Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Abstract

The Dung Beetle Optimization (DBO) algorithm is a powerful metaheuristic algorithm that is widely used for optimization problems. However, the DBO algorithm has limitations in balancing global exploration and local exploitation capabilities, often leading to getting stuck in local optima. To overcome these limitations and address global optimization problems, this study introduces the Multi-Strategy and Improved DBO (MSIDBO) Algorithm. The MSIDBO algorithm incorporates several advanced computational techniques to enhance its performance. Firstly, it introduces a random reverse learning strategy to improve population diversity and mitigate early convergence or local stagnation issues present in the DBO algorithm. Additionally, a fitness-distance balancing strategy is employed to better manage the trade-off between diversity and convergence within the population. Furthermore, the algorithm utilizes a spiral foraging strategy to enhance precision, promote strong exploratory capabilities, and prevent being trapped in local optima. To further enhance the global search ability and particle utilization of the MSIDBO algorithm, it combines the Optimal Dimension-Wise Gaussian Mutation strategy. By minimizing premature convergence, population diversity is increased, and the convergence of the algorithm is accelerated. This expansion of the search space reduces the likelihood of being trapped in local optima during the evolutionary process. To demonstrate the effectiveness of the MSIDBO algorithm, extensive experiments are conducted using benchmark test functions, comparing its performance against other well-known metaheuristic algorithms. The results highlight the feasibility and superiority of MSIDBO in solving optimization problems. Moreover, the MSIDBO algorithm is applied to path planning simulation experiments to showcase its practical application potential. A comparison with the DBO algorithm shows that MSIDBO generates shorter and faster paths, effectively addressing real-world application problems.

Improved Dung Beetle Optimizer Algorithm With Multi-Strategy for Global Optimization and UAV 3D Path Planning

“…Longhai Li and associates introduced a fitness-distance balance strategy and implemented a spiral foraging approach to refine the algorithm's search precision, expand its exploratory ability, and circumvent local optima. By integrating an optimal dimension Gaussian mutation strategy, they increased population diversity and hastened the algorithm's convergence speed [42]. Concurrently, Xu-ruo Wei and others merged the Simulated Annealing (SA) algorithm with the DBO algorithm to diminish the likelihood of converging to local extremes [43].…”

Enhanced Dung Beetle Optimization Algorithm for Practical Engineering Optimization

“…Although Xun proved the superiority of the DBO algorithm, it still has shortcomings, such as easily falling into local optima and poor global search ability. Therefore, many scholars have improved the DBO accordingly [16][17][18]. Fang et al suggested a DBO algorithm that merges quantum computing with multi-strategy (QHDBO), designed to prevent the algorithm from hitting local peaks through the t-distribution mutation approach in quantum computing, enhancing its convergence speed and optimization precision [19].…”

“…Li et al proposed a multi-strategy-improved DBO algorithm (MSIDBO) to shorten the path planning of mobile robots. Simulation experiments show that the MSIDBO algorithm effectively solves problems in practical applications [17]. Zhang and colleagues propose the development of a DBO algorithm, utilizing the Extreme Learning Machine (ELM) and Adaptive Spiral (ASDBO) to improve the predictive precision of photovoltaic (PV) power production.…”

An Enhanced Tree-Seed Algorithm for Function Optimization and Production Optimization

“…The algorithm is also used to implement a late-train scheduling method for railroad trains to effectively eliminate the effects of late trains. Li et al [ 12 ] improved the dung-beetle optimization and proposed a new improved evolutionary algorithm. The algorithm introduces a stochastic inverse learning strategy to improve population diversity and mitigate the problem of early convergence or local stagnation present in the algorithm.…”