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Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot.

Honggang Wu1, Xinming Zhang1,2, Linsen Song1

  • 1School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Computational Intelligence and Neuroscience
|November 7, 2022
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Summary

A new wild geese migration optimization (GMO) algorithm, inspired by goose behavior, offers superior computational performance. This novel meta-heuristic algorithm demonstrates strong applicability and accuracy for complex engineering and robotics challenges.

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Area of Science:

  • Computational Intelligence
  • Swarm Intelligence
  • Optimization Algorithms

Background:

  • Nature-inspired algorithms are crucial for solving complex optimization problems.
  • Existing algorithms may lack efficiency in certain challenging scenarios.
  • Understanding social behaviors can lead to novel computational approaches.

Purpose of the Study:

  • To introduce a new meta-heuristic algorithm, the wild geese migration optimization (GMO) algorithm.
  • To model the social behavior of wild geese for optimization.
  • To evaluate the GMO algorithm's performance on benchmark and real-world problems.

Main Methods:

  • Development of a mathematical model based on wild geese social and migration behaviors.
  • Testing the GMO algorithm on the CEC2017 benchmark function suite.
  • Application of the GMO algorithm to engineering design problems and robot inverse kinematics.

Main Results:

  • The GMO algorithm demonstrated excellent computational performance compared to existing algorithms.
  • It showed strong applicability and accuracy in solving engineering design problems.
  • The algorithm proved competitive in handling challenging problems with unknown search spaces.

Conclusions:

  • The wild geese migration optimization algorithm is a promising addition to swarm intelligence.
  • GMO offers a novel and effective solution for engineering design and robot kinematics.
  • The algorithm shows significant potential for tackling complex, real-world optimization tasks.