Optimal trajectory planning for collaborative robots based on improved adaptive multi-objective particle swarm algorithm

Research optimization on logistics distribution center location based on adaptive particle swarm algorithm

Automatic feature extraction for online chatter monitoring under variable milling conditions

The hub motor significantly increases the unsprung mass of electric in-wheel vehicles, which deteriorates the ride comfort and safety of vehicles and which can be effectively improved by optimizing the main suspension parameters of vehicles reasonably, so a multi-objective optimization method of main suspension parameters based on adaptive particle swarm algorithm is proposed and the dynamic model of a half in-wheel electric vehicle is established. Taking the stiffness coefficient of the suspension damping spring and damping coefficient of the damper as independent variables, the vertical acceleration of the body, the pitch acceleration and the vertical impact force of the hub motor as optimization variables, and the dynamic deflection of the suspension and the dynamic load of the wheel as constraint variables, the multi-objective optimization function is constructed, and the parameters are simulated and optimized under the compound pavement. The simulation results show that the vertical acceleration and pitch acceleration are reduced by 20.2% and 18.4% respectively, the vertical impact force of the front hub motor is reduced by 3.7%, and the ride comfort and safety are significantly improved.

Aiming at the shortcomings of the traditional fault diagnosis methods of electric motors, the author proposes a method based on the application of improved PSO algorithm in the simulation of motor fault diagnosis. The method optimizes the fault diagnosis method of BP neural network by adopting the adaptive mutation particle swarm algorithm. Firstly, the fault features are extracted from the response signals of the measurable points of the motor to be tested, and wavelet packet decomposition and normalization are performed to construct a sample set; then, the particle swarm improvement algorithm is used to optimize the weights and thresholds of the BP neural network, so as to realize the training and testing of the motor to be tested. In the fault diagnosis of a certain motor, it is found that the fault diagnosis time and diagnosis rate of this method are significantly improved compared with the previous ones, and the diagnosis rate reaches 99% when the center deviation range is 0.3, the efficiency of the fault diagnosis model for parameter optimization through the improved PSO algorithm is higher, and the accuracy of the diagnosis results is further improved.

For the traditional routing algorithms in software-defined wireless sensor networks in the application of the problem of uneven energy consumption and low energy utilization, an energy balanced routing algorithm GDPSO based on adaptive learning factor and Gaussian perturbation particle swarm optimization is proposed. Software-defined wireless sensor networks will be the data flow logic centralized to the control plane to obtain global information, the algorithm in the PSO on the basis of the increased Gaussian perturbation and dynamically adjust the adaptive silver improved algorithm, comprehensively consider the node residual energy, global particle location information, neighboring nodes and other factors to construct the optimal fitness function to select the optimal cluster head, reasonable division of the global sub-cluster, in the inter-cluster communication, combined with the link energy consumption, the global particle energy residual, to build the shortest routing tree. The results show that the improved algorithm can be adjusted dynamically according to the network cycle routing strategy, equalize energy consumption and extend network implementation time.

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To solve the complex background problems (e.g. Noise interference, object overlap, and different illumination) that affect the classification performance on plant leaf images, this paper proposes an instance segmentation and classification method for plant leaf images based on IFPN SNMS CFFI-Mask R-CNN (ISC-MRCNN) and ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN). To obtain the foreground of plant leaf images, the lateral connection structure of the feature map pyramid in ISC-MRCNN fuses the feature maps of different depths, so that the network learns more detailed features. Then, the Soft Non-Maximum Suppression Algorithm is employed to improve the detection performance of overlapping objects. Next, the pooling method of integrating the continuous function can reduce the precision loss during the alignment of the mapping between the feature map and the original image. Finally, by constructing a mask filter layer, complex backgrounds are masked. To distinguish the similarity between plant leaf images, APS-DCCNN is used to classify the foreground images. In this process, the Support Vector Machine is used to replace softmax and then an Adaptive Chaotic Particle Swarm Algorithm is employed to optimize it. The experimental results show that compared with Mask R-CNN, the average precision of ISC-MRCNN has increased by 1.89% under different thresholds. The proposed method is suitable for the object detection and instance segmentation problems with complex background. Besides, compared with traditional CNN, the average precision of the classification results obtained by APS-DCCNN has improved by 1.59%. This has shown that the proposed method is suitable for the classification of plant leaves.

Recently, the particle swarm algorithm (PSO) has demonstrated its effectiveness in solving multi-objective optimization problems. However, the performance of most existing multi-objective particle swarm algorithms depends largely on the global or individual best particles. Moreover, due to the rapid convergence of PSO in single objective optimization problems, PSO is prone to poorly distributed indicators when dealing with multi-objective optimization problems. To solve the above problems, we propose a multi-objective competitive particle swarm algorithm based on vector angles (VaCSO). Firstly, in order to remove the influence of global best particles or individual best particles on the algorithm, the competition mechanism is used. Secondly, in order to increase the diversity of solutions while maintaining the convergence of the algorithm, the population is clustered into two populations. Population 1 mainly considers the convergence of the solution in the offspring generation strategy. As a supplement, population 2 adds a new offspring generation strategy to maintain the distribution of the solution, and we innovatively proposed a three-particle competition to improve the distribution and diversity of particle swarms. Finally, based on vector angle information, we consider auxiliary learning to optimize the population gap, so as to improve the distribution of the algorithm. We have established two sets of comparative experiments to test the performance of VaCSO. We compared VaCSO with the currently popular multi-objective particle swarm optimizers and multi-objective evolutionary algorithms. Experimental results show that VaCSO has an excellent performance in convergence and distribution, and has a significant effect in optimizing quality.

Multi-objective optimization problems can be divided into continuous multi-objective optimization problems and discrete multi-objective optimization problems, and discrete multi-objective optimization is not universal. In practical applications, there are many discrete multi-objective optimization problems. The solution of different problems needs to design different evolutionary multi-objective algorithms according to their specific conditions. The threshold in the traditional multi-objective optimization of wireless network is a preset constant. It has relatively poor performance in the synthesis of images with different noise levels. An adaptive wireless network multi-objective optimization algorithm based on image synthesis is proposed. Based on the maximum inter class variance and maximum peak signal to noise ratio (SNR), an adaptive wireless network multi-objective optimization algorithm is established. The accuracy and noise immunity of image synthesis are also considered. In order to avoid the effect of threshold increase on algorithm efficiency, multi-objective optimization algorithm is introduced into the algorithm. Experiments show that the method proposed in this paper is accurate and robust and has good universality for the synthesis of different noise images.

In the railway station scheduling system under the water-rail combined transport mode, how to reasonably allocate equipment resources according to the operation tasks, improve the efficiency of mechanical loading and unloading, and reduce the cost of loading and unloading operations, has become an urgent problem in the process of building a modern port. In this paper, a railway station scheduling algorithm based on multi-objective particle swarm optimization algorithm (RSSAMOPSO) was designed. The multi-objective particle swarm algorithm was used to solve the multi-objective optimization problem of railway station scheduling. Aiming at the problems of decreasing con-vergence speed and the lost of the diversity of the solutions, a compression factor and an adaptive mutation operator are introduced. The improved multi-objective particle swarm algorithm (IM_MOPSO) is combined with the compression factor and adaptive mutation operator to obtain the optimal operation sequence and various equipment task allocation schemes. Experimental results show that the above method has a large practical significance and practical value for improving the efficiency of mechanical loading and unloading in the railway yard and reducing the operating cost of the railway yard.

In order to solve the problem that, the hyper-parameters of the existing random forest-based classification prediction model depend on empirical settings, which leads to unsatisfactory model performance. We propose a based on adaptive particle swarm optimization algorithm random forest model to optimize data classification and an adaptive particle swarm algorithm for optimizing hyper-parameters in the random forest to ensure that the model can better predict unbalanced data. Aiming at the premature convergence problem in the particle swarm optimization algorithm, the population is adaptively divided according to the fitness of the population, and an adaptive update strategy is introduced to enhance the ability of particles to jump out of the local optimum. The main steps of the model are as follows: Normalize the data set, initialize the model on the training set, and then use the particle swarm optimization algorithm to optimize the modeling process to establish a classification model. Experimental results show that our proposed algorithm is better than traditional algorithms, especially in terms of F1-Measure and ACC evaluation standards. The results of the six-keel imbalanced data set demonstrate the advantages of our proposed algorithm.

How to realize the unity of safety, comfort, and economy of building structures has been a hot spot of concern in the field of construction engineering. This paper searches for optimal particles using a hybrid optimization strategy and optimizes the weights of each index in the fitness function into the same interval. Then dynamic inertia weights are used to improve the performance of the algorithm, and an enhanced adaptive particle swarm algorithm is obtained. After selecting the optimization variables for the building design, the objective function and constraints are designed, and the improved particle swarm algorithm is used to solve the optimal design of the intelligent building. The total weight of the target building structure was reduced after optimization, and 47.04% of the building materials were saved. The outer diameter of the steel pipe concrete at the lowest level of the building increases from 1.73m to 2.06m after optimization, which fulfills the law of column change in building design. It has also been found that the wind resistance of the optimized building structure has improved. This paper provides a reliable basis for the application of adaptive algorithms in building design optimization, and the proposed method also provides an effective reference for the field of construction engineering.

In multi-objective particle swarm optimization, achieving a balance between solution convergence and diversity remains a crucial challenge. To cope with this difficulty, this paper proposes a novel multi-objective particle swarm algorithm, called ASDMOPSO, which aims to improve the optimization efficiency through the angular division of the archive and the dynamic update strategy. The algorithm efficiently classifies non-dominated solutions by dividing the external archive region into equal angles, thus achieving fine management and diversity maintenance of solutions during the optimization process. When the external archive overflows, the algorithm removes the solution in the highest density region using the congestion distance metric. At the same time, the research presents a multi-stage initialization approach. This method splits the random population into two subpopulations. Subsequently, a genetic algorithm and a differential evolutionary algorithm are utilized for optimization purposes in each subpopulation, respectively. As a result, the quality of the initial population is enhanced. To explore the solution space more efficiently, this paper designs a dynamic flight parameter adjustment technique. This technique balances exploration and exploitation by adjusting the optimization algorithm parameters in real time. The proposed algorithm is compared with several representative multi-objective optimization algorithms on 22 benchmark functions, and statistical tests, sensitivity analysis, and complexity analysis are conducted. The experimental results show that the ASDMOPSO algorithm is more competitive than other comparison algorithms, with significantly improved optimization efficiency. For example, on the ZDT4 test function, its average IGD value is 0.032, outperforming the standard PSO algorithm and surpassing all other comparison algorithms, thereby validating the algorithm's superiority in complex multi-objective optimization problems.

An adaptive immune-inspired multi-objective algorithm with multiple differential evolution strategies

To improve distribution and convergence of the obtained solution set in constrained multi-objective optimization problems, this paper presents an adaptive ε-constraint multi-objective evolutionary algorithm based on decomposition and differential evolution (ε-MOEA/D-DE). First, an adaptive ε-constraint strategy based on both evolution generation and constraint violation is designed to make better use of excellent evolution individuals and improve population diversity. Then, an adaptive differential evolution (DE) mutation strategy with full utilization of infeasible individuals is proposed to increase search efficiency and avoid falling into the local optimum. Finally, a replacement mechanism is suggested to take advantage of the infeasible individuals in the population with better objective function values and constraint violation degree, and thus both diversity and convergence are well coordinated. A comparative experiment with four other excellent constrained multi-objective algorithms was implemented on standard constrained multi-objective optimization problems (CF series), and the results showed that the diversity and convergence of our algorithm were both improved.