Dual optimization approach in discrete Hopfield neural network

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 164, P. 111929 - 111929

Published: July 7, 2024

Language: Английский

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Enhanced fractional probabilistic self-organizing maps with genetic algorithm optimization (EF-PRSOM)

Evolutionary Intelligence, Journal Year: 2025, Volume and Issue: 18(2)

Published: March 14, 2025

Language: Английский

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A Hybrid Genetic Algorithm with Tabu Search Using a Layered Process for High-Order QAM in MIMO Detection

Mathematics, Journal Year: 2024, Volume and Issue: 13(1), P. 2 - 2

Published: Dec. 24, 2024

In this paper, we propose a hybrid genetic algorithm (HGA) that embeds the tabu search mechanism into (GA) for multiple-input multiple-output (MIMO) detection. We modified selection and crossover operation to maintain diverse wide exploration areas, which is an advantage of GA, mutation perform local specific region. process, ’tabu’ concept also employed prevent repeated same area. addition, layered detection process applied simultaneously with proposed algorithm, not only improves bit error rate performance but reduces computational complexity. apply HGA (LHGA) MIMO system very high modulation order such as 64-quadrature amplitude (QAM), 256-QAM, 1024-QAM. Simulation results show LHGA outperforms conventional approaches. Especially, in 1024-QAM system, has less than 10% complexity 6 dB signal-to-noise ratio (SNR) gain compared GA-based scheme.

Language: Английский

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An Innovative Application of Swarm‐Based Algorithms for Peer Clustering

International Journal of Intelligent Systems, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

In most peer‐to‐peer (P2P) networks, peers are placed randomly or based on their geographical position, which can lead to a performance bottleneck. This problem be solved by using peer clustering algorithms. this paper, the significant results of paper described in following sentences. We propose two innovative swarm‐based metaheuristics for clustering, slime mold and K‐means. They competitively benchmarked, evaluated, compared nine well‐known conventional algorithms: artificial bee colony (ABC), ABC combined with K‐means, ant‐based ant fuzzy C‐means, genetic hierarchical particle swarm optimization (PSO). The benchmarks cover parameter sensitivity analysis comparative made 5 different metrics: execution time, Davies–Bouldin index (DBI), Dunn (DI), silhouette coefficient (SC), averaged dissimilarity (ADC). Furthermore, statistical is performed order validate obtained results. Slime K‐means outperform all other swarm‐inspired algorithms terms time quality solution.

Language: Английский

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High-order rotor Hopfield neural networks for associative memory

Neurocomputing, Journal Year: 2024, Volume and Issue: unknown, P. 128893 - 128893

Published: Nov. 1, 2024

Language: Английский

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