A random elite ensemble learning swarm optimizer for high-dimensional optimization

• DOI: 10.1007/s40747-023-00993-w
• Publication Date: 2023-03-23T04:27:23Z
• Authors: Qiang Yang; Gong-Wei Song; Xu-Dong Gao; Zhen-Yu Lu; Sang-Woon Jeon; Jun Zhang
• Keywords: Elite; Swarm intelligence

Abstract High-dimensional optimization problems are increasingly pervasive in real-world applications nowadays and become harder to optimize due interacting variables. To tackle such effectively, this paper designs a random elite ensemble learning swarm optimizer (REELSO) by taking inspiration from human observational theory. First, partitions particles the current into two exclusive groups: group consisting of top best non-elite containing rest based on their fitness values. Next, it employs build neighbors for each particle form positive environment observe. Subsequently, is updated cognitively among collectively all elites environment. For one thing, directed superior ones, thus convergence could be guaranteed. another, randomly formed particle, hence high diversity maintained. Finally, further devises dynamic partition strategy divide groups dynamically during evolution, so that gradually changes exploring immense solution space exploiting found optimal areas without serious loss. With above mechanisms, devised REELSO expected explore search exploit properly. Abundant experiments popularly used high-dimensional benchmark sets prove performs competitively with or even significantly outperforms several state-of-the-art approaches designed optimization.