Cloudde: A Heterogeneous Differential Evolution Algorithm and Its Distributed Cloud Version

Zhan, Zhi-Hui; Liu, Xiao-Fang; Zhang, Huaxiang; Yu, Zhengtao; Weng, Jian; Li, Yun; Gu, Tianlong; Zhang, Jun

doi:10.1109/tpds.2016.2597826

Cited by 141 publications

(73 citation statements)

References 60 publications

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“…One is to enhance the search ability of existing EAs by re-scheduling the local search operators [20], [21]; while the other is to simplify the problem via Divide-and-Conquer (DC) [22], [23], [24] or Dimension Reduction [25], [26]. For saving the computational time in each iteration, parallel computing or distributed computing techniques are frequently employed to optimize individual solutions or decision variables on different threads [12], [13], [27], [28], [29]. Among them, the DC methodology has been frequently introduced into EAs for large-scale optimization problems, since it can be regarded as an integrated solution to improve the above two factors for EAs.…”

Section: The Divide-and-conquer Based Easmentioning

confidence: 99%

“…The search step-size is adjusted with Eq. (12). For DC-NG and DC-NG-P, as the sub-problems are all 1-dimensional, each partial solution is actually a scalar, which is the same case with NPDC.…”

Section: Algorithm Settingsmentioning

confidence: 99%

“…• For many large-scale real-world optimization problems with real-time constraints, e.g., the placement of virtual machines in large-scale cluster [10], the above two drawbacks make EAs ill-equipped to handle them satisfactorily. For the last decade, researchers have made great efforts on solving the above two drawbacks [11], [12], [13]. Among the existing methods, the idea of Divide-and-Conquer (DC) has attracted most research attentions, as it is often viewed as an integrated solution for both drawbacks [14].…”

Section: Introductionmentioning

confidence: 99%

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A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

2019

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Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas. As a powerful optimization tool for many real-world applications, evolutionary algorithms (EAs) fail to solve the emerging large-scale problems both effectively and efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA that can not only produce high-quality solution by solving sub-problems separately, but also highly utilizes the power of parallel computing by solving the sub-problems simultaneously. Existing DC-based EAs that were deemed to enjoy the same advantages of the proposed algorithm, are shown to be practically incompatible with the parallel computing scheme, unless some trade-offs are made by compromising the solution quality.

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Section: The Divide-and-conquer Based Easmentioning

confidence: 99%

Section: Algorithm Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

2019

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“…Cloud computing is a large-scale distributed computing paradigm in which customers are able to access elastic resources on demand over the Internet by a pay-as-you-go principle [1][2][3]. Cloud computing facilitates services at three different levels, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), and four deployment models, including private cloud, community cloud, public cloud, and hybrid cloud [4].…”

Section: Introductionmentioning

confidence: 99%

An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing

2017

Self Cite

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Abstract:Energy efficiency is a significant topic in cloud computing. Dynamic consolidation of virtual machines (VMs) with live migration is an important method to reduce energy consumption. However, frequent VM live migration may cause a downtime of service. Therefore, the energy save and VM migration are two conflict objectives. In order to efficiently solve the dynamic VM consolidation, the dynamic VM placement (DVMP) problem is formed as a multiobjective problem in this paper. The goal of DVMP is to find a placement solution that uses the fewest servers to host the VMs, including two typical dynamic conditions of the assignment of new coming VMs and the re-allocation of existing VMs. Therefore, we propose a unified algorithm based on an ant colony system (ACS), termed the unified ACS (UACS), that works on both conditions. The UACS firstly uses sufficient servers to host the VMs and then gradually reduces the number of servers. With each especial number of servers, the UACS tries to find feasible solutions with the fewest VM migrations. Herein, a dynamic pheromone deposition method and a special heuristic information strategy are also designed to reduce the number of VM migrations. Therefore, the feasible solutions under different numbers of servers cover the Pareto front of the multiobjective space. Experiments with large-scale random workloads and real workload traces are conducted to evaluate the performance of the UACS. Compared with traditional heuristic, probabilistic, and other ACS based algorithms, the proposed UACS presents competitive performance in terms of energy consumption, the number of VM migrations, and maintaining quality of services (QoS) requirements.

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“…Other recent fruitful applications of WOA include software testing, 23 wind speed prediction, 24 and power systems. Furthermore, in the work of Zhan et al, 29 the use of EAs on cloud computing was proposed and a parallelized heterogeneous Differential Evolution (DE) algorithm on a distributed cloud was developed. However, when faced with complex problems, its performance decreases as huge computational efforts and time will be needed.…”

mentioning

confidence: 99%

Distributed Whale Optimization Algorithm based on MapReduce

Khalil

Alshayeji

Ahmad

2018

Concurrency and Computation

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Whale Optimization Algorithm (WOA) is a recent swarm intelligence based meta-heuristic optimization algorithm, which simulates the natural behavior of bubble-net hunting strategy of humpback whales and has been successfully applied to solve complex optimization problems in a wide range of disciplines. However, when applied to large-size problems, its performance degrades due to the need of massive computational work load. Distributed computing is one of the effective ways to improve the scalability of WOA for solving large-scale problems. In this paper, we propose a simple and robust distributed implementation of WOA using Hadoop MapReduce named MR-WOA. MapReduce paradigm is adopted as the distribution model since it is one of the most mature technologies to develop parallel algorithms which automatically handles communication, load balancing, data locality, and fault tolerance. The design of MR-WOA is discussed in details using the MapReduce paradigm. Experiments are conducted for a set of well-known benchmarks for evaluating the quality, speedup, and scalability of MR-WOA. The conducted experiments reveal that our approach achieves a promising speedup. For some benchmarks, speedup scales linearly with increasing the number of computational nodes.

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Cloudde: A Heterogeneous Differential Evolution Algorithm and Its Distributed Cloud Version

Cited by 141 publications

References 60 publications

A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing

Distributed Whale Optimization Algorithm based on MapReduce

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