In recent years, deep neural networks have shown superior performance in various fields,
but interpretability has always been the Achilles' heel of deep neural networks. The existing …

Chaotic maps have found applications in a wide range of fields. However, many existing one-
dimensional (1-D) chaotic maps suffer from limitations. Their smaller chaotic regions restrict …

Recent years have witnessed a surge in the development of multilevel variants of differential
evolution (DE), significantly enhancing the performance of DE algorithms. However …

In order to break through the speed bottleneck, researchers envision using tubes to cover
high-speed maglev trains and extract some of the air inside the tubes, creating a low-density …

Accurate estimation of SOC is crucial for the safe and efficient utilization of electric vehicles,
portable devices and renewable energy systems. Neural networks have become a powerful …

We investigate both classical and quantum chaotic dynamics of a two-mode Bose-Einstein
condensate driven by a continuously periodic field. We calculate the quantities, including …

Passive buildings employ the principles of active optimization and passive priority to
demonstrate their energy-saving, comfortable, and cost-effective qualities, thereby …

The Snow Ablation Optimizer (SAO) algorithm represents a new intelligent search method
founded on the sublimation and melting phenomena of snow. It incorporates a dual …

Differential evolution (DE) is a long-standing methodology for resolving intricate optimization
concerns. LSHADE is an effective variation of DE. It has been successful in numerous …

Single-objective optimization, especially with con-straints, is the most common class of
problems in biology, society, and energy. Among various optimization algorithms, swarm …