Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Xiankun Zhang,Wei Chen Conference proceedi

眼界

,Einführung in die Gründungsfinanzierung, effectively reduce the energy consumption of the solution. The performance of SLHH is tested against three state-of-the-art algorithms in 20 benchmark instances. The experiment results demonstrate the effectiveness of SLHH in addressing the multifaceted challenges of EDFJSP.

飲料

https://doi.org/10.1007/978-3-662-07583-8ing information from dominant individuals to improve the distribution of the population. Thirty instances of different scales are utilized to evaluate the effectiveness of the RLEDA. The experimental results show that the RLEDA outperforms the comparison algorithms in solving energy-efficient DHFJSP.

高腳酒杯

Dynamic Search Hybrid Fireworks Algorithmegy enhances the information exchange between fireworks and improves the convergence performance of the algorithm. Tests were conducted on the CEC2017 benchmark suite, and the experimental results show that DHFWA significantly outperforms previous fireworks algorithms.

Rotator-Cuff

A Self-learning Hyper-Heuristic Algorithm for Energy-Efficient Distributed Flexible Job Shop Schedul effectively reduce the energy consumption of the solution. The performance of SLHH is tested against three state-of-the-art algorithms in 20 benchmark instances. The experiment results demonstrate the effectiveness of SLHH in addressing the multifaceted challenges of EDFJSP.

peptic-ulcer

Reinforcement Learning-Based Estimation of Distribution Algorithm for Energy-Efficient Distributed Hing information from dominant individuals to improve the distribution of the population. Thirty instances of different scales are utilized to evaluate the effectiveness of the RLEDA. The experimental results show that the RLEDA outperforms the comparison algorithms in solving energy-efficient DHFJSP.

engender

DRILL

Die Rossby-Zahl-?hnlichkeitstheorie introduce a new fitness factor promoting knowledge transfer under particle guidance, preventing premature convergence to global optima. Results demonstrate that this method efficiently obtains high-precision feature subsets.

安慰

incite

Guided Particle Adaptation PSO for Feature Selection on High-dimensional Classification introduce a new fitness factor promoting knowledge transfer under particle guidance, preventing premature convergence to global optima. Results demonstrate that this method efficiently obtains high-precision feature subsets.

橢圓

A Double Deep Q Network Guided Online Learning Differential Evolution Algorithmomplexity and boost learning efficiency. Finally, an adaptive optimization operator is designed to select a suitable mutation strategy for the different search processes. The experimental results reveal that the proposed algorithm is superior to comparison algorithms on CEC 2017 real-parameter numerical optimization.