Binary PSO for Web Service Location-Allocation

Tan, Boxiong; Huang, Hai; Ma, Hui; Zhang, Mengjie

doi:10.1007/978-3-319-51691-2_31

Cited by 7 publications

(6 citation statements)

References 31 publications

(31 reference statements)

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“…For now, the problem model considers each service as an atomic service. With the increasing usages of composite services composed with atomic services distributed over the Internet [46], [47], we need to consider service composition workflow while solving WSLAP. Service composition workflows have a significant impact on the allocation of atomic services because the data flow between services could not be neglected.…”

Section: Discussionmentioning

confidence: 99%

Evolutionary Multi-Objective Optimization for Web Service Location Allocation Problem

Tan

Mei

et al. 2021

IEEE Trans. Serv. Comput.

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With the ever increasing number of functionally similar web services being available on the Internet, the market competition is becoming intense. Web service providers (WSPs) realize that good Quality of Service (QoS) is a key of business success and low network latency is a critical measurement of good QoS. Because network latency is related to location, a straightforward way to reduce network latency is to allocate services to proper locations. However, Web Service Location Allocation Problem (WSLAP) is a challenging task since there are multiple objectives potentially conflicting with each other and the solution search space has a combinatorial nature. In this paper, we consider minimizing the network latency and total cost simultaneously and model the WSLAP as a multi-objective optimization problem. We develop a new PSO-based algorithm to provide a set of trade-off solutions. The results show that the new algorithm can provide a more diverse range of solutions than the compared three well known multi-objective optimization algorithms. Moreover, the new algorithm performs better especially on large problems.

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Section: Discussionmentioning

confidence: 99%

Evolutionary Multi-Objective Optimization for Web Service Location Allocation Problem

Tan

Mei

et al. 2021

IEEE Trans. Serv. Comput.

Self Cite

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“… 2021 ), gate matrix layout (de Oliveira and Lorena 2002 ), web service location-allocation (Tan et al. 2017 ), water distribution system (Zheng et al. 2014 ), oil well drilling (Guria et al.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

“…Tan et al suggested an improved binary PSO algorithm which uses adaptive inertia technology to assign web service locations (Tan et al. 2017 ). Figure 6 presents its binary encoding, and indicates whether service s is allocated at position j .…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

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This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

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“…Based on the analysis, Liu et al [97] proposed a BPSO algorithm named Up BPSO, which outperformed the standard BPSO algorithm with a constant and a linearly decreasing inertia weight. Up BPSO was already applied to achieve web-service allocation [98], which aimed to minimize network latency and cost to deploy servers.…”

Section: Binary Pso (Bpso)mentioning

confidence: 99%

Evolutionary Computation for Feature Selection in Classification

Nguyễn¹

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<p>Classification aims to identify a class label of an instance according to the information from its characteristics or features. Unfortunately, many classification problems have a large feature set containing irrelevant and redundant features, which reduce the classification performance. In order to address the above problem, feature selection is proposed to select a small subset of relevant features. There are three main types of feature selection methods, i.e. wrapper, embedded and filter approaches. Wrappers use a classification algorithm to evaluate candidate feature subsets. In embedded approaches, the selection process is embedded in the training process of a classification algorithm. Different from the other two approaches, filters do not involve any classification algorithm during the selection process. Feature selection is an important process but it is not an easy task due to its large search space and complex feature interactions. Because of the potential global search ability, Evolutionary Computation (EC), especially Particle Swarm Optimization (PSO), has been widely and successfully applied to feature selection. However, there is potential to improve the effectiveness and efficiency of EC-based feature selection. The overall goal of this thesis is to investigate and improve the capability of EC for feature selection to select small feature subsets while maintaining or even improving the classification performance compared to using all features. Different aspects of feature selection are considered in this thesis such as the number of objectives (single-objective/multi-objective), the fitness function (filter/wrapper), and the searching mechanism. This thesis introduces a new fitness function based on mutual information which is calculated by an estimation approach instead of the traditional counting approach. Results show that the estimation approach works well on both continuous and discrete data. More importantly, mutual information calculated by the estimation approach can capture feature interactions better than the traditional counting approach. This thesis develops a novel binary PSO algorithm, which is the first work to redefine some core concepts of PSO such as velocity and momentum to suit the characteristics of binary search spaces. Experimental results show that the proposed binary PSO algorithm evolve better solutions than other binary EC algorithms when the search spaces are large and complex. Specifically, on feature selection, the proposed binary PSO algorithm can select smaller feature subsets with similar or better classification accuracies, especially when there are a large number of features. This thesis proposes surrogate models for wrapper-based feature selection. The surrogate models use surrogate training sets which are subsets of informative instances selected from the training set. Experimental results show that the proposed surrogate models assist PSO to reduce the computational cost while maintaining or even improving the classification performance compared to using only the original training set. The thesis develops the first wrapper-based multi-objective feature selection algorithm using MOEA/D. A new decomposition strategy using multiple reference points for MOEA/D is designed, which can deal with different characteristics of multi-objective feature selection such as highly discontinuous Pareto fronts and complex relationships between objectives. The experimental results show that the proposed algorithm can evolve more diverse non-dominated sets than other multi-objective algorithms. This thesis introduces the first PSO-based feature selection algorithm for transfer learning. In the proposed algorithm, the fitness function uses classification performance to reduce the differences between domains while maintaining the discriminative ability on the target domain. The experimental results show that the proposed algorithm can select feature subsets which achieve better classification performance than four state-of-the-art feature-based transfer learning algorithms.</p>

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Binary PSO for Web Service Location-Allocation

Cited by 7 publications

References 31 publications

Evolutionary Multi-Objective Optimization for Web Service Location Allocation Problem

Evolutionary Multi-Objective Optimization for Web Service Location Allocation Problem

A survey on binary metaheuristic algorithms and their engineering applications

Evolutionary Computation for Feature Selection in Classification

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