A New Adaptive Cellular Genetic Algorithm for Mixed Variable Optimization Problems | ||||
| IJCI. International Journal of Computers and Information | ||||
| Article 5, Volume 11, Issue 1, January 2024, Page 44-61 PDF (707.91 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijci.2023.222908.1115 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Alaa Fahim  
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| Assiut University | ||||
| Abstract | ||||
| The cellular genetic algorithm (CGA) improve genetic algorithm for subclass(GA) with a dispersed population where an exchange individuals is limited to close neighbors. In this research, the adaptive cellular genetic algorithm (ACG) applicable to make better exploration in the search space. The ACG algorithm assists the best solutions to reach the optimal solution faster. Various problems regarding mixed variable optimization (MVO) problems arise in numerous models and real applications. We applied our the ACG algorithm to solve the MVO problem, which is called as the adaptive cellular genetic mixed variable ACGMV method. The performance of the ACGMV algorithm is tested on a set of several benchmark test problems. The experimental findings indicate that ACGMV algorithm performs better than other methods. In addition, we applied ACGMV to solve the hard data-clustering problem. which is called ACGMV-HC. The mixed-variable programming approach is used to formulate the data clustering problem. Based on our result, the ACGMV-HC algorithm is more effective than other methods | ||||
| Keywords | ||||
| Mixed variable optimization; Cellular genetic algorithm; hard cluster | ||||
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