ArticleComputational intelligence and neuroscience2022
Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Depression detection from multimodalities based on LeNet with hunter-geese optimization.Scientific reports · 2026Article
- An Enhanced Black-Winged Kite Algorithm with Multiple Strategies for Global Optimization and Constrained Engineering Applications.Biomimetics (Basel, Switzerland) · 2026Article
- A Hybrid Nonlinear Greater Cane Rat Algorithm with Sine-Cosine Algorithm for Global Optimization and Constrained Engineering Applications.Biomimetics (Basel, Switzerland) · 2025Article
- Novel Greylag Goose Optimization Algorithm with Evolutionary Game Theory (EGGO).Biomimetics (Basel, Switzerland) · 2025Article
- An innovative complex-valued encoding black-winged kite algorithm for global optimization.Scientific reports · 2025Article
- Nature-Inspired Approach: A Novel Rat Optimization Algorithm for Global Optimization.Biomimetics (Basel, Switzerland) · 2024Article
- A Hybrid Nonlinear Whale Optimization Algorithm with Sine Cosine for Global Optimization.Biomimetics (Basel, Switzerland) · 2024Article
- CGJO: a novel complex-valued encoding golden jackal optimization.Scientific reports · 2024Article
- PECSO: An Improved Chicken Swarm Optimization Algorithm with Performance-Enhanced Strategy and Its Application.Biomimetics (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
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Abstract
This paper proposes a new meta-heuristic algorithm, named wild geese migration optimization (GMO) algorithm. It is inspired by the social behavior of wild geese swarming in nature. They maintain a special formation for long-distance migration in small groups for survival and reproduction. The mathematical model is established based on these social behaviors to solve optimization problems. Meanwhile, the performance of the GMO algorithm is tested on the stable benchmark function of CEC2017, and its potential for dealing with practical problems is studied in five engineering design problems and the inverse kinematics solution of robot. The test results show that the GMO algorithm has excellent computational performance compared to other algorithms. The practical application results show that the GMO algorithm has strong applicability, more accurate optimization results, and more competitiveness in challenging problems with unknown search space, compared with well-known algorithms in the literature. The proposal of GMO algorithm enriches the team of swarm intelligence optimization algorithms and also provides a new solution for solving engineering design problems and inverse kinematics of robots.
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