Performance Evaluation of Three Intelligent Optimization Algorithms for Obstacle Avoidance Path Planning

Uniform magnetic fields are widely used in atomic magnetometers, MRI and other fields. The traditional method of solving closed-form equations is usually used to design highly uniform magnetic field coils. In this paper, a new intelligent algorithm, wolf pack algorithm (WPA), is proposed to design the coil structure of a highly uniform magnetic field. This method can replace the traditional method to obtain the coil parameters. We additionally use a discrete optimization method to solve the rounding error. According to the investigation, compared with particle swarm optimization (PSO) algorithm, WPA has stronger global optimization ability, faster convergence speed and multiple optimization strategies. We set appropriate coil constraints (structural and process constraints) and simplify the multi-dimensional solutions to one-dimensional solutions to reduce the difficulty of operation. The distribution of magnetic field intensity of three axes of coils is analyzed by the finite element analysis method, and the 1%, 0.1%, and 0.01% uniform regions are studied. Under the same conditions, the uniform magnetic field regions of the coils designed by the PSO and WPA, as well as a single pair of axial and radial coils are compared. The performance results of multiple pairs of coils are superior to those of a single pair of coils. In general, the uniformity of the axial and radial magnetic field coils designed by the WPA is better than that designed by the PSO. Finally, the experimental value of the magnetic field uniformity is nearly consistent with the theoretical value.

As robotics technologies evolve rapidly, path planning has emerged as a fundamental pillar underpinning autonomous navigation systems. Traditional algorithms such as A*, Dijkstra, and Bellman-Ford demonstrate competence in structured environments, yet exhibit notable limitations in dynamic and uncertain scenarios. In contrast, intelligent algorithmsincluding Genetic Algorithms (GA), Reinforcement Learning (RL), and hybrid strategiesprovide measurable advantages in adaptability, learning capacity, and robustness. This study centers on comparing traditional and intelligent control algorithms for path planning from a theoretical perspective, outlining their respective principles, performance traits, and application contexts. The findings suggest that intelligent algorithms, owing to their capacity for real-time learning, environmental adaptation, and heuristic generalization, consistently outperform traditional approaches in dynamic and unstructured operational contexts. The paper offers critical theoretical insights to guide future algorithm selection and optimization for robot navigation. Furthermore, the paper discusses real-world implementation challenges, such as computational complexity, sensor uncertainty, and algorithm convergence stability, to highlight the applicability of these techniques in complex systems.

Background: Path planning technology has a wide range of applications in many fields. Applications in the field of high technology include: autonomous and touchless action of robots; obstacle avoidance and defense flight of drones; cruise missiles to avoid radar search, anti- bouncing attacks, and to complete the task of sudden defense and demolition. Applications in daily life include: GPS navigation; road planning based on GIS system; urban road network planning and navigation. Applications in the field of decision-making management include: vehicle problem (VRP) in logistics management and similar resource allocation problems in resource management. Routing problems in the field of communication technology. Any planning problem that can be topologized as a point and line network can basically be solved by the method of path planning. Different intelligent algorithms have different characteristics, and their application scope and fields are also different, so it is of great significance for the development of path planning technology to study the path planning intelligent algorithms from the characteristics of the algorithms themselves and their applications. Objective: Analyze the advantages and disadvantages of various types of planning algorithms, look forward to the future development trend of mobile robot path planning, and provide certain ideas for the research of mobile robot path planning. Methods: Search journals, patents, conferences, and papers related to mobile robot path planning, and summarize and analyze the advantages and disadvantages of various planning algorithms. Results: Based on the research results of many scholars, this study summarizes different mobile robot path planning methods. It is divided into four categories: traditional planning, intelligent search, artificial intelligence and local obstacle avoidance. This paper introduces and analyzes the latest research results of these types, including their design ideas, advantages and disadvantages, and improvement measures. The research methods adopted are analyzed in order to maximize the advantages of each algorithm and expand the application field of robot path planning to provide ideas and references. Conclusion: This paper provides guidance for the design and optimization of robot path planning. Finally, this paper summarizes the future development trend of robot path planning, and looks forward to the future development trend and key areas of robot path planning.

A wavelet neural network (WNN) prediction model based on improved wolf pack algorithm(WPA) is proposed to meet the demand of Shanxi's spot market, which requires power sales companies to "quote quantity but not offer price". Customers are classified by considering the differences in electricity consumption characteristics of different industries. The bidirectional comparison method is used to preprocess the original data, which can effectively reduce the errors in the process of data measurement and acquisition. Using WNN to forecast the customer's day-ahead electrical load. Meantime, the intelligent algorithm is used to optimize the network parameters and strengthen the prediction performance of the WNN. The wolf pack algorithm is used to optimize the system, and the walking mode of the wolf pack is improved, which further to improve the optimization ability of the system. An example shows that the model is more accurate in short-term load prediction. Compared with before the improvement, the prediction accuracy has been improved by nearly 2%, and the accuracy has reached 96.4%, which can provide a strong guarantee of the electricity sales company to master the customer load curve, participate in the spot market, and improve its competitiveness.

The article firstly introduces two respective theories on research and evolvement about Wolf Pack Intelligent Optimization Algorithm, and compares with the differences and similarities between the two theories. It also illustrates the differences in performance on optimization solution through the experimental result. Then, it concludes the improvement research on wolf pack algorithm, focused on the improvement on parameter setting and hybrid algorithm. Thirdly, the article elaborates the typical applications about wolf pack algorithm on function optimization, combination optimization and engineering optimization. At last, the article summarizes the deficiency in the research and proposes the research goal of next step.

Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption.

Meng, X. and Li, X., 2020. Research on optimization of port logistics distribution path planning based on intelligent group classification algorithm. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 205-207. Coconut Creek (Florida), ISSN 0749-0208.At present, the logistics industry has become an important part of social and economic development. The success of modern logistics enterprises depends on efficient cooperation with all upstream and downstream enterprises in the supply chain, and information technology is the prerequisite for such cooperation. Artificial Fish Swarm Algorithm (AFSA) is applied to robot path planning in intelligent bionic way. In order to improve the solving speed of the planning and reduce the length of the planning path, the convergence speed of the algorithm in the later stage is reduced and the local optimal solution is easily trapped. Location information is of great significance to the application of artificial fish swarm algorithm. The location of events or information sources is an important part of data transmission in sensor networks. The operation of the distribution system plays a vital role in the whole logistics system, and its analysis and design are the premise for its good operation.

Firefly algorithm is a new heuristic intelligent optimization algorithm and has excellent performance in many optimization problems. However, like other intelligent algorithms, the firefly algorithm still has some shortcomings, such as the algorithm is easy to fall into the local optimal, and the convergence speed is slow in the later period. Therefore, this paper proposes a new firefly algorithm with dynamic step change strategy (DsFA) to balance the global and local search capabilities. Thirteen well-known benchmark functions are used to verify the performance of our proposed method, the computational results show that DsFA is more efficient than many other FA algorithms.KeywordsFirefly algorithmHeuristic intelligent optimization algorithmDynamic step change strategy

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

The underground coal mine environment is complex, and traditional drilling path planning relies on experience, leading to issues such as high target deviation rates and excessive ineffective footage. This study constructs a three-dimensional trajectory geometric model, integrates geological and engineering constraints, and proposes an improved adaptive intelligent optimization algorithm. These measures enable precise planning and efficient optimization of underground directional drilling paths, providing technical support for safe and efficient coal mine drilling.

ABSTRACTDue to interference phenomena among unnatural dimensions of the motion robots’ operations space, optimal path planning of them has to satisfy not just one criterion, but rather multi-objects. In this paper, we propose a novel multi-object approach for optimal mobile robot path planning, based on bees pollen optimizer (BPO). We consider two objects of distance and smooth path of the special plan for motion robots for constructing a minimization one. In operation environment for action robots, the location of the target and the obstacles are set up for the solution of BPO. The selected sequence of the mobile robot is a set of the chosen global best settlement in each iteration, which updates its archived data throughout the movement for motion robots in order. A series of simulations are executed in some environments for the best pathway once the robot reaches its goal. The results indicate that the proposed approach offered the robot path to its target without touching the obstacles, and the proposed method may be an alternative approach to optimize the motion robot path planning.

Antenna modeling based on meta-heuristic intelligent algorithms and neural networks

Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.

Path planning is an important research direction in the field of robotics; however, with the advancement of modern science and technology, the study of efficient, stable, and safe path-planning technology has become a realistic need in the field of robotics research. This paper introduces an improved sparrow search algorithm (ISSA) with a fusion strategy to further improve the ability to solve challenging tasks. First, the sparrow population is initialized using circle chaotic mapping to enhance diversity. Second, the location update formula of the northern goshawk is used in the exploration phase to replace the sparrow search algorithm's location update formula in the security situation. This improves the discoverer model's search breadth in the solution space and optimizes the problem-solving efficiency. Third, the algorithm adopts the Lévy flight strategy to improve the global optimization ability, so that the sparrow jumps out of the local optimum in the later stage of iteration. Finally, the adaptive T-distribution mutation strategy enhances the local exploration ability in late iterations, thus improving the sparrow search algorithm's convergence speed. This was applied to the CEC2021 function set and compared with other standard intelligent optimization algorithms to test its performance. In addition, the ISSA was implemented in the path-planning problem of mobile robots. The comparative study shows that the proposed algorithm is superior to the SSA in terms of path length, running time, path optimality, and stability. The results show that the proposed method is more effective, robust, and feasible in mobile robot path planning.

The robotic mobile fulfillment system (RMFS) is a new automatic warehousing system, a type of green technology, and an emerging trend in the logistics industry. In this study, we take an RMFS as the research object and combine the connected issues of storage location assignment and path planning into one optimization problem from the perspective of collaborative optimization. A sustainable mathematical model for the collaborative optimization of storage location assignment and path planning (COSLAPP) is established, which considers the relationship between the location assignment of goods and rack storage and path planning in an RMFS. On this basis, we propose a location assignment strategy for goods clustering and rack turnover, which utilizes reservation tables, sets AGV operation rules to resolve AGV running conflicts, and improves the A-star(A*) algorithm based on the node load to find the shortest path by which the AGV handling the racks can complete the order picking. Ultimately, simulation studies were performed to ascertain the effectiveness of COSLAPP in the RMFS; the results show that the new approach can significantly improve order picking efficiency, reduce energy consumption, and lessen the operating costs of the warehouse of a distribution center.