An ensemble approach to meta-heuristic algorithms: Comparative analysis and its applications
- Publisher:
- Elsevier BV
- Publication Type:
- Journal Article
- Citation:
- Computers and Industrial Engineering, 2021, 162, pp. 107739
- Issue Date:
- 2021-12-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S0360835221006434-main.pdf | Published version | 4.5 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
In this paper, we intend to propose an ensemble optimization algorithm based on Follow The Leader (FTL), Multi-verse Optimizer (MVO), and Salp Swarm Algorithm (SSA) to solve constrained optimization problems. The FTL, MVO, and SSA are swarm-based algorithms that update their particle position using a selection approach. Less number of control parameters and a common selection approach make these algorithms suitable for hybridization. In this work, combinations of FTL, MVO, and SSA algorithms such as FTL_MVO, FTL_SSA, MVO_SSA, and FTL_MVO_SSA have been proposed to solve different optimization problems. The proposed ensemble optimization algorithms have been compared with base optimization algorithms on forty-eight unimodal and multimodal benchmark functions. The ensemble model has achieved significant performance improvement over base FTL, MVO, and SSA. Moreover, these algorithms have been tested on six well-known constrained optimization problems to benchmark their performance over real-world applications. Finally, the comparison with classical optimization algorithms reveals the efficacy of the proposed models.
Please use this identifier to cite or link to this item: