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 throughout the search process.
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), which extends the Whale Migration Algorithm within a non-dominated sorting and elite-selection framework. A maximin scrambled Sobol initialization scheme is first employed to improve the distribution of the initial population. An archive-guided adaptive Student-t flight mechanism is then incorporated into the leader-whale position update to dynamically balance global exploration and local exploitation. In addition, archive crowding information and archive-entry success feedback are jointly used to adjust the search behavior according to both environmental diversity and recent search performance. SMOWMA is evaluated on five widely used multi-objective benchmark suites, namely ZDT, DTLZ, WFG, UF, and CF, using four performance indicators: generational distance (GD), inverted generational distance (IGD), spacing (SP), and hypervolume (HV). The results, together with Friedman tests and Holm-adjusted Wilcoxon tests, demonstrate that SMOWMA achieves competitive overall performance in terms of convergence, diversity, and objective-space coverage, although its relative advantage remains problem-dependent. The practical applicability of SMOWMA is further examined using multi-objective welded-beam design formulations, a bi-objective four-bar truss design problem, and a five-objective car side-impact design problem. The engineering results show that SMOWMA can obtain competitive and stable approximation sets for constrained design problems with different numbers of objectives, supporting its effectiveness and applicability in multi-objective engineering optimization.
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 algorithms: the multi-objective optimization ABC algorithm based on dynamic weight control, and the multi-objective optimization dual-file ABC algorithm based on average cognitive strategy. The former introduces dynamic domain search and adaptive weight factor allocation at the mechanism level to accelerate convergence and suppress premature convergence. The latter enhances coverage balance on complex frontiers through average cognitive position guidance and coordinated updating of elite/individual dual-file. Compared with other multi-objective bee colony algorithms, the research algorithm performed the best. In the dual-objective ZDT1, ZDT2, and ZDT3 testing functions, the standard deviations were only 4.669 × 10−5, 6.254 × 10−5, and 1.625 × 10−4, respectively, demonstrating high stability and accuracy. For the DTLZ series testing functions, the research algorithm had good convergence effect and good solution set distribution. In addition, the complex UF function testing revealed that the research algorithm was more efficient in solving multi-objective problems and had a higher number of optimal solutions. The research algorithm has significant advantages in multi-objective optimization problems, not only improving the accuracy and stability of the solution, but also enhancing the distribution of the solution set. This method provides an effective solution for optimizing complex systems. The successful application of these algorithms is expected to exert a more critical role in the field of intelligent optimization.
Many real-world optimization problems involve conflicting objectives that need to be minimized to reduce cost and/or maximized to increase profit. In this study, a multi-objective social group optimization (MOSGO) is proposed and implemented to solve multi-objective problems and find approximated solutions to the optimal Pareto front. A new mechanism, the dynamic fitness function, is introduced and integrated with non-dominated sorting and crowding distance elimination strategies to enhance the quality of the non-dominated solutions. The dynamic fitness function is designed to select the best solution for each objective at each iteration. Non-dominated sorting is used to dismiss weak solutions, and crowding distance elimination is deployed to achieve the best solution diversity. The suggested algorithm is compared with four competitive algorithms: the multi-objective artificial hummingbird algorithm (MOAHA), the multi-objective particle swarm optimization (MOPSO), the multi-objective ant lion optimizer (MOALO), and the non-dominated sorting genetic algorithm-II (NSGA-II). Computational simulations are performed on well-studied ZDT benchmark test functions. Comprehensive comparisons are carried out regarding convergence, diversity, and solution distribution. Experiment 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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