Optimization of Location Allocation of Web Services Using a Modified Non-dominated Sorting Genetic Algorithm

Tan, Boxiong; Ma, Hui; Zhang, Mengjie

doi:10.1007/978-3-319-28270-1_21

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…They formulated the problem as a stochastic mixed integer program and proposed a simulation-based hybrid heuristic to solve the dynamic problem under different response time service level. In contrast, in our previous studies [11], [12], we proposed a multi-objective algorithm with linear aggregation using PSO and a multi-objective algorithm with Pareto front using NSGA-II. Both results show that multi-objective model suits the problem well.…”

Section: Related Workmentioning

confidence: 91%

“…In addition, we make a comparison between a dynamic function with a congregation of several static rounding functions with different thresholds. Lastly, we conduct an experiment considering the overall performance of a BMOPSOCD with a dynamic rounding function in comparison with three other algorithms: PSO, BNSPSO and NSGA-II (see [12]).…”

Section: Experiments Designmentioning

confidence: 99%

“…All fitness values are normalized between 0 and 1 with a linear normalization method described in [12]. The normalization method requires the maximum and minimum values of cost and response time that we found during optimization.…”

Section: Comparison Designmentioning

confidence: 99%

“…To evaluate the performance of our proposed BMOPSOCD with dynamic function, we conducted experiments to compare its performance with BNSPSO, BPSO from our previous research [11], and NSGA-II from [12]. Based on the analysis in Section 5.5, we use the Quadratic function as the rounding function.…”

Section: Bmopsocd Versus Bnspso Nsga-ii and Bpsomentioning

confidence: 99%

“…Even though the BPSO approach can solve the problem, a single-objective algorithm can only provide one solution for each run, hence, BPSO cannot provide alternatives when service providers do not have preferences. Therefore, we further investigated a Pareto front approach [12] with Sorting Genetic Algorithm-II (NSGA-II).…”

Section: Introductionmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 91%

Section: Experiments Designmentioning

confidence: 99%

Section: Comparison Designmentioning

confidence: 99%

Section: Bmopsocd Versus Bnspso Nsga-ii and Bpsomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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