problem to some extent, the required computational time is usually high. A promising direction is to integrate these two categories of solutions. However, its practical application suffers from three challenges: no good search scheme, no consideration of the natural organization of sub-problems, and the lack of diverse combinations. In this work, we propose a novel approach, called multi-clusters adaptive brain storm optimization (MCaBSO) algorithm. The proposed method uses brain storm optimization (BSO) as the search scheme to combine the division of search space with the exploration of the reduced search space. MCaBSO uses the twin support vector machine (TWSVM) to effectively divide the search space according to the natural organization of sub-problems. MCaBSO provides an adaptive dual strategy that gives guidance for the generation of diverse combinations. MCaBSO enables the agile exploration of the reduced search space and generates more high-quality combinations. MCaBSO is evaluated on two datasets to show effectiveness and efficiency.

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A novel and efficient salp swarm algorithm for large-scale QoS-aware service composition selection

Ren

et al. 2022

Computing

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A parallel refined probabilistic approach for QoS-aware service composition

Cited by 8 publications

References 42 publications

Multi‐Objective Service Composition Using Enhanced Multi‐Objective Differential Evolution Algorithm

Multi‐Objective Service Composition Using Enhanced Multi‐Objective Differential Evolution Algorithm

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

A novel and efficient salp swarm algorithm for large-scale QoS-aware service composition selection

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