Cooperative 3D multi-UAV path planning in obstacle-dense airspace is a high-dimensional constrained optimization problem involving obstacle clearance, inter-UAV separation, and path smoothness. In cluttered environments, narrow feasible corridors may lead to unstable optimization and poor repeatability in practical missions. To address this engineering challenge, this paper proposes a two-stage HCPO-SDF framework, where an enhanced Hybrid Crested Porcupine Optimizer (HCPO) performs joint waypoint optimization and signed-distance-field-guided cubic-spline refinement achieves collision-aware path smoothing. By coordinating four complementary enhancement mechanisms, HCPO mitigates premature convergence and balances exploration and exploitation in high-dimensional constrained search. With the same objective-function evaluation budget for every algorithm, HCPO records the lowest or tied-lowest mean and best objective values on 10 of the 12 CEC2022 functions. This result covers 83.3% of the test functions. The CEC2020 ablation results show that the four enhancement mechanisms make complementary contributions. Experiments in three increasingly cluttered 3D environments show that HCPO–SDF achieves a planning success rate of up to 90.0% and exhibits improved run-to-run stability compared with the original CPO, indicating its effectiveness for cooperative multi-UAV path planning in cluttered 3D environments.

To solve the path planning problem of Unmanned Aerial Vehicle (UAV) in multi-hazardous environments, a Behavioral Training Mechanism Based Partial Reinforcement Optimization Algorithm (BT-PRO) is proposed. In BT-PRO, an elite population strategy is proposed to optimize the initial population distribution and accelerate convergence speed. Then, a Gaussian perturbation-based global stimulation strategy is used during the stimulation phase to direct the learner population towards the optimal solution and augment the exploitation capacity of BT-PRO. Finally, a novel behavioral training mechanism is designed to enhance population communication, balance the exploitation and exploration of BT-PRO, and improve algorithmic search efficiency. After ablation experiments and verification using the CEC2022 benchmark test function, the experimental results indicate that BT-PRO exhibits superior optimization capability and robustness compared to other optimization algorithms. Furthermore, the UAV path planning problem is addressed through the BT-PRO algorithm. Experimental results show that BT-PRO can generate smooth and low-cost paths in both low-dimensional small-scale scenarios and high-dimensional large-scale scenarios. These findings substantiate the effectiveness of BT-PRO in resolving the challenges associated with UAV path planning.

With the rapid growth of electric vehicle (EV) adoption, charging station operation is increasingly challenged by stochastic charging demand, significant load fluctuations, and limited distribution capacity. Traditional fixed pric ing or simple time-of-use pricing mechanisms often struggle to balance operational revenue, user service quality, and grid security. To address these challenges, this paper develops a dynamic pricing optimization framework for EV charging stations. Unlike existing studies that simplify user response, charging dynamics, or grid-side feasibility, the proposed framework explicitly couples behavioral, physical, and grid-level mechanisms within a unified simulation-based profit evaluation model. The model incorporates price-sensitive vehicle arrivals, queuing behavior, abandonment mechanisms, nonlinear charging power, and transformer capacity constraints, thereby converting EV charging pricing into a high-dimensional stochastic black-box optimization problem. To solve this high-dimensional stochastic pricing optimization problem, this paper proposes the Leader-Driven Dream Optimization Algorithm (LDDOA), which improves the original Dream Optimization Algorithm through leader-follower learning and dimension-level information transfer to enhance global search capability and con vergence stability. In the EV charging station dynamic pricing case study, a 7-day operation horizon with 56 pricing decision variables is considered. Under the fixed-price baseline strategy, the weekly profit is 4996.76 CNY. In contrast, the proposed LDDOA-based pricing strategy achieves an average profit of 7106.77 CNY over 20 independent runs, corresponding to a 42.23% improvement in the base-case scenario. The best observed profit reaches 7757.44 CNY. Additional sensitivity experiments under different demand elasticities, user compo sitions, capacity margins, and operational settings further show that the proposed framework maintains stable profit improvement while keeping waiting times, abandonment rates, and peak-load risks at low levels. The op timized pricing strategy also redistributes charging demand across time, thereby reducing peak-load pressure while maintaining service quality. The findings indicate that the proposed method can effectively coordinate revenue optimization, grid security, and user service quality in complex stochastic environments, providing a practical optimization tool for dynamic pricing and intelligent operation of EV charging stations.

Variational Mode Decomposition (VMD) is effective for analyzing mechanical vibration signals, but its performance is hindered by noise interference and sensitivity to parameters like the number of mode components and penalty factor. This study aims to enhance VMD for bearing fault analysis. Methods We propose a Mixed Grey Wolf Optimizer (MBGWO) integrating Latin Hypercube Sampling, a Beetle Antennae Search-inspired “vision” mechanism, and a nonlinear convergence factor to optimize VMD parameters. The VMD framework is improved with frequency distribution-guided center frequency updates and energy-adaptive mode suppression. Results The MBGWO-optimized VMD, coupled with envelope spectrum analysis, effectively decomposes bearing vibration signals and extracts early fault features. Conclusion Simulation and experimental results on synthetic and real bearing datasets demonstrate the proposed method’s superior performance and robustness compared to traditional VMD, offering significant potential for early fault detection in rotating machinery.

A sparse compressed deep echo state network (SCDESN) incorporating sparse input units, compressed sampling and principal component analysis (PCA) units is proposed in this paper, and theoretically proved the sufficient and necessary conditions to ensure the echo state characteristics of the proposed scheme. In addition, a hybrid arithmetic optimization algorithm based on matrix design strategy and boundary selection strategy (SVD-HAOA) was proposed to optimize the hyper-parameters of the SCDESN model, taking into account the characteristics of the SCDESN model. The steps and process for optimizing the hyper parameters of the SCDESN model using SVD-HAOA were presented. Finally, the SCDESN model optimized by SVD-HAOA was experimentally and summarized on a classic benchmark dataset and two real-world chaotic time series. The outcomes of the simulation indicated that the suggested model surpassed alternative models in performance and enhanced computational efficiency while maintaining prediction accuracy. This model may serve as a viable alternative for real-world applications.

Unmanned aerial vehicle (UAV) path planning is critical for autonomous flight operations, yet existing algorithms face challenges in balancing optimization accuracy and computational efficiency. In this paper, we proposed a Multi-Strategy Snow Avalanches Algorithm (MSAA) to address the limitations of the original Snow Avalanches Algorithm (SAA), which suffered from low convergence precision and local optima entrapment. The MSAA integrated three key enhancements: a chaos theory-based initialization method to enhance population diversity, a stochastic central learning mechanism for dynamic exploration–exploitation balance, and an improved sine–cosine operator to prevent premature convergence. Ultimately, the MSAA was successfully applied to the challenge of 3D UAV path planning. By integrating cubic B-spline interpolation for smooth trajectory generation, the proposed method achieved impressive results, with mean path error rates of 0.5

A novel hybrid arithmetic optimization algorithm (HAOA) is proposed to address the inherent constraints of traditional numerical computing techniques, including increased computational complexity and excessive reliance on gradient information. First, the Latin hypercube sampling initialization strategy is used to generate higher quality initial candidate solutions. Then, the strategy is used to expand the advantages of leading individuals, boost the local search ability and improve the computational accuracy of the algorithm. Finally, the adaptive t-distribution mutation perturbation strategy is adopted to randomly perturb the position of the current optimal solution, aiming to prevent the algorithm from falling into a local optimal solution. The performance of HAOA is examined in comparison to alternative algorithms using the CEC 2022 data set and Bayesian validation is applied to comprehensively validate the superiority of the HAOA. Numerical experimental results demonstrate that HAOA achieves more accurate extremal and integral results, and has a high solution speed.

Dynamic optimization problems exist widely in chemical industry, and its operational variables change with the evolution of both space and time. Therefore, dynamic optimization problems have important research significance and challenges. To solve this problem, a multi-strategy mayfly optimization algorithm (MMOA) combined with control variable parameterization method(CVP) is proposed in this paper. MMOA introduces three improvements on the basis of the original algorithm, namely, circle chaos crossover strategy, center wandering strategy and boundary correction strategy. The hybrid strategy can better balance the exploration and exploitation ability of the algorithm. Based on MATLAB simulation environment, MMOA was evaluated. The experimental results show that MMOA has excellent performance in solving precision, convergence speed and stability for the benchmark function. For the six classical chemical dynamic optimization problems, MMOA obtained the performance indexes of 0.61071, 0.4776, 0.57486, 0.73768, 0.11861 and 0.13307, respectively. Compared with the data in the previous literature, MMOA can obtain more accurate control trajectory and better performance indicators. It provides an effective way to solve the dynamic optimization problem.

Since late 2019, coronavirus disease 2019 (COVID-19) has been spreading globally, presenting a significant threat to human lives and health, and exerting a profound impact on worldwide economic development. Due to the highly contagious nature of COVID-19, precise and prompt diagnosis has become paramount. The effective and rapid identification of COVID-19 through computed tomography (CT) images has thus garnered substantial interest, prompting scientists to propose various segmentation methods aimed at improving the diagnostic accuracy of CT images. Drawing from these foundations, the study introduces an innovative multilevel threshold segmentation method known as the Reinforcement Learning-based Enhanced Sand Cat algorithm (QLSCSO). QLSCSO represents a novel optimization algorithm distinguished by its remarkable convergence accuracy and the capacity to escape local optima. The introduction of this optimizer incorporates reinforcement learning methodologies into the population iteration process of heuristic techniques. In the algorithm’s update phase, a hybrid model and three distinct mutation strategies are employed to enhance its capability to overcome local optima. Consequently, the developed QLSCSO method produces high-quality segmentation results while reducing vulnerability to segmentation process stagnation. To establish the effectiveness of the proposed method, comparative analyses are initiated between QLSCSO and other advanced meta-algorithms using the IEEE CEC 2022 benchmark functions. Furthermore, QLSCSO undergoes experimental evaluations on CT images of COVID-19, including comprehensive comparative assessments with other competing segmentation methods and thorough validation. The results conclusively demonstrate the outstanding performance of the unique segmentation method based on QLSCSO across a range of performance evaluation metrics. Therefore, this approach offers an efficient segmentation procedure for COVID-19 images and even other pathological medical images.

Multimodal multi-objective problems refer to situations where more than one Pareto set in the decision space corresponds to the same Pareto front in the objective space. In this paper, we propose a multimodal multi-objective coati optimization algorithm based on spectral clustering (MMOCOA-SC) for use in multimodal multi-objective problems. The algorithm starts by creating stable subpopulations via a spectral clustering method, grouping similar individuals within each subpopulation. Then, an improved coati optimization algorithm is proposed in order to increase the diversity of the population and continuously search for near-Pareto optimal solutions. Finally, we use an improved crowding distance method combined with a non-dominated sorting method to maintain and retain multiple near-Pareto optimal solutions. The MMOCOA-SC is evaluated alongside five state-of-the-art algorithms using the 2020 CEC test suite, IDMP test problems, and twelve classic engineering application problems. In comparison to the other five state-of-the-art algorithms, the experimental results show that MMOCOA-SC exhibits a superior performance when applied to the problems. This study explores multimodal multi-objective optimization algorithms with a focus on the concept of symmetry, which is crucial for multimodal multi-objective optimization problems in terms of achieving a balanced decision space and a diversity of solutions in the objective space.

Unmanned aerial vehicle (UAV) path planning plays an important role in UAV flight, and an effective algorithm is needed to realize UAV path planning. The sand cat algorithm is characterized by simple parameter setting and easy implementation. However, the convergence speed is slow, easy to fall into the local optimum. In order to solve these problems, a novel sand cat algorithm incorporating learning behaviors (LSCSO) is proposed. LSCSO is inspired by the life habits and learning ability of sand cats and incorporates a new position update strategy into the basic Sand Cat Optimization Algorithm, which maintains the diversity of the population and improves the convergence ability during the optimization process. Finally, LSCSO is applied to the challenging UAV 3D path planning with cubic B-spline interpolation to generate a smooth path, and the proposed algorithm is compared with a variety of other competing algorithms. The experimental results show that LSCSO has excellent optimization-seeking ability and plans a safe and feasible path with minimal cost consideration among all the compared algorithms.

针对无源定位收敛速度慢、方位信息不准确等问题,提出面向三维无源目标定位的深度学习算法优化.利用激光雷达传感器采集目标三维数据,通过数据配准得到高质量点云数据;建立包含三层网络结构的卷积神经网络,将采集到的数据输入到网络中,确定定位边界框,计算位置置信度;针对卷积神经网络处理稀疏数据速度过慢的问题,设计一种稀疏卷积并行算法,引入检测器优化网络结构,提高运算速度;确立边界框约束条件,改善网络训练性能;设置边框位置误差、置信度误差以及定位误差三种损失函数,增强算法的特征学习能力,使算法的定位能力更强.实验结果表明,所提方法定位误差小,加快了收敛速度.

Aerial photography is a long-range, non-contact method of target detection technology that enables qualitative or quantitative analysis of the target. However, aerial photography images generally have certain chromatic aberration and color distortion. Therefore, effective segmentation of aerial images can further enhance the feature information and reduce the computational difficulty for subsequent image processing. In this paper, we propose an improved version of Golden Jackal Optimization, which is dubbed Helper Mechanism Based Golden Jackal Optimization (HGJO), to apply multilevel threshold segmentation to aerial images. The proposed method uses opposition-based learning to boost population diversity. And a new approach to calculate the prey escape energy is proposed to improve the convergence speed of the algorithm. In addition, the Cauchy distribution is introduced to adjust the original update scheme to enhance the exploration capability of the algorithm. Finally, a novel "helper mechanism" is designed to improve the performance for escape the local optima. To demonstrate the effectiveness of the proposed algorithm, we use the CEC2022 benchmark function test suite to perform comparison experiments. the HGJO is compared with the original GJO and five classical meta-heuristics. The experimental results show that HGJO is able to achieve competitive results in the benchmark test set. Finally, all of the algorithms are applied to the experiments of variable threshold segmentation of aerial images, and the results show that the aerial photography images segmented by HGJO beat the others. Noteworthy, the source code of HGJO is publicly available at https://github.com/Vang-z/HGJO.

近年来群智能优化算法因其原理简单且具有良好的全局寻优性能,在控制向量参数化方法的框架下,被广泛应用于化工动态优化问题的求解.为进一步提高化工动态优化问题的求解精度,提出一种增强型海鸥优化算法.该算法采用约束初始化思想来生成初始种群,引入动态收敛因子优化海鸥的攻击模式,以及将Levy飞行策略与动态收敛因子结合并应用到海鸥位置的更新过程,以增强算法跳出局部最优的能力.最后,将提出的改进算法应用于管式反应器的平行反应和批式反应器的优化求解,并与其他智能优化算法以及相关文献的优化结果进行对比分析.实验结果表明,增强型海鸥优化算法可以取得有竞争力的结果,算法具有较强的寻优性能.

The numerical solution of the dynamic optimization problem is often sought for chemical processes, but the discretization of control variables is a difficult problem. Firstly, based on the analysis of the seagull optimization algorithm, this paper introduces the cognitive part in the process of a seagull's attack behavior to make the group approach the best position. Secondly, the algorithm adds the mechanism of natural selection, where the fitness value is used to sort the population, and the best half is used to replace the worst half, so as to find out the optimal solution. Finally, the improved seagull optimization algorithm (ISOA) is combined with the unequal division method to solve dynamic optimization problems. The feasibility of the method is verified by three practical examples of dynamic optimization in chemical industry.

From the end of 2019 until now, the Coronavirus Disease 2019 (COVID-19) has been rampaging around the world, posing a great threat to people's lives and health, as well as a serious impact on economic development. Considering the severely infectious nature of COVID-19, the diagnosis of COVID-19 has become crucial. Identification through the use of Computed Tomography (CT) images is an efficient and quick means. Therefore, scientific researchers have proposed numerous segmentation methods to improve the diagnosis of CT images. In this paper, we propose a reinforcement learning-based golden jackal optimization algorithm, which is named QLGJO, to segment CT images in furtherance of the diagnosis of COVID-19. Reinforcement learning is combined for the first time with meta-heuristics in segmentation problem. This strategy can effectively overcome the disadvantage that the original algorithm tends to fall into local optimum. In addition, one hybrid model and three different mutation strategies were applied to the update part of the algorithm in order to enrich the diversity of the population. Two experiments were carried out to test the performance of the proposed algorithm. First, compare QLGJO with other advanced meta-heuristics using the IEEE CEC2022 benchmark functions. Secondly, QLGJO was experimentally evaluated on CT images of COVID-19 using the Otsu method and compared with several well-known meta-heuristics. It is shown that QLGJO is very competitive in benchmark function and image segmentation experiments compared with other advanced meta-heuristics. Furthermore, the source code of the QLGJO is publicly available at https://github.com/Vang-z/QLGJO .

非线性方程组的求解具有重要的数学意义和实际意义,结合二次插值和差分进化算法的优点,提出了一种混合哈里斯鹰优化算法(HHHO)用于求解非线性方程组.先在勘探阶段采用二次插值方法,增强了算法的全局搜索能力;当算法陷入局部最优时,根据早熟机制,针对陷入局部最优的哈里斯鹰进行变异、选择操作,增强种群的多样性,避免算法陷入早熟.通过10 个基准测试函数的测试,证明了HHHO算法在局部搜索能力,求解精度方面优于HHO算法,通过5 个非线性方程组的求解验证上述算法在求解精度、解的求解个数上都有一定的优势.最后把HHHO算法用于求解几何约束问题和三角函数超越方程,进一步验证了算法高效的求解性能.

针对人工水母群算法(JS)在求解较为复杂问题和部分工程问题时存在易陷入局部最优和寻优精度低等问题,提出一种融合多策略的人工水母算法.首先,该算法利用莱维飞行的扰动策略对洋流运动和被动运动阶段位置更新方式进行扰动,增强算法跳出局部最优的能力;其次,对水母跟随洋流和群内被动运动阶段引入指数递减惯性权重,使算法扩大全局搜索范围,提高寻优收敛速度;最后,将黄金正弦引导机制引入至被动运动阶段,替换原有被动运动位置更新方式,提升算法在探索和开发阶段的寻优能力.通过对6个基准测试函数进行测试分析,采用Wilcoxon秩和检验证明改进算法的寻优能力强、收敛速度快,将其应用于压力弹簧设计工程实际问题中,进一步验证了改进算法的适应性和有效性.

为提高无输出变压器功率放大电路的稳定性及可靠性,提出一种自适应火烈鸟搜索优化算法,对其中点电压进行平衡控制.该算法采用Sigmoid函数控制迁徙火烈鸟的惯性权重系数,进行非线性自适应变化,平衡粒子的全局搜索能力和局部探索能力;采用双曲正切函数控制觅食火烈鸟的扩散因子,平衡粒子的寻优速度和收敛精度;在算法后期引入基于中心距的种群变异,提高种群多样性,同时提高算法的精度.基于三种类型的测试函数对所提算法的性能进行验证,并与其他4种典型的群智能优化算法做了对比.结果显示,所提算法在寻优速度和收敛精度上均具有较大的提升.接着利用齿轮系设计问题对算法进行工程测试,最后,对改进算法进行无输出变压器功率放大电路仿真测试,结果证明了算法的优越性.

Optimization problems in the field of industrial engineering usually involve massive amounts of information and complex scheduling process with the characteristics of high-dimension and non-convexity, which bring many challenges to finding an optimal solution. We proposed an improved beetle swarm optimization (IBSO) algorithm demonstrating the potential to solve different problems of path planning in static environment with good performance. Firstly, the algorithm is an upgrade of the original beetle antennae search (BAS) algorithm and the search strategy is improved by replacing a single beetle by multiple beetles. Secondly, the global search ability gets enhanced, and the diversity of optimization is improved through introducing nonlinear sinusoidal disturbance with Levy flight mechanism in beetles’ position. Finally, the search performance of beetle swarm is improved by simulating the characteristics of employment bees to search for a better solution near the honey source field in the Artificial Bee Colony (ABC) algorithm. Our experiment results show that IBSO algorithm can achieve higher search efficiency and wider search ranges through well balancing the advantages of local search and fast optimization of the BAS algorithm with the global search of the improved mechanism. The IBSO algorithm has shown the potential to provide a new solution for several optimization problems in path planning in static environment.