Enhancing artificial bee colony algorithm with multi-elite guidance

Application of artificial bee colony algorithm based on homogenization mapping and collaborative acquisition control in network communication security

In order to optimize the spectrum allocation strategy of existing wireless communication networks and improve information transmission efficiency and data transmission security, this study uses the independent correlation characteristics of chaotic time series to simulate the collection and control strategy of bees, and proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection control. Furthermore, it proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection and control. The method begins by establishing a composite system of uniformly distributed Chebyshev maps. In the neighborhood intervals where the nectar sources are firmly connected and relatively independent, the algorithm then conducts a chaotic traversal search. The research results demonstrated the great performance of the suggested algorithm in each test function as well as the positive effects of the optimization search. The network throughput rate was over 300 kbps, the quantity of security service eavesdropping was below 0.1, and the spectrum utilization rate of the algorithm-based allocation method could be enhanced to 0.8 at the most. Overall, the performance of the proposed algorithm outperformed the comparison algorithm, with high optimization accuracy and a significant amount of optimization. This is favorable for the efficient use of spectrum resources and the secure transmission of communication data, and it encourages the development of spectrum allocation technology in wireless communication networks.

A Parkinson's Auxiliary Diagnosis Algorithm Based on a Hyperparameter Optimization Method of Deep Learning

Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the early stage of the disease. In this paper, a Parkinson's auxiliary diagnosis algorithm based on a hyperparameter optimization method of deep learning is proposed for the Parkinson's diagnosis. The diagnosis system uses ResNet50 to achieve feature extraction and Parkinson's classification, mainly including speech signal processing part, algorithm improvement part based on Artificial Bee Colony algorithm (ABC) and optimizing the hyperparameters of ResNet50 part. The improved algorithm is called Gbest Dimension Artificial Bee Colony algorithm (GDABC), proposing "Range pruning strategy" which aims at narrowing the scope of search and "Dimension adjustment strategy" which is to adjust gbest dimension by dimension. The accuracy of the diagnosis system in the verification set of Mobile Device Voice Recordings at King's College London (MDVR-CKL) dataset can reach more than 96%. Compared with current Parkinson's sound diagnosis methods and other optimization algorithms, our auxiliary diagnosis system shows better classification performance on the dataset within limited time and resources.

Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm

In electrical impedance tomography (EIT) detection of industrial two-phase flows, the Gauss-Newton algorithm is often used for imaging. In complex cases with multiple bubbles, this method has poor imaging accuracy. To address this issue, a new algorithm called the artificial bee colony-optimized radial basis function neural network (ABC-RBFNN) is applied to industrial two-phase flow EIT for the first time. This algorithm aims to enhance the accuracy of image reconstruction in electrical impedance tomography (EIT) technology. The EIDORS-v3.10 software platform is utilized to generate electrode data for a 16-electrode EIT system with varying numbers of bubbles. This generated data is then employed as training data to effectively train the ABC-RBFNN model. The reconstructed electrical impedance image produced from this process is evaluated using the image correlation coefficient (ICC) and root mean square error (RMSE) criteria. Tests conducted on both noisy and noiseless test set data demonstrate that the ABC-RBFNN algorithm achieves a higher ICC value and a lower RMSE value compared to the Gauss-Newton algorithm and the radial basis function neural network (RBFNN) algorithm. These results validate that the ABC-RBFNN algorithm exhibits superior noise immunity. Tests conducted on bubble models of various sizes and quantities, as well as circular bubble models, demonstrate the ABC-RBFNN algorithm's capability to accurately determine the size and shape of bubbles. This outcome confirms the algorithm's generalization ability. Moreover, when experimental data collected from a 16-electrode EIT experimental device is employed as test data, the ABC-RBFNN algorithm consistently and accurately identifies the size and position of the target. This achievement establishes a solid foundation for the practical application of the algorithm.

Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output

The aim of this study is to enhance the performance of a nonlinear three-rigid-link maneuver (RLM) in terms of trajectory tracking, disturbance and noise cancellation, and adaptability to joint flexibility. To achieve this, an optimized sliding mode controller with a proportional integral derivative surface (SMC-PID) is employed for maneuver control. An improved artificial bee colony algorithm with multi-elite guidance (MGABC) is utilized to obtain optimal values for the sliding surface and switching mode gain and attain the best performance for the robot maneuver system. The selection of the MGABC algorithm is based on its efficient exploration and exploitation techniques. The performance of the optimized SMC-PID robotic system is compared against other optimization algorithms found in existing literature, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), and Grey Wolf Optimizer (GWO). The implemented controller effectively reduces the tracking error to 0.00691 radians, eliminates chattering phenomena in the control effort, and demonstrates robustness against disturbances and noise. The controller ensures that the objective function (OBJF) is minimized, with 0.954% increase in OBJF under low disturbance and noise conditions and 14.55% under severe disturbance and noise conditions. Moreover, the optimized controller exhibits resilience to variations in payload mass analysis, with the percentage increase in OBJF values ranging from 5.726% under low uncertainty conditions to 18.887% under severe uncertainty conditions. Flexible-link maneuvers (FLM) offer advantages such as improved safety and increased operating speeds in real-world applications. In this study, we investigated the impact of joint flexibility on the performance of the FLM system. Our proposed controller demonstrated superior tracking performance, characterized by minimal vibrations in the movement of the end effector.

Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm

This study introduces and compares two optimization techniques, the basic Artificial Bee Colony (ABC) and the enhanced Artificial Bee Colony with multi-elite guidance (MGABC), for determining optimal gains of a Proportional-Integral-Derivative (PID) controller in a 3 degrees of freedom (DOF) rigid link manipulator (RLM) system. The objective function used in the optimization process is a novel function that is based on the well-known Lyapunov stability functions. This function is evaluated against established error-based objective functions commonly used in control systems. The convergence curves of the optimization process demonstrate that the MGABC algorithm outperforms the basic ABC algorithm by effectively exploring the search space and avoiding local optima. The evaluation of the controller's performance in trajectory tracking reveals the superiority of the Lyapunov-based objective function (LBF), with significant improvements over other objective functions such as IAE, ISE, ITAE, MAE and MRSE. The optimized system demonstrates robustness to diverse disturbance conditions and uncertainty in the mass of the payload, while also exhibiting adaptability to joints flexibility without inducing any vibrations in the movement of the end-effector. The proposed techniques and objective function offer promising avenues for the optimization of PID controllers in various robotic applications.

A Novel Swarm Intelligence Algorithm with a Parasitism-Relation-Based Structure for Mobile Robot Path Planning

A multi-swarm-evolutionary structure based on the parasitic relationship in the biosphere is proposed in this paper and, according to the conception, the Para-PSO-ABC algorithm (ParaPA), combined with merits of the modified particle swarm optimization (MPSO) and artificial bee colony algorithm (ABC), is conducted with the multimodal routing strategy to enhance the safety and the cost issue for the mobile robot path planning problem. The evolution is divided into three stages, where the first is the independent evolutionary stage, with the same evolution strategies for each swarm. The second is the fusion stage, in which individuals are evolved hierarchically in the parasitism structure. Finally, in the interaction stage, a multi-swarm-elite strategy is used to filter the information through a predefined cross function among swarms. Meanwhile, the segment obstacle-avoiding strategy is proposed to accelerate the searching speed with two fitness functions. The best path is selected according to the performance on the safety and consumption issues. The introduced algorithm is examined with different obstacle allocations and simulated in the real routing environment compared with some typical algorithms. The results verify the productiveness of the parasitism-relation-based structure and the stage-based evolution strategy in path planning.

A Modified Reptile Search Algorithm for Numerical Optimization Problems

The reptile search algorithm (RSA) is a swarm-based metaheuristic algorithm inspired by the encirclement and hunt mechanisms of crocodiles. Compared with other algorithms, RSA is competitive but still suffers from low population diversity, unbalanced exploitation and exploration, and the tendency to fall into local optima. To overcome these shortcomings, a modified variant of RSA, named MRSA, is proposed in this paper. First, an adaptive chaotic reverse learning strategy is employed to enhance the population diversity. Second, an elite alternative pooling strategy is proposed to balance exploitation and exploration. Finally, a shifted distribution estimation strategy is used to correct the evolutionary direction and improve the algorithm performance. Subsequently, the superiority of MRSA is verified using 23 benchmark functions, IEEE CEC2017 benchmark functions, and robot path planning problems. The Friedman test, the Wilcoxon signed-rank test, and simulation results show that the proposed MRSA outperforms other comparative algorithms in terms of convergence accuracy, convergence speed, and stability.

An improved artificial bee colony algorithm based on Bayesian estimation

Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. The numerical experiment results indicate that BEABC dominates its peers over most test functions, and the significant statistics show that the significant excellence rate of BEABC is $$76\%$$ 76 % in the overall comparison. In addition, to further test the performance of BEABC, seven multi-objective problems and two real-word optimization problems are solved. The comparison results show that BEABC can achieve better results than other EA competitors.

A novel coevolving differential evolution and its application in intelligent device-to-device communication systems

Due to the increasingly demand of wireless broadband applications in modern society, the device-to-device (D2D) communication technique plays an important role for improving communication spectrum efficiency and quality of service (QoS). This study focuses on the optimal allocation of link resource in D2D communication systems using intelligent approaches, in order to obtain optimal energy efficiency of D2D-pair users (DP) and also ensure communication QoS. To be specific, the optimal resource allocation (ORA) model for ensuring the cooperation between DP and cellular users (CU) is established, and a novel coding strategy of ORA model is also proposed. Then, for efficiently optimizing the ORA model, a novel swarm-intelligence-based algorithm called the dynamic topology coevolving differential evolution (DTC-DE) is developed, and the efficiency of DTC-DE is also tested by a comprehensive set of benchmark functions. Finally, the DTC-DE algorithm is employed for optimizing the proposed ORA model, and some state-of-the-art algorithms are also employed for comparison. Result of case study shows that the DTC-DE outperforms its competitors significantly, and the optimal resource allocation can be obtained by DTC-DE with robust performance.

A Novel Music Emotion Recognition Model Using Neural Network Technology

Music plays an extremely important role in people’s production and life. The amount of music is growing rapidly. At the same time, the demand for music organization, classification, and retrieval is also increasing. Paying more attention to the emotional expression of creators and the psychological characteristics of music are also indispensable personalized needs of users. The existing music emotion recognition (MER) methods have the following two challenges. First, the emotional color conveyed by the first music is constantly changing with the playback of the music, and it is difficult to accurately express the ups and downs of music emotion based on the analysis of the entire music. Second, it is difficult to analyze music emotions based on the pitch, length, and intensity of the notes, which can hardly reflect the soul and connotation of music. In this paper, an improved back propagation (BP) algorithm neural network is used to analyze music data. Because the traditional BP network tends to fall into local solutions, the selection of initial weights and thresholds directly affects the training effect. This paper introduces artificial bee colony (ABC) algorithm to improve the structure of BP neural network. The output value of the ABC algorithm is used as the weight and threshold of the BP neural network. The ABC algorithm is responsible for adjusting the weights and thresholds, and feeds back the optimal weights and thresholds to the BP neural network system. BP neural network with ABC algorithm can improve the global search ability of the BP network, while reducing the probability of the BP network falling into the local optimal solution, and the convergence speed is faster. Through experiments on public music data sets, the experimental results show that compared with other comparative models, the MER method used in this paper has better recognition effect and faster recognition speed.