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Cooperative Coevolution Differential Evolution Based on Spark for Large-Scale Optimization Problems
Tan, Xujie,Lee, Hyun-Ae,Shin, Seong-Yoon The Korea Institute of Information and Commucation 2021 Journal of information and communication convergen Vol.19 No.3
Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.
Differential Evolution with Multi-strategies based Soft Island Model
Tan, Xujie,Shin, Seong-Yoon The Korea Institute of Information and Commucation 2019 Journal of information and communication convergen Vol.17 No.4
Differential evolution (DE) is an uncomplicated and serviceable developmental algorithm. Nevertheless, its execution depends on strategies and regulating structures. The combination of several strategies between subpopulations helps to stabilize the probing on DE. In this paper, we propose a unique k-mean soft island model DE(KSDE) algorithm which maintains population diversity through soft island model (SIM). A combination of various approaches, called KSDE, intended for migrating the subpopulation information through SIM is developed in this study. First, the population is divided into k subpopulations using the k-means clustering algorithm. Second, the mutation pattern is singled randomly from a strategy pool. Third, the subpopulation information is migrated using SIM. The performance of KSDE was analyzed using 13 benchmark indices and compared with those of high-technology DE variants. The results demonstrate the efficiency and suitability of the KSDE system, and confirm that KSDE is a cost-effective algorithm compared with four other DE algorithms.
Genetic algorithm‐based content distribution strategy for F‐RAN architectures
Xujie Li,Ziya Wang,Ying Sun,Siyuan Zhou,Yanli Xu,Guoping Tan 한국전자통신연구원 2019 ETRI Journal Vol.41 No.3
Fog radio access network (F‐RAN) architectures provide markedly improved performance compared to conventional approaches. In this paper, an efficient genetic algorithm‐based content distribution scheme is proposed that improves the throughput and reduces the transmission delay of a F‐RAN. First, an F‐RAN system model is presented that includes a certain number of randomly distributed fog access points (F‐APs) that cache popular content from cloud and other sources. Second, the problem of efficient content distribution in F‐RANs is described. Third, the details of the proposed optimal genetic algorithm‐based content distribution scheme are presented. Finally, simulation results are presented that show the performance of the proposed algorithm rapidly approaches the optimal throughput. When compared with the performance of existing random and exhaustive algorithms, that of the proposed method is demonstrably superior.
Hybrid Differential Evolution Technique
Liu Xiao-Wen,Xujie Tan,Hyun-Chang Lee,Oh-Hyung Kang,Seong-Yoon Shin 한국정보통신학회 2021 2016 INTERNATIONAL CONFERENCE Vol.12 No.1
In this paper, we proposed a hybrid DE based on an ecological model algorithm called SparkHDE-EM, where we introduced a Spark-based island model to implement parallelization of different DE transformations and maintain a balance between resources using the Monod model. It includes three DE variants: jDE, JADE and CUDE.