This paper presents methods for implementing SOMA (self-organizingmigrating Algorithm) in parallel with the CUDA (Compute Unified Device Architecture) system that can be used to perform the dominant of up-speed when ...
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ISBN:
(纸本)9783319509044;9783319509037
This paper presents methods for implementing SOMA (self-organizingmigrating Algorithm) in parallel with the CUDA (Compute Unified Device Architecture) system that can be used to perform the dominant of up-speed when using SOMA algorithm. SOMA has many individual points to find the global minimum which is the key for paralleling this system because each individual can work separately and share the position for all when it moves. Nowadays, due to the humongous size of data and the limitation of the process in single Central Processing Unit (CPU), it becomes impossible to deal with. As a result of these limitations, we need more CPUs working at the same time to do the same job or take advantage of the power of parallel processing in GPGPU (General-Purpose graphics processing unit). Additionally, many supercomputers are built with the need of Parallel Processing in order to meet the power of hardware. Based on the architecture of CUDA, it can handle the threads in SOMA independence. We use two methods with different architecture in CUDA to help SOMA run much faster than single threading method. This paper also uses some techniques to help SOMA work more effective.
Evolutionary algorithms (EAs) have proven to be a powerful and robust optimizing technique even for complex optimization problems. The main aim of this work is to show that such a powerful simulating and optimizing of...
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ISBN:
(纸本)9783319272474;9783319272450
Evolutionary algorithms (EAs) have proven to be a powerful and robust optimizing technique even for complex optimization problems. The main aim of this work is to show that such a powerful simulating and optimizing of a non-linear dynamic process. In this paper, the complex reaction sequence used to study various reaction kinetics by optimization the rate constants. Two algorithms from the field of artificial intelligent-Differential evolution (DE), self-organizingmigrating algorithm (SOMA) are used in this investigation. Two optimization techniques were developed using Mathematica for accurately determining the rate constants of the reaction at certain temperature from the experimental data. The results show that EAs are used successfully in the process optimization.
In the paper, we try to provide a comprehensive look on a multi-objective design of radiating, guiding and reflecting structures fabricated both from special materials (semiconductors, high-impedance surfaces) and con...
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In the paper, we try to provide a comprehensive look on a multi-objective design of radiating, guiding and reflecting structures fabricated both from special materials (semiconductors, high-impedance surfaces) and conventional ones (microwave substrates, fully metallic antennas). Discussions are devoted to the proper selection of the numerical solver used for evaluating partial objectives, to the selection of the domain of analysis, to the proper formulation of the multi-objective function and to the way of computing the Pareto front of optimal solutions (here, we exploit swarm-intelligence algorithms, evolutionary methods and self-organizing migrating algorithms). The above-described approaches are applied to the design of selected types of microwave antennas, transmission lines and reflectors. Considering obtained results, the paper is concluded by generalizing remarks.
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