An improved chaos sparrow search algorithm for UAV path planning

Dynamic gold rush optimizer: fusing worker adaptation and salp navigation mechanism for enhanced search

The Dynamic Gold Rush Optimizer (DGRO) is presented as an advanced variant of the original Gold Rush Optimizer (GRO), addressing its inherent limitations in exploration and exploitation. While GRO has demonstrated efficacy in solving optimization problems, its susceptibility to premature convergence and suboptimal solutions remains a critical challenge. To overcome these limitations, DGRO introduces two novel mechanisms: the Salp Navigation Mechanism (SNM) and the Worker Adaptation Mechanism (WAM). The SNM enhances both exploration and exploitation by dynamically guiding the population through a stochastic strategy that ensures effective navigation of the solution space. This mechanism also facilitates a smooth transition between exploration and exploitation, enabling the algorithm to maintain diversity during early iterations and refine solutions in later stages. Complementing this, the WAM strengthens the exploration phase by promoting localized interactions among individuals within the population, fostering adaptive learning of promising search regions. Together, these mechanisms significantly improve DGRO's ability to converge toward global optima. A comprehensive experimental evaluation was conducted using benchmark functions from the Congress on Evolutionary Computation (CEC) CEC2013 and CEC2020 test suites across 30 and 50-dimensional spaces, alongside seven complex engineering optimization problems. Statistical analyses, including the Wilcoxon Rank-Sum Test (WRST) and Friedman Rank Test (FRT), validate DGRO's superior performance, demonstrating significant advancements in optimization capability and stability. These findings underscore the effectiveness of DGRO as a competitive and robust optimization algorithm.

Dynamic path planning of UAV with least inflection point based on adaptive neighborhood A* algorithm and multi-strategy fusion

Planning a safe and efficient global path in a complex three-dimensional environment is a complex and challenging optimization task. Existing planning algorithms are faced with problems such as lengthy path, too many inflection points and insufficient dynamic obstacle avoidance performance. In order to solve these challenges, this paper proposes a dynamic obstacle avoidance algorithm (MSF-MTPO) with multi-strategy fusion to achieve the least inflection point path optimization. Initially, an adaptive extended neighborhood A* algorithm is designed, which dynamically adjusts the neighborhood extension range according to the distribution of obstacles around the current location, selecting the optimal travel direction and step size each time to reduce redundant paths and unnecessary extended nodes. Then, combined with the two-way search mechanism, starting from the original starting point and the end point, the opposite current node is searched as the target point, respectively, so as to reduce the number of search nodes and reduce the search time. In order to further improve the path efficiency, an inflection point trajectory correction method is designed to eliminate redundant inflection points on the premise of ensuring path safety. Fourthly, in order to solve the problem of path deviation or excessive softening caused by the limited path control points in existing smoothing methods, a local tangent circle smoothing method is proposed, which effectively improves the smoothness of the trajectory on the basis of retaining the superiority of the original path. Finally, the global optimization path is used as the guiding trajectory of artificial potential field method to avoid falling into local optimum and realize dynamic obstacle avoidance. In addition, the performance is compared with several advanced algorithms in different environments, and the MSF-MTPO algorithm has the lowest path cost in different complex scenarios, which proves the effectiveness and stability of MSF-MTPO in UAV 3D path planning.

Multi-Strategy Improved Harris Hawk Optimization Algorithm and Its Application in Path Planning

Path planning is a key problem in the autonomous navigation of mobile robots and a research hotspot in the field of robotics. Harris Hawk Optimization (HHO) faces challenges such as low solution accuracy and a slow convergence speed, and it easy falls into local optimization in path planning applications. For this reason, this paper proposes a Multi-strategy Improved Harris Hawk Optimization (MIHHO) algorithm. First, the double adaptive weight strategy is used to enhance the search capability of the algorithm to significantly improve the convergence accuracy and speed of path planning; second, the Dimension Learning-based Hunting (DLH) search strategy is introduced to effectively balance exploration and exploitation while maintaining the diversity of the population; and then, Position update strategy based on Dung Beetle Optimizer algorithm is proposed to reduce the algorithm's possibility of falling into local optimal solutions during path planning. The experimental results of the comparison of the test functions show that the MIHHO algorithm is ranked first in terms of performance, with significant improvements in optimization seeking ability, convergence speed, and stability. Finally, MIHHO is applied to robot path planning, and the test results show that in four environments with different complexities and scales, the average path lengths of MIHHO are improved by 1.99%, 14.45%, 4.52%, and 9.19% compared to HHO, respectively. These results indicate that MIHHO has significant performance advantages in path planning tasks and helps to improve the path planning efficiency and accuracy of mobile robots.

Multi-UAV Cooperative Coverage Search for Various Regions Based on Differential Evolution Algorithm

In recent years, remotely controlling an unmanned aerial vehicle (UAV) to perform coverage search missions has become increasingly popular due to the advantages of the UAV, such as small size, high maneuverability, and low cost. However, due to the distance limitations of the remote control and endurance of a UAV, a single UAV cannot effectively perform a search mission in various and complex regions. Thus, using a group of UAVs to deal with coverage search missions has become a research hotspot in the last decade. In this paper, a differential evolution (DE)-based multi-UAV cooperative coverage algorithm is proposed to deal with the coverage tasks in different regions. In the proposed algorithm, named DECSMU, the entire coverage process is divided into many coverage stages. Before each coverage stage, every UAV automatically plans its flight path based on DE. To obtain a promising flight trajectory for a UAV, a dynamic reward function is designed to evaluate the quality of the planned path in terms of the coverage rate and the energy consumption of the UAV. In each coverage stage, an information interaction between different UAVs is carried out through a communication network, and a distributed model predictive control is used to realize the collaborative coverage of multiple UAVs. The experimental results show that the strategy can achieve high coverage and a low energy consumption index under the constraints of collision avoidance. The favorable performance in DECSMU on different regions also demonstrate that it has outstanding stability and generality.

Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed "Mean Differential Variation", to enhance the algorithm's ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems.

Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV Inspection

Intelligent workshop UAV inspection path planning is a typical indoor UAV path planning technology. The UAV can conduct intelligent inspection on each work area of the workshop to solve or provide timely feedback on problems in the work area. The sparrow search algorithm (SSA), as a novel swarm intelligence optimization algorithm, has been proven to have good optimization performance. However, the reduction in the SSA's search capability in the middle or late stage of iterations reduces population diversity, leading to shortcomings of the algorithm, including low convergence speed, low solution accuracy and an increased risk of falling into local optima. To overcome these difficulties, an improved sparrow search algorithm (namely the chaotic mapping-firefly sparrow search algorithm (CFSSA)) is proposed by integrating chaotic cube mapping initialization, firefly algorithm disturbance search and tent chaos mapping perturbation search. First, chaotic cube mapping was used to initialize the population to improve the distribution quality and diversity of the population. Then, after the sparrow search, the firefly algorithm disturbance and tent chaos mapping perturbation were employed to update the positions of all individuals in the population to enable a full search of the algorithm in the solution space. This technique can effectively avoid falling into local optima and improve the convergence speed and solution accuracy. The simulation results showed that, compared with the traditional intelligent bionic algorithms, the optimized algorithm provided a greatly improved convergence capability. The feasibility of the proposed algorithm was validated with a final simulation test. Compared with other SSA optimization algorithms, the results show that the CFSSA has the best efficiency. In an inspection path planning problem, the CFSSA has its advantages and applicability and is an applicable algorithm compared to SSA optimization algorithms.