ENHANCING GLOBAL OPTIMIZATION WITH AMECO: ADAPTIVE MEMORY ENHANCED COOT OPTIMIZER | ||||
| ERJ. Engineering Research Journal | ||||
| Articles in Press, Accepted Manuscript, Available Online from 20 May 2025 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/erjm.2025.354157.1378 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Mai A. Farag   ; Rizk M. Rizk-Allah; Kholoud A. Gaber
| ||||
| Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom, Egypt | ||||
| Abstract | ||||
| ABSTRACT The COOT optimization algorithm is a biologically-inspired technique recognized for its global optimization capability, simplicity in control parameters, and ease of implementation. Despite its effectiveness in global optimization, COOT often gets stuck in a local optimum and experiences premature convergence caused by insufficient population diversity. To overcome these limitations, we introduce a hybrid algorithm called the Adaptive Memory Enhanced Coot Optimizer (AMECO). This new algorithm merges COOT with a crossover operator and utilizes an archive memory mechanism, also known as a memory pool, to enhance exploration and prevent convergence to local minima. AMECO leverages the global optimization strengths of Genetic Algorithms (GA) along with COOT's rapid convergence by generating new leader positions through the crossover of leaders selected via tournament selection within the COOT population. This approach optimizes the exploration-exploitation balance, resulting in better solution quality. AMECO's performance was assessed using 13 numerical benchmark functions with 100 dimensions and 5 mechanical engineering optimization problems, showing superior accuracy and robustness compared to other algorithms in most cases. | ||||
| Keywords | ||||
| Optimization; COOT algorithm; Mechanical engineering optimization problems | ||||
|
Statistics Article View: 110 |
||||
