Abstract
The paper proposes the ways to apply swarm intelligence strategies to parallelize neuroevolution methods for synthesizing artificial neural networks. The proposed approaches will solve a number of problems that usually arise during designing high-performance computing related to the synthesis of neural networks. The object of research is the process of developing a parallel approach for the neuroevolution synthesis of artificial neural networks, namely, the use of swarm intelligence strategies to solve a number of problems in designing a method that would use the resources of a parallel computer system.One of the most problematic areas is the highly adaptive nature and significant operating time of neuroevolution methods. One way to solve these problems is to use parallel computer systems and distributed computing. However, a number of questions arise when designing a parallel neuroevolution method.During research a number of tasks were solved, which included the analysis and study of neuroevolution methods for synthesizing artificial neural networks and problems of their parallelization. Attention is also paid to swarm intelligence methods, which have gained popularity recently and show good results.The new method developed during the work was based on strategies for organizing work with swarm particles. Thus, sub-populations distributed between threads and individuals were analyzed as individual particles that interact with each other and depend on the local environment. Classical genetic operators were modified by criterion mechanisms to improve adaptability.During the experiments, the developed method was compared with classical methods. During the work, special attention was paid not only to the characteristics of the resulting neuromodels, but also to the load on the processor during Operation. The developed method showed acceptable results for all comparisons. The new approach has significantly improved the quality level of the parallel neuroevolution synthesis method, allowing to evenly use the capabilities of computing nodes in a parallel system
Highlights
The usage of machine learning techniques and artificial neural network (ANN) tools facilitates, automates and improves accuracy during processing many types of data
The new approach has significantly improved the quality level of the parallel neuroevolution synthesis method, allowing to evenly use the capabilities of computing nodes in a parallel system
The object of research is the process of designing a parallel method of neuroevolution synthesis of ANN, namely, the using of swarm intelligence strategies to solve a number of problems in designing a method that would use the resources of a parallel computer system
Summary
The usage of machine learning techniques and artificial neural network (ANN) tools facilitates, automates and improves accuracy during processing many types of data. The synthesis of a model based on the ANN consists of two main stages: initial determination of the topology (architecture or structure of the ANN) and training (determination of the weights of connections, neurons, etc.). The error propagation method has become widespread and popular. This method has a number of disadvantages [1]:. It is difficult to predict how long the training itself will take, because much depends on the input data, a certain network architecture, and so on; – the problem of network retraining. The error propagation method is used only for the training stage of the already developed ANN topo logy. An alternative approach has always been evolutionary methods [1], which in the context of ANN synthesis are referred to as neuroevolution methods
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