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An efficient multi-objective parrot optimizer for global and engineering optimization problems.

Mohammed R Saad1, Marwa M Emam2, Essam H Houssein3,4

  • 1Faculty of Computers and Information, Luxor University, Luxor, Egypt.

Scientific Reports
|February 11, 2025
PubMed
Summary
This summary is machine-generated.

The new Multi-Objective Parrot Optimizer (MOPO) effectively solves complex multi-objective optimization problems. It outperforms existing algorithms on benchmark and engineering tasks, demonstrating its robust global search capabilities.

Keywords:
Multi-objective optimization techniquesMulti-objective parrot optimizerPareto optimal solutionsParrot optimizer

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Evolutionary Computation

Background:

  • Single-objective optimization algorithms like the Parrot Optimizer (PO) show strong global search capabilities.
  • Multi-objective optimization (MOO) problems require algorithms that can find a set of optimal trade-off solutions (Pareto optimal solutions).

Purpose of the Study:

  • To extend the Parrot Optimizer (PO) into a Multi-Objective Parrot Optimizer (MOPO) for tackling MOO problems.
  • To evaluate MOPO's performance on standard benchmark suites and real-world engineering challenges.

Main Methods:

  • MOPO incorporates an outward archive to maintain Pareto optimal solutions, inspired by parrot behavior.
  • Performance was assessed using the CEC'2020 multi-objective benchmark suite, engineering design challenges, and helical coil spring optimization.
  • Comparative analysis was conducted against seven state-of-the-art algorithms: IMOMRFO, MOGTO, MOGWO, MOWOA, MOSMA, MOPSO, and NSGA-II.

Main Results:

  • MOPO demonstrated superior performance across multiple metrics, including PSP, IGDX, HV, GD, spacing, and maximum spread.
  • The algorithm proved effective on constrained engineering design problems and real-world automotive applications.
  • MOPO consistently outperformed the compared state-of-the-art algorithms.

Conclusions:

  • MOPO is a robust and effective algorithm for addressing complex multi-objective optimization problems.
  • Its ability to preserve Pareto optimal solutions and strong search capabilities make it suitable for practical applications.
  • MOPO represents a significant advancement in multi-objective optimization techniques.