TL;DR
This work presents an enhanced multi-objective dung beetle optimization algorithm that is implemented to tackle the multi-objective path planning optimization problem for mobile robot, and numerical simulation results verify that the method achieves sound performance in resolving practical engineering issues.
The proposed Multi-Objective Narwhal Optimizer (MONO), a Pareto-based extension of the recently developed Narwhal Optimizer, incorporates Pareto dominance, external archive management, adaptive multi-leader guidance, and crowding-distance-based diversity preservation to effectively balance convergence and exploration t...
Experimental results show that the proposed improved dung beetle optimization (IDBO) algorithm, which integrates multiple coordinated mechanisms to enhance the original dung beetle optimizer, can serve as a competitive optimizer for numerical benchmark problems and offline static 3D UAV path-planning simulations.
Multi-objective optimization plays an important role in modern design and complex engineering applications. However, achieving an effective balance between the convergence and diversity of Pareto-optimal solutions remains challenging. This paper proposes a Sobol-driven Multi-objective Whale Migration Algorithm (SMOWMA)...
Traditional artificial bee colony (ABC) algorithms have common shortcomings in solving multi-objective optimization problems, such as limited search ability, susceptibility to getting stuck in local optima, low solution accuracy, and premature convergence. This study innovatively proposes two ABC optimization algorithm...
Experimental results show that the proposed MOSGO provides, in most problems, significantly better convergence near the true Pareto front, with improved diversity and spread of solutions, compared to other multi-objective algorithms.
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