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Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization/Maximization.

Mohammad Dehghani1, Pavel Trojovský2

  • 1Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 71557-13876, Iran.

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|July 20, 2021
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Summary
This summary is machine-generated.

A new Teamwork Optimization Algorithm (TOA) simulates teamwork to solve complex problems. TOA demonstrates superior performance compared to existing optimization algorithms.

Keywords:
optimizationoptimization algorithmoptimization problempopulation-basedteamwork

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Metaheuristics

Background:

  • Population-based optimization algorithms are widely used for solving complex problems.
  • Existing algorithms face challenges in efficiency and effectiveness for certain optimization tasks.

Purpose of the Study:

  • To introduce a novel population-based optimization algorithm, the Teamwork Optimization Algorithm (TOA).
  • To mathematically model TOA for practical application in solving optimization problems.
  • To evaluate TOA's performance against established algorithms.

Main Methods:

  • The Teamwork Optimization Algorithm (TOA) is developed, inspired by human teamwork dynamics.
  • TOA is mathematically formulated to enable its application to optimization problems.
  • The algorithm's efficacy is tested on twenty-three standard objective functions.

Main Results:

  • The Teamwork Optimization Algorithm (TOA) effectively solves a diverse set of optimization problems.
  • Comparative analysis shows TOA significantly outperforms eight well-known optimization algorithms.
  • TOA provides competitive quasi-optimal solutions, demonstrating its practical utility.

Conclusions:

  • The proposed Teamwork Optimization Algorithm (TOA) is a capable and competitive method for addressing optimization challenges.
  • TOA's simulation of teamwork provides a novel and effective approach to algorithm design.
  • The algorithm shows significant promise for future applications in various optimization domains.