Harmony Search for Self-configuration of Fault–Tolerant and Intelligent Grids

Balicki, Jerzy; Korłub, Waldemar; Paluszak, Jacek; Tyszka, Maciej

doi:10.1007/978-3-319-45378-1_50

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…The Benchmark855 was used for this purpose, too. We consider fifteen non-dominated solutions obtained by MQPSO, Non-dominated Sorting Genetic Algorithm II (NSGA-II) [ 37 ], Multi-criteria Genetic Programming (MGP) [ 38 ], Multi-criteria Differential Evolution (MDE) [ 4 ] and Multi-criteria Harmony Search (MHS) [ 39 ].…”

Section: Numerical Experimentsmentioning

confidence: 99%

Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud

Balicki

2021

Entropy

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Particle swarm optimization algorithm (PSO) is an effective metaheuristic that can determine Pareto-optimal solutions. We propose an extended PSO by introducing quantum gates in order to ensure the diversity of particle populations that are looking for efficient alternatives. The quality of solutions was verified in the issue of assignment of resources in the computing cloud to improve the live migration of virtual machines. We consider the multi-criteria optimization problem of deep learning-based models embedded into virtual machines. Computing clouds with deep learning agents can support several areas of education, smart city or economy. Because deep learning agents require lots of computer resources, seven criteria are studied such as electric power of hosts, reliability of cloud, CPU workload of the bottleneck host, communication capacity of the critical node, a free RAM capacity of the most loaded memory, a free disc memory capacity of the most busy storage, and overall computer costs. Quantum gates modify an accepted position for the current location of a particle. To verify the above concept, various simulations have been carried out on the laboratory cloud based on the OpenStack platform. Numerical experiments have confirmed that multi-objective quantum-inspired particle swarm optimization algorithm provides better solutions than the other metaheuristics.

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Section: Numerical Experimentsmentioning

confidence: 99%

Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud

Balicki

2021

Entropy

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“…The whole process keeps iterating until the termination condition is reached. The multiobjective optimization process is as follows 6 and its flowchart is shown in Figure 1 f(x 2 ),. .…”

Section: Related Workmentioning

confidence: 99%

“…The whole process keeps iterating until the termination condition is reached. The multiobjective optimization process is as follows 6 and its flowchart is shown in Figure 1. Define vector minimization multiple target functions according to the problem: V -min f ( x ) = [ f ( x 1 ), f ( x 2 ),…, f ( x n )] T , Initialize the HMCR , PAR , bw parameters of the algorithm; Initialize the harmony memory library, the library size is HMS .…”

Section: Related Workmentioning

confidence: 99%

Parallel algorithm of multiobjective optimization harmony search based on cloud computing

Wenhong

Tang

et al. 2017

Journal of Algorithms & Computational Technology

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In order to solve the problems of traditional harmony search in complex function multiobjective optimization, such as low precision, slow convergence, and easy to fall into local optimum, this article proposes a multiobjective optimization harmony search parallel algorithm based on cloud computing. First, according to the characteristics that the traditional harmony search algorithm uses a single harmony library for storing and processing the memory harmony, and it is divided into multiple harmony sublibraries according to different harmony. At the same time, the roulette selection and dynamic trade-off factor strategies are used for the dynamic setting of harmony memory library value-taking probability, pitch fine-tuning probability, pitch fine-tuning bandwidth, and other parameters which the traditional harmony search algorithm mainly relies on. Then, MapReduce programming model is used to establish Map and Reduce core parallel computing functions, to construct the parallel algorithm of dynamic parameter harmony search based on cloud computing. Finally, the algorithm optimization comparison test is conducted on Hadoop platform and compared with several existing optimal harmony search algorithms, the searching precision of this algorithm is improved by eight orders of magnitude, and the iteration number on the convergence speed is reduced by 6500 times, and the parallel achieves the linear acceleration ratio. Experimental results show that the optimization efficiency of this algorithm is higher than several existing optimal harmony search algorithms.

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Harmony Search for Self-configuration of Fault–Tolerant and Intelligent Grids

Cited by 2 publications

References 31 publications

Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud

Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud

Parallel algorithm of multiobjective optimization harmony search based on cloud computing

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