| Abstract: |
| Multi-disciplinary engineering applications are widespread in numerous research disciplines. Lots of optimizers have been proposed in the literature to address these types of problems. However, the optimizer`s performance considerably decreases with the growth in the complexity and other scale issues. Huge ranges of optimizers have been proposed to address the complex engineering application in the last few decades. Present study deals with enhancement strategies of Artificial Gorilla Troops Optimizer (GTO) based on divers` gorilla`s groups. These techniques are named as AGTO, AGT-1 and AGTO-2. The objective is to improve the algorithm by avoiding the function from trapping in local minima and premature convergence in dealing with higher dimensional functions. To evaluate the superiority of the proposed strategies, 41 CEC standard test suites and six high dimensional multi-disciplinary engineering applications have been considered and its performance have been tested with the results obtained with various state- of -art- optimizers in terms of faster convergence rate and escaping in local minima etc. Simulated results establishes that splitting gorillas in groups and permitting them to have distinct separate and social intelligence can enhance the superiority of GTO significantly. |
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