Swarm Intelligence Optimization Algorithm Research Articles

Robot Time-Optimal Trajectory Planning Based on Quintic Polynomial Interpolation and Improved Harris Hawks Algorithm

Time-optimal trajectory planning is one of the most important ways to improve work efficiency and reduce cost and plays an important role in practical application scenarios of robots. Therefore, it is necessary to optimize the running time of the trajectory. In this paper, a robot time-optimal trajectory planning method based on quintic polynomial interpolation and an improved Harris hawks algorithm is proposed. Interpolation with a quintic polynomial has a smooth angular velocity and no acceleration jumps. It has widespread application in the realm of robot trajectory planning. However, the interpolation time is usually obtained by testing experience, and there is no unified criterion to determine it, so it is difficult to obtain the optimal trajectory running time. Because the Harris hawks algorithm adopts a multi-population search strategy, compared with other swarm intelligent optimization algorithms such as the particle swarm optimization algorithm and the fruit fly optimization algorithm, it can avoid problems such as single population diversity, low mutation probability, and easily falling into the local optimum. Therefore, the Harris hawks algorithm is introduced to overcome this problem. However, because some key parameters in HHO are simply set to constant or linear attenuation, efficient optimization cannot be achieved. Therefore, the nonlinear energy decrement strategy is introduced in the basic Harris hawks algorithm to improve the convergence speed and accuracy. The results show that the optimal time of the proposed algorithm is reduced by 1.1062 s, 0.5705 s, and 0.3133 s, respectively, and improved by 33.39%, 19.66%, and 12.24% compared with those based on particle swarm optimization, fruit fly algorithm, and Harris hawks algorithms, respectively. In multiple groups of repeated experiments, compared with particle swarm optimization, the fruit fly algorithm, and the Harris hawks algorithm, the computational efficiency was reduced by 4.7019 s, 1.2016 s, and 0.2875 s, respectively, and increased by 52.40%, 21.96%, and 6.30%. Under the optimal time, the maximum angular displacement, angular velocity, and angular acceleration of each joint trajectory meet the constraint conditions, and their average values are only 75.51%, 38.41%, and 28.73% of the maximum constraint. Finally, the robot end-effector trajectory passes through the pose points steadily and continuously under the cartesian space optimal time.

Application of Swarm Intelligence Optimization Algorithm in Logistics Delivery Path Optimization under the Background of Big Data

Traditional logistics delivery route optimization algorithm has some problems such as long time to find the optimal route. Based on this, this paper discusses swarm intelligence optimization algorithm and logistics delivery route optimization. To solve the logistics vehicle routing problem, considering that the basic ACO (ant colony optimization) has the disadvantages of slow convergence speed and easy to fall into local optimum, this paper proposes a new hybrid population optimization algorithm and applies it to VRPTW (vehicle routing problem with time windows). In addition, the concept of crowding degree in AFSA (artificial fish swarm algorithm) is introduced into ACO. In the early stage of the optimization process, a strong crowding degree limit is set to ensure that most ants are not affected by pheromone concentration to conduct random optimization. The simulation results show that the AC (accuracy rate) of this algorithm is 95.08%, which is higher than the traditional PSO (particle swarm optimization) algorithm and general heuristic algorithm. The hybrid algorithm can effectively improve the optimization efficiency of VRPTW, lay a foundation for solving large-scale VRPTW, and provide new research ideas and methods. At the same time, the results fully show that the algorithm in this paper has certain advantages in performance, and it can be applied to logistics delivery route optimization.

Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm

The quality of unmanned surface vehicle (USV) local path planning directly affects its safety and autonomy performance. The USV local path planning might easily be trapped into local optima. The swarm intelligence optimization algorithm is a novel and effective method to solve the path-planning problem. Aiming to address this problem, a hybrid bacterial foraging optimization algorithm with a simulated annealing mechanism is proposed. The proposed algorithm preserves a three-layer nested structure, and a simulated annealing mechanism is incorporated into the outermost nested dispersal operator. The proposed algorithm can effectively escape the local optima. Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) rules and dynamic obstacles are considered as the constraints for the proposed algorithm to design different obstacle avoidance strategies for USVs. The coastal port is selected as the working environment of the USV in the visual test platform. The experimental results show the USV can successfully avoid the various obstacles in the coastal port, and efficiently plan collision-free paths.

An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism.

The Harris hawks optimization (HHO) algorithm is a new swarm-based natural heuristic algorithm that has previously shown excellent performance. However, HHO still has some shortcomings, which are premature convergence and falling into local optima due to an imbalance of the exploration and exploitation capabilities. To overcome these shortcomings, a new HHO variant algorithm based on a chaotic sequence and an opposite elite learning mechanism (HHO-CS-OELM) is proposed in this paper. The chaotic sequence can improve the global search ability of the HHO algorithm due to enhancing the diversity of the population, and the opposite elite learning can enhance the local search ability of the HHO algorithm by maintaining the optimal individual. Meanwhile, it also overcomes the shortcoming that the exploration cannot be carried out at the late iteration in the HHO algorithm and balances the exploration and exploitation capabilities of the HHO algorithm. The performance of the HHO-CS-OELM algorithm is verified by comparison with 14 optimization algorithms on 23 benchmark functions and an engineering problem. Experimental results show that the HHO-CS-OELM algorithm performs better than the state-of-the-art swarm intelligence optimization algorithms.

Physical Stint Virtual Representation of Biomedical Signals With Wireless Sensors Using Swarm Intelligence Optimization Algorithm

Many people in society are facing problems related to health care, and diseases in the body are unable to be identified even with the presence of sensing technologies. The major reason for such failures in the identification process is that no virtual technologies are identified in the market. Most healthcare solicitations aim to design a particular application that provides information only about sensing values and fails to recall the virtual representation of that represented values. Therefore, this article provides an integration platform that connects the sensing devices with virtual reality/audio reality (VR/AR) techniques, which are applied in real time for detecting the presence of infections inside the body. In addition, one type of swarm intelligent algorithm is implemented in the recognition procedure with a modified fitness function and it is termed fruit fly optimization (FFO). The process of FFO provides much low layer perception, thus enhancing the output for smooth operation. To examine the real-time conditions, the projected AR/VR procedure is applied with biomedical sensors where three different case studies are separated. From the comparative numerical results, it is pragmatic that the proposed method provides better numerical results with 65% full-scale representations and less than 0.5 dB of distortion at 0.3% tuning force.

Transformer state prediction based on CSSA-BP neural network algorithm

As an important part of the power system, it is particularly important for the monitoring of the status of the power transformer in real time opearation. A transformer winding temperature prediction method through swarm intelligent optimization algorithm (CSSA) and BP neural network is proposed. Firstly, the CSSA-BP prediction model of the winding’s temperatur of dry-type transformer is established, and the existing 300 groups of data on the cloud platform are used for simulation analysis. The results show that, through the optimization by sparrow algorithm, the proposed model has a good prediction performance and is of great significance for realizing transformer winding temperature monitoring.

Human activity recognition using marine predators algorithm with deep learning

In the era of smart life, tracking human activities and motion can play a significant role in the advanced modern applications, such as the Internet of things (IoT), Internet of healthcare things (IoHT), smart homes, eldercare, and different health informatics-based applications. Human activity recognition (HAR) has the ability to expose abundant information collected from different devices (i.e., cameras or sensors) that can represent human motion and activities. The recent advances in artificial intelligence methods, including deep learning (DL) and swarm intelligence (SI) optimization algorithms, play a significant role in different applications. In this paper, we integrate the applications of both DL and SI to build a robust HAR system using wearable sensor data. A light feature extraction approach is developed using the residual convolutional network and a recurrent neural network (RCNN-BiGRU). To select the optimal feature set, we develop new feature selection methods based on the marine predator algorithm (MPA). Besides a basic version of the MPA, three binary variants are developed for this goal, called MPAS, MPAS10 and MPAV. We test the proposed MPA variants with comprehensive comparisons to several optimization algorithms using different evaluation indicators as well as statistical tests to ensure their performance quality. We conclude that MPAV recorded the best performance compared to other MPA variants as well as other compared methods.

An efficient hybrid swarm intelligence optimization algorithm for solving nonlinear systems and clustering problems

An efficient hybrid swarm intelligence optimization algorithm for solving nonlinear systems and clustering problems

Online Prediction Method for Power System Frequency Response Analysis Based on Swarm Intelligence Fusion Model

Instability at transient frequency caused by faults in complex power systems is one of the greatest threats to operational safety. By analyzing the frequency response of power system in real-time and adopting control strategies promptly, power system accidents can be efficiently prevented. While existing online analysis methods integrate physical-driven and data-driven methodologies, they do not effectively utilize frequency timing characteristics. Consequently, a swarm intelligence fusion model, which integrates physical-driven and data-driven methods, is proposed as an improved frequency response analysis method. The transient frequency affecting components are separated into primary state variables and system time series data based on the properties of the time sequence. To preserve the actual relationship of the electrical mechanism model, the system frequency response (SFR) model is used as the physical-driven method for the primary state variables of the system. The Long Short Term Memory (LSTM) network was used as the data-driven method to extract timing features and correct the SFR model’s prediction using the system time series data as input. The two methods are combined using the bootstrap mode to form the fusion model, and the structure of the model is optimized using an improved sparrow search algorithm (ISSA), a swarm intelligence optimization algorithm. The model structure is adapted autonomously, implementing a method for online frequency response analysis. The simulation on the New England 39-bus system has verified that the method can quickly and accurately calculate the dynamic process of frequency response after a large-scale disturbance.

Application of Improved Monarch Butterfly Optimization for Parameters’ Optimization

The reasonable selection of cutting parameters in the machining process is of great significance to improve productivity, reduce production costs, and improve the quality of parts. However, due to the complexity of cutting parameter model optimization, most factories currently use experience or refer to relevant manuals to select the value of cutting parameters in production. In order to avoid and minimize abnormalities, they usually select more experienced and conservative values, and often do not select reasonable cutting parameters, which is not conducive to improving productivity, reducing production costs, and improving the quality of parts. Therefore, the research on cutting parameter optimization has important theoretical value and application value. In this paper, in order to find the optimal cutting parameters, the cutting model is solved by the improved monarch butterfly optimization (IMBO) algorithm, and the optimized cutting parameters are obtained. By establishing the mathematical model of cutting, the constraint conditions of actual machining are introduced into the model. In order to solve the model, some ideas of particle swarm optimization (PSO) and differential evolution (DE) are added to the traditional monarch butterfly optimization (MBO) algorithm. The MBO algorithm is improved to deal with multiobjective optimization problems. The IMBO algorithm is used to optimize the cutting model. The experiment shows that the optimized cutting parameters can significantly reduce production cost and maintain high production efficiency. Compared with NSGA‐II algorithm and other swarm intelligence optimization algorithms, it shows that the IMBO algorithm has certain advantages in multiobjective optimization.

An Improved Sparrow Search Algorithm and Its Application in HIFU Sound Field.

The sparrow search algorithm (SSA) is a novel swarm intelligence optimization algorithm. It has a fast convergence speed and strong global search ability. However, SSA also has many shortcomings, such as the unstable quality of the initial population, easy to fall into the local optimal solution, and the diversity of the population decreases with the iterative process. In order to solve these problems, this paper proposes an improved sparrow search algorithm (ISSA). ISSA uses Chebyshev chaotic map and elite opposition-based learning strategy to initialize the population and improve the quality of the initial population. In the process of producer location update, dynamic weight factor and Levy flight strategy are introduced to avoid falling into a local optimal solution. The mutation strategy is applied to the scrounger location update process, and the mutation operation is performed on individuals to increase the diversity of the population. In order to verify the feasibility and effectiveness of ISSA, it is tested on 23 benchmark functions. The results show that compared with other seven algorithms, ISSA has higher convergence accuracy, faster convergence speed, and stronger stability. Finally, ISSA is used to optimize the sound field of high-intensity focused ultrasound (HIFU). The results show that ISSA can effectively improve the focusing performance and reduce the influence of sound field sidelobe, which is of great benefit for HIFU treatment.

Research on Chaotic Chimp Optimization Algorithm Based on Adaptive Tuning and Its Optimization for Engineering Application

The original Chimp Optimization Algorithm has disadvantages such as slow convergence, the tendency to fall into local optima, and low accuracy in finding the best. To alleviate the existing problems, a chaotic chimp optimization algorithm based on adaptive tuning is proposed. First, sine chaos mapping was used to initialize the chimpanzee population and enhance the quality and diversity of the initialized population. Then the global search capability and local exploitation capability of the optimization algorithm at iteration are enhanced by improving the convergence factor f and dynamically changing the number of chimpanzee precedence echelons. Finally, 10 benchmark functions are used to test the optimization‐seeking performance of the Improved Chimp Optimization Algorithm, while an engineering design optimization problem is introduced to compare the experimental results with other swarm intelligence optimization algorithms. The Improved Chimp Optimization Algorithm is shown to have better convergence and solution accuracy, resulting in an improvement in the global optimization‐seeking capability of the original Chimp Optimization Algorithm.

Thermal Properties Reconstruction and Temperature Fields in Asphalt Pavements: Inverse Problem and Optimisation Algorithms

A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement. The calculation of each layer only needs four iterations to achieve convergence. Furthermore, in order to improve the calculation accuracy a swarm intelligence optimization algorithm is also exploited to inversely analyze the laws by which the thermal physical parameters of the asphalt pavement materials change with temperature. Using the basic cuckoo and the gray wolf algorithms, an adaptive hybrid optimization algorithm is obtained and used to determine the relationship between the thermal diffusivity of two types of asphalt pavement materials and the temperature. As shown by the results, the prediction accuracy achievable with this approach is higher than that of the linear model.

FineAP: Fine-Grained Access Point Deployment Strategy for 60 GHz Millimeter-Wave Wireless Networks

Traditional access point (AP) deployment is based on the principle of minimum area coverage, which takes the AP signal range as the radius and performs a geometric calculation to determine the deployment location. These methods lack support for various contexts and requirements, making them only useful in a conventional rectangular environment. As a result, we design a fine-grained AP deployment (FineAP) strategy, which first uses ray-tracing to determine the signal distribution of various locations in the environment, and then uses a hybrid swarm intelligence optimization algorithm based on the principle of signal matrix maximization to determine the AP deployment location. When compared to traditional deployment methods, FineAP can handle a variety of application scenarios with close to upbound performance and the least minimum of overhead.

A Whale Optimization Algorithm with Distributed Collaboration and Reverse Learning Ability

Due to the development of digital transformation, intelligent algorithms are getting more and more attention. The whale optimization algorithm (WOA) is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems. However, with the increased dimensions, higher requirements are put forward for algorithm performance. The double population whale optimization algorithm with distributed collaboration and reverse learning ability (DCRWOA) is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems. In the DCRWOA algorithm, the novel double population search strategy is constructed. Meanwhile, the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area. Numerical experiments are carried out using standard test functions with different dimensions (10, 50, 100, 200). The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algorithm. The results show that the DCRWOA algorithm has higher optimization accuracy and stability, and the convergence speed is significantly improved. Therefore, the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.

Muti-Fusion Swarm Intelligence Optimization Algorithm in Base Station Coverage Optimization Problems

Muti-Fusion Swarm Intelligence Optimization Algorithm in Base Station Coverage Optimization Problems

Improved Remora Optimization Algorithm with Mutualistic Strategy for Solving Constrained Engineering Optimization Problems

Recently, a new swarm intelligence optimization algorithm called the remora optimization algorithm (ROA) was proposed. ROA simulates the remora’s behavior of the adsorption host and uses some formulas of the sailfish optimization (SFO) algorithm and whale optimization algorithm (WOA) to update the solutions. However, the performance of ROA is still unsatisfactory. When solving complex problems, ROA’s convergence ability requires further improvement. Moreover, it is easy to fall into local optimization. Since the remora depends on the host to obtain food and optimize ROA performance, this paper introduces the mutualistic strategy to strengthen the symbiotic relationship between the remora and the host. Meanwhile, chaotic tent mapping and roulette wheel selection are added to further improve the algorithm’s performance. By incorporating the above improvements, this paper proposes an improved remora optimization algorithm with a mutualistic strategy (IROA) and uses 23 benchmark functions in different dimensions and CEC2020 functions to validate the performance of the proposed IROA. Experimental studies on six classical engineering problems demonstrate that the proposed IROA has excellent advantages in solving practical optimization problems.

Defect Detection Image Processing Technology Based on Swarm Intelligence Optimization Algorithm

The swarm intelligence optimization algorithm has obtained good results in practical application in the field of image processing with defect detection, and it has become the focus and hot spot of attention and research in the field of image processing. In this paper, the application of ALO as the representative of the relevant swarm intelligence optimization algorithm is studied to address the problems and shortcomings of image processing technology in the field of object defect detection. By extracting typical defect detection image samples, the effect of the application of the algorithm in sample processing is systematically studied. In addition, the introduction of perturbation strategy and inertia weights in ALO effectively improves the search performance of the algorithm. Finally, this paper analyzes the performance comparison between the commonly used defect detection image processing techniques and the algorithm in this paper by establishing comparative verification experiments. The experimental results show that the image processing strategy constructed in this paper has significant application advantages in the dimensions of image enhancement and image processing applicability.

Damage Identification Method and Uncertainty Analysis of Beam Structures Based on SVM and Swarm Intelligence Algorithm

A two-stage damage identification method for beam structures based on support vector machine and swarm intelligence optimization algorithms is proposed. First, the frequencies and mode shapes of the beam structure are obtained using the smooth orthogonal decomposition method, and the normalized modal curvature is calculated as the input of a pre-trained support vector machine to determine the damage location. Then, the stiffness loss at the damaged location of the structure is calculated using swarm intelligence algorithms. The fitness function is the sum of the residual squares of the frequencies of the damaged structure identified by the smooth orthogonal decomposition method and the frequencies calculated for each iteration of the intelligent optimization algorithm. Numerical examples of a damaged simply supported beam structure are used to verify the damage identification performance of the two-stage method. The accuracy of the support vector machine model under different damage degrees and noise levels is studied using the Monte-Carlo method, and an uncertainty of the damage degree prediction value is studied by comparing the particle swarm optimization algorithm, moth-fire algorithm, and mayfly algorithm.

Structural system identification and damage detection using adaptive hybrid Jaya and differential evolution algorithm with mutation pool strategy

Engineering applications of swarm intelligence optimization algorithms have been widely employed to identify structural systems and damages owing to their merits of simplicity, flexibility, robustness etc., while they often suffer from the defects of slow efficiency, premature convergence or even trapping into local optima in solving the inverse problem of nonlinear optimization-based parameter identification with partial noise-contaminated measurements. To deal with this issue, an adaptive hybrid Jaya and differential evolution (AHJDE) algorithm is proposed based on Jaya algorithm and differential evolution (DE) by effectively combining the advantages of both algorithms. In the proposed AHJDE, four improvements including adaptive mutation strategy, dynamic mutation and crossover operators, sampling-based resizing search space and linear resizing population size are integrated. The effectiveness of the proposed method is verified using a numerical example of 20-DOF linear system by comparing its performance with particle swarm optimization, modified artificial bee colony algorithm, clustering tree seeds algorithm, improved butterfly optimization algorithm etc. considering known mass case and unknown mass case. In addition, numerical studies on a nonlinear single degree-of-freedom system with classical Bouc-Wen hysteretic model and improved Bouc-Wen model are implemented to investigate the applicability in the field of nonlinear system identification. Finally, a series of experimental tests on a five-story steel frame structure are conducted in the laboratory to further validate the performance of the proposed approach in damage identification. Identification results demonstrate the proposed AHJDE can accurately and effectively identify the unknown system parameters and damages with limited sensors and noise-polluted responses.