In recent years, path planning has been one of the most concerned problems in mobile robotics. This study investigates a multi-objective path planning problem focused on minimizing path length and maximizing path safe...
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In recent years, path planning has been one of the most concerned problems in mobile robotics. This study investigates a multi-objective path planning problem focused on minimizing path length and maximizing path safety. Based on the characteristics of this problem, a mathematical model is established, and then an enhanced artificial bee colony algorithm is proposed to solve this problem. In the proposed algorithm, a new hybrid initialization strategy is designed to generate a high-quality initial population. In the employed bee phase, in addition to the crossover and mutation operators, two objective-oriented evolutionary operators are developed. In the onlooker bee phase, two self-learning optimization mechanisms are applied to the non-dominated and dominated individuals, respectively. Specifically, the collaborative-based optimization mechanism is designed to improve the quality of the non-dominated individuals. The dominance-guide optimization mechanism is developed to guide the dominated individuals to learn from the non-dominated ones. In the scout bee phase, a novel individual-restart strategy that considers the useful information of global best solutions is investigated, which increases the proposed algorithm's exploration ability. Finally, the proposed algorithm is compared with five state-of-the-art algorithms on sixteen instances from four representative environments. Simulation results show that the proposed algorithm achieved average improvements of 2.60% and 90.77% on the hypervolume and inverted generational distance metrics, respectively, compared with the algorithm with the second-best performance. These demonstrate the effectiveness and high performance of the proposed algorithm for solving multi-objective path planning problems in terms of both population diversity and solution quality.
The application of beam-hopping technology to low earth orbit satellites can effectively achieve flexible allocation and efficient utilization of on-board resources. Considering that the power resources on low earth o...
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The application of beam-hopping technology to low earth orbit satellites can effectively achieve flexible allocation and efficient utilization of on-board resources. Considering that the power resources on low earth orbit satellites are limited, the electromagnetic environment is complex and changeable, and the terminal distribution and service requirements are highly dynamic. We established the service model, service priority model and multibeam resource scheduling model under the constraints of beam bandwidth, on-board power, service priorities, etc. To solve the catastrophic problem of a large solution space in the resource scheduling model and to improve the convergence of the algorithm, we propose an enhanced artificial bee colony algorithm. The optimization strategy improves the process of population initialization, solution updates, and search for the global optimal solution. The simulation results show that under the constraints of cochannel interference and on-board resource utilization, the algorithm always converges to the objective function at the fastest speed, which proves that the algorithm has high applicability to the high dynamic characteristics of LEO satellites. In addition, the algorithm can obtain the global optimal solution, and thus, it can ensure the fairness of resource allocation and the effectiveness of service completion.
An enhanced artificial bee colony algorithm is presented for optimal reconfiguration of a photovoltaicthermoelectric generation hybrid system, especially under partial shading conditions. The goal is to achieve real-t...
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An enhanced artificial bee colony algorithm is presented for optimal reconfiguration of a photovoltaicthermoelectric generation hybrid system, especially under partial shading conditions. The goal is to achieve real-time maximum power extraction. To address the limitation of the original artificialbeecolonyalgorithm of susceptibility to local optima, a second-order oscillatory perturbation tactic is integrated for a more refined balance between local exploitation and global exploration during iterations. For the performance evaluation of the modified enhanced artificial bee colony algorithm, its performance is validated and compared against eleven other meta-heuristic strategies under three distinct array configurations: 4 x 4, 15 x 15, and 15 x 20, exposed to varied shading situations. The maximum power of 15 x 15 and 20 x 20 arrays after enhanced artificial bee colony algorithm reconfiguration increased by 22.63 % and 22.90 %, respectively, than before the reconfiguration. enhanced artificial bee colony algorithm significantly reduced the adverse effects of multiple peak power distributions generated by partial shading conditions, achieving the lowest mismatch power loss, with 15 x 15 and 20 x 20 arrays improving by 3.44 % and 5.20 % over the gravity search algorithm, respectively. Additionally, the practical hardware implementation feasibility of enhanced artificial bee colony algorithm is confirmed through a hardware-in-loop analysis utilizing the RTLAB platform, emphasizing its relevance in realworld engineering scenarios.
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