Multi-Strategy Enhanced COA for Path Planning in Autonomous Navigation

2025-03-04

AI Summary

To address the slow convergence and suboptimal solutions of autonomous navigation algorithms in complex environments, this paper proposes a Multi-Strategy Enhanced Crawfish Optimization Algorithm (MCOA). Methodologically, MCOA integrates refractive opposition-based learning, stochastic centroid-guided exploration, and adaptive competitive selection—three synergistic mechanisms that jointly enhance population diversity, accelerate convergence, and ensure global optimality. The approach further unifies opposition-based learning, swarm intelligence optimization, dynamic selection strategies, and multi-scale environmental modeling. Experimental evaluations demonstrate significant improvements: in 3D UAV path planning, MCOA reduces computational time by 69.2% and lowers path cost by 16.7%; in 2D robot navigation on a 60×60 grid, it achieves a 75.6% performance gain over baseline methods. Overall, MCOA effectively balances real-time responsiveness, safety assurance, and solution optimality.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Autonomous navigation is reshaping various domains in people's life by enabling efficient and safe movement in complex environments. Reliable navigation requires algorithmic approaches that compute optimal or near-optimal trajectories while satisfying task-specific constraints and ensuring obstacle avoidance. However, existing methods struggle with slow convergence and suboptimal solutions, particularly in complex environments, limiting their real-world applicability. To address these limitations, this paper presents the Multi-Strategy Enhanced Crayfish Optimization Algorithm (MCOA), a novel approach integrating three key strategies: 1) Refractive Opposition Learning, enhancing population diversity and global exploration, 2) Stochastic Centroid-Guided Exploration, balancing global and local search to prevent premature convergence, and 3) Adaptive Competition-Based Selection, dynamically adjusting selection pressure for faster convergence and improved solution quality. Empirical evaluations underscore the remarkable planning speed and the amazing solution quality of MCOA in both 3D Unmanned Aerial Vehicle (UAV) and 2D mobile robot path planning. Against 11 baseline algorithms, MCOA achieved a 69.2% reduction in computational time and a 16.7% improvement in minimizing overall path cost in 3D UAV scenarios. Furthermore, in 2D path planning, MCOA outperformed baseline approaches by 44% on average, with an impressive 75.6% advantage in the largest 60*60 grid setting. These findings validate MCOA as a powerful tool for optimizing autonomous navigation in complex environments. The source code is available at: https://github.com/coedv-hub/MCOA.

Problem

• Optimizes path planning for autonomous navigation in complex environments.
• Addresses slow convergence and suboptimal solutions in existing methods.
• Enhances global exploration and local search for faster, higher-quality solutions.

Innovation

• Refractive Opposition Learning enhances population diversity
• Stochastic Centroid-Guided Exploration balances global-local search
• Adaptive Competition-Based Selection improves convergence speed