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Enhanced gorilla troops optimizer powered by marine predator algorithm: global optimization and engineering design.

Mohamed H Hassan1, Salah Kamel1, Ali Wagdy Mohamed2

  • 1Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt.

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Summary
This summary is machine-generated.

The Enhanced Gorilla Troops Optimizer (EGTO) improves metaheuristic search by integrating Marine Predators Algorithm (MPA) principles, offering superior performance and stability for complex optimization problems.

Keywords:
CEC2019Gorilla troops optimizerMarine predator algorithmSwarm intelligence

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • Existing metaheuristic algorithms like the Gorilla Troops Optimizer (GTO) struggle with convergence accuracy and stability in complex optimization tasks.
  • Advanced optimization techniques are needed to overcome the limitations of traditional GTO.
  • The Marine Predators Algorithm (MPA) offers insights into enhancing search capabilities.

Purpose of the Study:

  • To introduce an enhanced metaheuristic algorithm, the Enhanced Gorilla Troops Optimizer (EGTO).
  • To improve the convergence accuracy and stability of the Gorilla Troops Optimizer (GTO).
  • To balance exploration and exploitation phases in optimization problems effectively.

Main Methods:

  • Developed the Enhanced Gorilla Troops Optimizer (EGTO) by integrating high and low-velocity ratios from the Marine Predators Algorithm (MPA) into the Gorilla Troops Optimizer (GTO).
  • Evaluated EGTO on 23 standard benchmark functions and 10 complex functions from the CEC2019 benchmark suite.
  • Tested EGTO on seven engineering design problems: three-bar truss, compression spring, pressure vessel, cantilever beam, welded beam, speed reducer, and gear train designs.

Main Results:

  • EGTO demonstrated superior performance compared to established algorithms including Tuna Swarm Optimization, Grey Wolf Optimizer, Gradient Based Optimizer, Artificial Rabbits Optimization Algorithm, Pelican Optimization Algorithm, and the original GTO.
  • The algorithm achieved robust convergence rates and effectively located local/global optima across diverse test functions.
  • EGTO showed significant efficacy in solving complex engineering design problems.

Conclusions:

  • The Enhanced Gorilla Troops Optimizer (EGTO) provides a powerful and efficient metaheuristic approach for complex optimization.
  • EGTO effectively balances exploration and exploitation with fewer parameters and operations.
  • The proposed EGTO algorithm outperforms existing methods in both benchmark testing and engineering applications.