Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 89, P. 101624 - 101624
Published: June 5, 2024
Language: Английский
Citations
1Published: March 26, 2024
Stagnation at local optima represents a significant challenge in bio–inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing hybrid model that combines the Orca Predator Algorithm with Deep Q–Learning. is an technique mimics hunting behavior of orcas. It solves complex problems exploring and exploiting search spaces efficiently. Q–Learning reinforcement learning deep neural networks. integration aims turn stagnation problem into opportunity for more focused effective exploitation, enhancing technique’s performance accuracy. The proposed leverages biomimetic strengths identify promising regions nearby space, complemented fine–tuning capabilities navigate these areas precisely. practical application approach evaluated using high–dimensional Heartbeat Categorization Dataset, focusing on feature selection problem. dataset, comprising electrocardiogram signals, provided robust platform testing our model. Our experimental results are encouraging, showcasing strategy capability relevant features without significantly compromising metrics machine models. analysis was performed comparing improved method against its native version set state–of–the–art algorithms.
Language: Английский
Citations
0Published: Jan. 1, 2024
Language: Английский
Citations
0Emergence, complexity and computation, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 152
Published: Jan. 1, 2024
Language: Английский
Citations
0Published: Dec. 25, 2024
Language: Английский
Citations
0