.png){kind=icon}
.png){kind=icon}
Return
Multi-Swarm Dynamic Crow Search Algorithm for Dynamic Multi-Objective Optimization
.png){kind=icon}
PRE
AI
DOI：10.1002/tee.70165.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Dynamic multi-objective optimization problems (DMOPs) are one of the most challenging problems in real-world systems. This paper proposes a multi-swarm dynamic crow search algorithm (CSA) to solve DMOPs effectively and advance the application of CSA for DMOPs. Three components are introduced in the algorithm. The multi-swarm co-evolution mechanism creates a distinct swarm for each optimization objective, while a memory time-based archive update strategy is introduced. A complex behavior strategy is developed to adaptively adjust the key parameters and guide the swarms for fast convergence. The dynamism handling mechanism uses random re-evaluation for change detection, proposes a split selection method, and a memory reuse strategy to choose old solutions with good diversity, and considers random re-initialization and prediction-based approaches to respond to the change. Extensive experiments demonstrate that the proposed algorithm is competitive in both optimization performance and computational cost when compared with state-of-the-art methods. (c) 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
Keywords:
dynamic multi-objective optimization
crow search algorithm
multi-swarm
change response
Journal
IF:
1.1
Papers:
244
Citations:
1.8K