Due to the increased employment of robots in modern society, path planning methods based on human–robot collaborative mobile robots have been the subject of research in both academia and industry. The dynamic window approach used in the research of the robot local path planning problem involves a mixture of fixed weight coefficients, which makes it hard to deal with the changing dynamic environment and the issue of the sub-optimal global planning paths that arise after local obstacle avoidance. By dynamically modifying the combination of weight coefficients, we propose, in this research, the use of fuzzy control logic to optimize the evaluation function’s sub-functions and enhance the algorithm’s performance through the safe and dynamic avoidance of obstacles. The global path is introduced to enhance the dynamic window technique’s ability to plan globally, and important points on the global path are selected as key sub-target sites for the local motion planning phase of the dynamic window technique. The motion position changes after local obstacle avoidance to keep the mobile robot on the intended global path. According to the simulation results, the enhanced dynamic window algorithm cuts planning time and path length by 16% and 5%, respectively, while maintaining good obstacle avoidance and considering a better global path in the face of various dynamic environments. It is difficult to achieve a local optimum using this algorithm.

This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios.

Magnetically levitated (ML) systems that incorporate PCB coils represent a growing trend in precision machining, valued for their controllable current flow and high fill factor. The size of modern power devices is decreasing to enhance power density, minimize parasitic inductance, and reduce power losses. However, due to the high resistance of PCB coils, managing heat generation has become a significant area of study. This paper seeks to optimize PCB coil design to minimize power loss and control peak temperatures in ML systems, using a numerical model. An improved magnetic node model is employed to construct the magnetic fields of an ML system. The proposed optimization method considers the interdependencies among parameters to reduce overall power loss from coil resistance and switching losses in the H-bridge circuit, while enhancing heat dissipation efficiency in steady-state operation. A heuristic multi-objective optimization algorithm is employed to optimize the design of the ML actuator. The optimization process initially focuses on the PCB coils, with the magnet size held constant. Once the optimal coil parameters are identified, the magnet volume is optimized. By integrating a theoretical analysis with simulation, this approach effectively addresses the optimization challenges and achieves the desired performance for the ML actuator. Coils and magnets are constructed based on the optimized design and tested by the magnetic field simulation software Radia, confirming the feasibility of the approach. The method was also applied to a different type of ML system for comparison, demonstrating the universality of the proposed strategy. In this optimization effort, the maximum temperature reduction reached an impressive 50 °C