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A new hybrid algorithm to solve bound-constrained nonlinear optimization problems

Duary, A ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1007/s00521-019-04696-7
  3. Publisher: Springer , 2020
  4. Abstract:
  5. The goal of this work is to propose a hybrid algorithm called real-coded self-organizing migrating genetic algorithm by combining real-coded genetic algorithm (RCGA) and self-organizing migrating algorithm (SOMA) for solving bound-constrained nonlinear optimization problems having multimodal continuous functions. In RCGA, exponential ranking selection, whole-arithmetic crossover and non-uniform mutation operations have been used as different operators where as in SOMA, a modification has been done. The performance of the proposed hybrid algorithm has been tested by solving a set of benchmark optimization problems taken from the existing literature. Then, the simulated results have been compared numerically and graphically with existing algorithms. In the graphical comparison, a modified performance index has been proposed. Finally, the proposed algorithm has been applied to solve two real-life problems. © 2020, Springer-Verlag London Ltd., part of Springer Nature
  6. Keywords:
  7. Genetic algorithm ; Global optimization ; Multimodal continuous function ; Performance index ; Self-organizing migrating algorithm ; Benchmarking ; Functions ; Genetic algorithms ; Global optimization ; Constrained non-linear optimizations ; Graphical comparison ; Multimodal continuous functions ; Optimization problems ; Performance indices ; Real coded genetic algorithm ; Constrained optimization
  8. Source: Neural Computing and Applications ; Volume 32, Issue 16 , 2020 , Pages 12427-12452
  9. URL: https://link.springer.com/article/10.1007/s00521-019-04696-7