This paper proposes a parallel fish migration optimization algorithm with compact technology (PCFMO), and designs a sequential communication strategy between groups and a compact technology to save memory space. This paper uses 30 benchmark functions on CEC 2014 and three engineering problems as test benchmarks to compare the PCFMO algorithm with seven well-known algorithms, including Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Harris Hawks Optimization (HHO), Salp Swarm Algorithm (SSA), Fish Migration Optimization (FMO), Archimedes Optimization Algorithm (AOA) and Aquila Optimizer (AO). The experimental results show that the PCFMO algorithm achieves better results and has less space occupied by the population. The comprehensive performance of wireless sensor networks (WSN) is challenged by the battery energy limitation of sensor nodes distributed in specific areas. A proper cluster head set can manage energy consumption reasonably to extend the life cycle of the sensor network and increase the amount of message transmission. This paper takes the energy consumption in each round as the fitness function and adds the memory principle to the PCFMO algorithm to speed up the search for the optimal cluster head set. Compared with the LEACH, HFAPSO and PSO-C algorithms, the PCFMO algorithm based on the memory principle (PCFMO-Memory) can speed up the convergence of finding the optimal cluster head set, extend the life cycle of WSN and increase the amount of message transmission. 

Parallel fish migration optimization with compact technology based on memory principle for wireless sensor networks

In this paper, the main subharmonic resonance of the Mathieu-Duffing system with a quintic oscillator under simple harmonic excitation, the route to chaos, and the bifurcation of the system under the influence of different parameters is studied. The amplitude-frequency and phase-frequency response equations of the main resonance of the system are determined by the harmonic balance method. The amplitude-frequency and phase-frequency response equations of the steady solution to the system under the combined action of parametric excitation and forced excitation are obtained by using the average method, and the stability conditions of the steady solution are obtained based on Lyapunov's first method. The necessary conditions for heteroclinic orbit cross section intersection and chaos of the system are given by the Melnikov method. Based on the separation of fast and slow variables, the bifurcation phenomena of the system under different conditions are obtained. The amplitude-frequency characteristics of the total response of the system under different excitation frequencies are investigated by analytical and numerical methods, respectively, which shows that the two methods achieve consistency in the trend. The influence of fractional order and fractional derivative term coefficient on the amplitude-frequency response of the main resonance of the system is analyzed. The effects of nonlinear stiffness coefficient, parametric excitation term coefficient, and fractional order on the amplitude-frequency response of subharmonic resonance are discussed. Through analysis, it is found that the existence of parametric excitation will cause the subharmonic resonance of the Mathieu-Duffing oscillator to jump. Finally, the subcritical and supercritical fork bifurcations of the system caused by different parameter changes are studied. Through analysis, it is known that the parametric excitation coefficient causes subcritical fork bifurcations and fractional order causes supercritical fork bifurcations.

Resonance and bifurcation of fractional quintic Mathieu-Duffing system.

The use of DC motors is now common because of its advantages and has become an important necessity in helping human activities. Generally, motor control is designed with PID control. The main problem that is often discussed in PID is parameter tuning, namely determining the value of the Kp, Ki, and Kd parameters in order to obtain optimal system performance. In this study, one method for tuning PID parameters on a DC motor will be used, namely the Particle Swarm Optimization (PSO) method. Parameter optimization using the PSO method has stable results compared to other methods. The results of tuning the PID controller parameters using the PSO method on the MATLAB Simulink obtained optimal results where the value of Kp = 8.9099, K = 2.1469, and Kd = 0.31952 with the value of rise time of 0.0740, settling time of 0.1361 and overshoot of 0. Then the results of hardware testing by entering the PID value in the Arduino IDE software produce a stable motor speed response where Kp = 1.4551, Ki= 1.3079, and Kd = 0.80271 with a rise time value of 4.3296, settling time of 7.3333 and overshoot of 1.

/pdf/particle-swarm-optimization-pso-tuning-of-pid-control-on-dc-uuknfilw.pdf

Particle Swarm Optimization (PSO) Tuning of PID Control on DC Motor

A single strategy used in the firefly algorithm (FA) cannot effectively solve the complex optimal scheduling problem. Thus, we propose the FA with division of roles (DRFA). Herein, fireflies are divided into leaders, developers, and followers, while a learning strategy is assigned to each role: the leader chooses the greedy Cauchy mutation; the developer chooses two leaders randomly and uses the elite neighborhood search strategy for local development; the follower randomly selects two excellent particles for global exploration. To improve the efficiency of the fixed step size used in FA, a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages. Role division can balance the development and exploration ability of the algorithm. The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems. The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of optimal scheduling of cascade reservoirs.

Firefly algorithm with division of roles for complex optimal scheduling

Wear and tear are usually caused by various factors, which reduce the life span of a mechanical part. In the control engineering of an Electrohydraulic actuator system, the wear and tear can be caused by the system or the controller itself. This article examines the chattering effect that occurs during the sliding mode controller (SMC) design, and its effect on the nonlinear electrohydraulic actuator (EHA) system. To examine the chattering phenomenon, signum function is first applied on the switching function of the SMC. Then, parameters of the controller are obtained using single objective particle swarm optimization (PSO) method. These parameters are then applied to the switching function with hyperbolic tangent function. Lastly, the performance of both functions is analysed and compared based on graph and numerical data. From the output data, chattering phenomenon generated on the signum function is greatly eliminated by using hyperbolic tangent function.

Chattering Analysis of an Optimized Sliding Mode Controller for an Electro-Hydraulic Actuator System

Proportional Integral Derivative (PID) controller is one of the most classical controllers, which has a good performance in industrial applications. The traditional PID parameter tuning relies on experience, however, the intelligent algorithm is used to optimize the controller, which makes it more convenient. Fish Migration Optimization (FMO) is an excellent algorithm that mimics the swim and migration behaviors of fish biology. Especially, the formulas for optimization were obtained from biologists. However, the optimization effect of FMO for PID control is not prominent, since it is easy to skip the optimal solution with integer-order velocity. In order to improve the optimization performance of FMO, Fractional-Order Fish Migration Optimization (FOFMO) is proposed based on fractional calculus (FC) theory. In FOFMO, the velocity and position are updated in fractional-order forms. In addition, the fishes should migration back to a position which is more conducive to survival. Therefore, a new strategy based on the global best solution to generate new positions of offsprings is proposed. The experiments are performed on benchmark functions and PID controller. The results show that FOFMO is superior to the original FMO, and the PID controller tuned by FOFMO is more robust and has better performance than other contrast algorithms.

/pdf/optimal-design-and-simulation-for-pid-controller-using-428a2gmt8o.pdf

Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm

QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm generalized differential evolution (DE) algorithm to matrix form. QUATRE was originally designed for a continuous search space, but many practical applications are binary optimization problems. Therefore, we designed a novel binary version of QUATRE. The proposed binary algorithm is implemented using two different approaches. In the first approach, the new individuals produced by mutation and crossover operation are binarized. In the second approach, binarization is done after mutation, then cross operation with other individuals is performed. Transfer functions are critical to binarization, so four families of transfer functions are introduced for the proposed algorithm. Then, the analysis is performed and an improved transfer function is proposed. Furthermore, in order to balance exploration and exploitation, a new liner increment scale factor is proposed. Experiments on 23 benchmark functions show that the proposed two approaches are superior to state-of-the-art algorithms. Moreover, we applied it for dimensionality reduction of hyperspectral image (HSI) in order to test the ability of the proposed algorithm to solve practical problems. The experimental results on HSI imply that the proposed methods are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm

To this day, the prevention of coronavirus disease is still an arduous battle. Medical imaging technology has played an important role in the fight against the epidemic. This paper is to perform feature selection on the CT image feature sets used for COVID-19 detection to improve the speed and accuracy of detection. In this work, the population-based intelligent optimization algorithm Aquila optimizer is used for feature selection. This feature selection method uses an S-shaped transfer function to process continuous values and convert them into binary form. And when the performance of the updated solution is not good, a new mutation strategy is proposed to enhance the convergence effect of the solution. Through the verification of two CT image sets, the experimental results show that the use of the S-shaped transfer function and the proposed mutation strategy can effectively improve the effect of feature selection. The prediction accuracy of the features selected by this method on the two open datasets is 99.67% and 99.28%, respectively. 

A Novel Feature Selection Algorithm Based on Aquila Optimizer for COVID-19 Classification

Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm

Arithmetic Optimization Algorithm (AOA) has simple structure, few parameters and is easy to implement. It utilizes the distribution behavior of the main arithmetic operators in mathematics. The Multiplication and Division operators are used for exploration, while the Subtraction and Addition are used for exploitation. In this manuscript, the AOA algorithm was converted into binary form with the Multiplication Mathematical Optimizer Operator (MOO) redesigned for better exploration. Then, four families of transfer functions are used in the binary AOA (BAOA). Moreover, in order to further improve the performance, the parallel mechanism is introduced to the BAOA and proposed the Parallel Binary AOA (PBAOA) algorithm. The proposed algorithms are applied for feature selection problem on 10 low-dimensional and 10 high-dimensional datasets from UCI and scikit-feature repository. The results show that the proposed BAOA and PBAOA algorithms are superior to the classical and state-of-the-art algorithms. The Z-shaped transfer functions are more suitable for the proposed algorithms on low-dimensional datasets, while the V-shaped transfer functions are more suitable for high-dimensional datasets. Moreover, no matter what transfer function is used, the parallel mechanism is effective for BAOA. 

Zhongjie Zhuang

Papers

Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm

A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm

A Novel Feature Selection Algorithm Based on Aquila Optimizer for COVID-19 Classification

Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm

Parallel binary arithmetic optimization algorithm and its application for feature selection