Adaptive Particle Swarm Optimization Algorithm With Genetic Mutation Operation

In this article we describe a novel Adaptive particle swarm optimization (APSO) algorithm based on population diversity information. It is presented to solve the precocious convergence problem of particle swarm optimization algorithm. The APSO algorithm uses the information of the population diversity to adjust nonlinearly inertia weight. Velocity mutation factor and position interchange factor are both introduced and the global performance is clearly improved. The APSO algorithm is applied to optimization of parameters in the optimal model based on support vector machine (SVM). SVM is a popular classification method with many diverse applications. A novel Adaptive particle swarm optimization (APSO) based approach for parameter determination and feature selection of the SVM, termed APSO+SVM is developed. The illustrating example shows that the classification accuracy of APSO+SVM is higher than other traditional methods of classification, so using APSO+SVM method to classify is feasible and effective.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

An adaptive Particle Swarm Optimization algorithm for fire source identification of the utility tunnel fire

Due to the disadvantages of the canonical Particle Swarm Optimization (PSO), a new adaptive particle swarm optimization algorithm based on Sigmoid inertia weight was proposed. On the one hand, the premature computing formula of the particles was introduced so as to conduct the evolution process and enhance the convergent speed. On the other hand, the decision threshold of particles' focusing degree was employed in order to make the PSO adaptively adopt the inertia weight between linear decrease and nonlinear decrease based on Sigmoid function, which could effectively prevent the PSO from plunging into local minimum. The experimental results show that the proposed algorithm is feasible and effective.

Aiming at the control variables of reactive power optimization are discrete, and some parameters in the standard particle swarm optimization (PSO) algorithm need to be predefined by test, so the algorithm's practicability is restricted. For these reasons, an adaptive particle swarm optimization (APSO) algorithm is proposed by the authors. APSO introduces the self-adaptive tuning strategy and boundary constraint conditions can find the global optimal solution and solve the discrete variables. The reactive power optimization results of the standard IEEE-30-bus power system show that APSO is efficient than standard PSO. The global convergence accuracy and convergence stability is obviously improved compared with that of PSO.

Compensation for target speckle phase by use of the combination of the adaptive particle swarm optimization algorithm and the array detector method in heterodyne detection

Aiming at the problem of reliability redundancy design algorithm, this paper presents an adaptive particle swarm optimization algorithm with foraging behavior. In this method, the bacterial foraging process is introduced into the adaptive particle swarm optimization algorithm, which can give the function of particle movement and turning, improve the ability of local search and avoid premature behavior. Using the classical test function and the reliability redundancy allocation problem of avionics system, the results show that the improved algorithm has better reliability, higher precision and faster convergence speed.

The method of repeated trial and proofreading is generally used to the convention reducer design, but these methods is low efficiency and the size of the reducer is often large. Aiming the problems, this paper presents an adaptive particle swarm optimization algorithm with foraging behavior, in this method, the bacterial foraging process is introduced into the adaptive particle swarm optimization algorithm, which can provide the function of particle chemotaxis, swarming, reproduction, elimination and dispersal, to improve the ability of local search and avoid premature behavior. By test verification through typical function and the application of the optimization design in the structure of the reducer with discrete and continuous variables, the results are shown that the new algorithm has the advantages of good reliability, strong searching ability and high accuracy. It can be used in engineering design, and has a strong applicability.

Aiming at the trend of “S” growth in annual electricity consumption in Ningxia, the gray linear regression combined model (GLM) load forecasting method is adopted, which can improve the lack of exponential growth trend in linear regression model and the lack of linear factors in the GM(1,1) model. However, the traditional GLM model has theoretical defects in the process of solving parameters, and there are limitations in the application range. Therefore, this paper introduces an adaptive particle swarm optimization algorithm which is more efficient than the standard particle swarm optimization algorithm, and combines it with the gray linear regression combination model. The adaptive particle swarm optimization algorithm is used to solve the parameters of the GLM model, then carry out residual correction. Adaptive particle swarm optimization gray linear regression combination model (APSO-GLM) is proposed. The example analysis shows that the model overcomes the defects of the GM(1,1) model and the linear regression model. Compared with the single model, the prediction accuracy is higher and it has a wider application range than the traditional GLM model.

Energy optimization strategy of vehicle DCS system based on APSO algorithm

This paper presents a novel optimal H∞ tracking-based adaptive fuzzy neural network controller (HAFNNC) for a variable speed wind turbine. The main objective of the controller is to optimise the energy captured from the wind. In the presence of large uncertainties, H∞ control approach produces oscillatory phenomenon due to the higher needed gain. In order to reduce this gain, fuzzy neural network (FNN) with online adaptation of the parameters is used to estimate the uncertain parts of the system plant and hence enable a lower gain to be used. To eliminate the trade-offs between the H∞ tracking performance and the high gain at the control input, we have introduced a new method based on the modification of the output tracking error through the use of both the exponential function and the adaptive particle swarm optimisation (APSO) algorithm. The stability and effectiveness of the proposed method are proved by Lyapunov method and the simulations are given to demonstrate the performance of the proposed approach.

Online monitoring of underwater wireless power transfer (UWPT) system parameters is of great significance for improving system transmission efficiency and stability. This paper proposes a UWPT system parameter identification method based on the adaptive particle swarm optimization Algorithm (APSO) algorithm. By measuring the front-end port impedance, it enables online identification of important parameters such as parasitic parameters due to eddy current effect, load resistance, and mutual inductance, without bilateral communication. Additionally, it eliminates the need for solving the circuit matrix multi-parameter equations precisely, saving computational resources. The paper conducts a mathematical analysis of the UWPT system's eddy current effect model and constructs an optimization model for parameter identification. Finally, an experimental platform is established to verify the effectiveness of the proposed parameter identification method. The PSO algorithm and the proposed APSO algorithm are used for parameter identification of the UWPT system under different load and coil offset conditions. Experimental results show that under various operating conditions, the APSO algorithm achieves an average identification error of 0.94‰ and an average convergence count of 60. Compared to the traditional PSO algorithm, the APSO algorithm achieves higher accuracy and faster convergence speed in parameter identification.

In this paper, an adaptive particle swarm optimization algorithm based on cloud model (C-APSO) is proposed. In the suggested method, the velocities of the all particles are adjusted based on the strategy that a particle whose fitness value is nearer to the optimal particle will fly with smaller velocity. Considering the properties of randomness and stable tendency of a normal cloud model, a Y-conditional normal cloud generator is used to gain the inertial factors of the particles. The simulations of function optimization show that the proposed method has advantage of global convergence property and can effectively alleviate the problem of premature convergence.

This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator’s objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer–Rao lower bound (CRLB).

A proton exchange membrane fuel cell (PEMFC) is applied as dependable power source. According to the experiments data, the influence of PEMFC operating temperature on its output performance is analyzed. An optimum operation temperature trajectory is achieved by a means of effective informed adaptive particle swarm optimization algorithm (EIA-PSO) with better equilibrium characteristic between global search and local search. Furthermore, a PID controller is utilized for design of a PEMFC control system based on the characteristic of optimal operation temperature. It is verified that EIA-PSO can make the reference governor fit the optimum operation temperature trace with higher precision. According to the experimental testing of stability and track ability, it is realized that the optimum operation temperature trace with the condition of various loads can be tracked by employing proposed control system so that the optimal output performance of PEMFC can be completed. Therefore, the proposed control system will make significant influence on designing the real-time temperature control system of PEMFC.