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A network is an aggregation of nodes joined by edges, representing entities and their relationships. In social network clustering, nodes are organized into clusters according to their connectivity patterns, with the goal of community detection. The detection of community structures in networks is essential. However, existing techniques for community detection have not yet utilized the potential of the Fish School Search (FSS) algorithm and modularity principles. We have proposed a novel method, clustering with the Fish School Search algorithm and modularity function (FSC), that enhances modularity in network clustering by iteratively partitioning the network and optimizing the modularity function using the Fish School Search Algorithm. This approach facilitates the discovery of highly modular community structures, improving the resolution and effectiveness of network clustering. We tested FSC on well-known and unknown network structures. Also, we tested it on a network generated using the LFR model to test its performance on networks with different community structures. Our methodology demonstrates strong performance in identifying community structures, indicating its effectiveness in capturing cohesive communities and accurately identifying actual community structures.

Objectives. Recent research in machine learning and artificial intelligence aimed at improving prediction accuracy and reducing computational complexity resulted in a novel neural network architecture referred to as an extreme learning machine (ELM). An ELM comprises a single-hidden-layer feedforward neural network in which the weights of connections among input-layer neurons and hidden-layer neurons are initialized randomly, while the weights of connections among hidden-layer neurons and output-layer neurons are computed using a generalized Moore– Penrose pseudoinverse operation. The replacement of the iterative learning process currently used in many neural network architectures with the random initialization of input weights and the explicit computation of output weights significantly increases the performance of this novel machine learning algorithm while preserving good generalization performance. However, since the random initialization of input weights does not necessarily guarantee optimal prediction accuracy, the purpose of the present work was to develop and study approaches to intelligent adjustment of input weights in ELMs using bioinspired algorithms in order to improve the prediction accuracy of this data analysis tool in regression problems.Methods. Methods of optimization theory, theory of evolutionary computation and swarm intelligence, probability theory, mathematical statistics and systems analysis were used.Results. Approaches to the intelligent adjustment of input weights in ELMs were developed and studied. These approaches are based on the genetic algorithm, the particle swarm algorithm, the fish school search algorithm, as well as the chaotic fish school search algorithm with exponential step decay proposed by the authors. By adjusting input weights with bioinspired optimization algorithms, it was shown that the prediction accuracy of ELMs in regression problems can be improved to reduce the number of hidden-layer neurons to reach a high prediction accuracy on learning and test datasets. In the considered problems, the best ELM configurations can be obtained using the chaotic fish school search algorithm with exponential step decay.Conclusions. The obtained results showed that the prediction accuracy of ELMs can be improved by using bioinspired algorithms for the intelligent adjustment of input weights. Additional calculations are required to adjust the weights; therefore, the use of ELMs in combination with bioinspired algorithms may be advisable where it is necessary to obtain the most accurate and most compact ELM configuration.