ArticleBiomimetics (Basel, Switzerland)2023
An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning.
Article in Biomimetics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Hybrid optimization algorithm for solving path planning problems based on grey wolf optimization algorithm.Scientific reports · 2026Article
- An improved sparrow search algorithm and its application in the deployment of 3D wireless sensor nodes.Scientific reports · 2025Article
- Grid-Based Path Planning of Agricultural Robots Driven by Multi-Strategy Collaborative Evolution Honey Badger Algorithm.Biomimetics (Basel, Switzerland) · 2025Article
- Adaptive mechanism-based grey wolf optimizer for feature selection in high-dimensional classification.PloS one · 2025Article
- An improved beetle antennae search algorithm and its application in coverage of wireless sensor networks.Scientific reports · 2024Article
- A Survey on Biomimetic and Intelligent Algorithms with Applications.Biomimetics (Basel, Switzerland) · 2024Article
- A Grey Wolf Optimizer Algorithm for Multi-Objective Cumulative Capacitated Vehicle Routing Problem Considering Operation Time.Biomimetics (Basel, Switzerland) · 2024Article
- An Improved Harris Hawks Optimization Algorithm and Its Application in Grid Map Path Planning.Biomimetics (Basel, Switzerland) · 2023Article
- Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems.Biomimetics (Basel, Switzerland) · 2023Article
- Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model.Biomimetics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This paper discusses a hybrid grey wolf optimizer utilizing a clone selection algorithm (pGWO-CSA) to overcome the disadvantages of a standard grey wolf optimizer (GWO), such as slow convergence speed, low accuracy in the single-peak function, and easily falling into local optimum in the multi-peak function and complex problems. The modifications of the proposed pGWO-CSA could be classified into the following three aspects. Firstly, a nonlinear function is used instead of a linear function for adjusting the iterative attenuation of the convergence factor to balance exploitation and exploration automatically. Then, an optimal
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Registered trials
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