Titlebook: Advances in Swarm Intelligence; 15th International C Ying Tan,Yuhui Shi Conference proceedings 2024 The Editor(s) (if applicable) and The A

單片眼鏡

Strategisches Kompetenz-Managementctively, this paper designs a modified variable velocity strategy particle swarm optimization algorithm. The algorithm incorporates whale encircling and flipping, along with an inertia weight updating strategy for random perturbation, known as WETVVS-MOPSO. The results show that WETVVS-MOPSO significantly outperforms its competitors.

Extricate

時間等

以煙熏消毒

Focus-Words

Gründungsintention von Akademikernof the algorithm on 8 benchmark functions. Experimental results demonstrate that our improved fusion strategy has superior comprehensive performance over advanced optimization algorithms such as the Artificial Rabbit optimization, implying evident superiority and application potential of our strategy.

refraction

不滿分子

Multi-strategy Enhanced Particle Swarm Optimization Algorithm for?Elevator Group Schedulingloyed to search the nearby solution spaces to further avoid local optimum. Simulation results have demonstrated that the proposed algorithm can achieve a shorter passenger waiting time than traditional particle swarm optimization.

態(tài)度暖昧

Convolutional Neural Network Architecture Design Using an Improved Surrogate-Assisted Particle Swarmerify the proposed algorithm and compare it with some mainstream network structures and processes. Experimental results show that the classification accuracy of the proposed algorithm is equivalent to or even better than similar algorithms and consumes fewer computing resources.

跟隨

遠足

Multi-strategy Integration Model Based on?Black-Winged Kite Algorithm and?Artificial Rabbit Optimizaof the algorithm on 8 benchmark functions. Experimental results demonstrate that our improved fusion strategy has superior comprehensive performance over advanced optimization algorithms such as the Artificial Rabbit optimization, implying evident superiority and application potential of our strategy.