A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems

This paper proposes a modified optimal PIDD2 controller for flexible-link manipulator. The single flexible link is modeled mathematically in which the flexible link and base rotation are modeled as stiff systems using Lagrange’s method. The system obtained as a result will have one degree of freedom. In the proposed work, the comparison of two types of controller, i.e., PID and PIDD2, is done for controlling the position and trajectory of the single-link manipulator. The main objective is to control the trajectory with minimum tip oscillation. The tuning of the controllers is done using the Ziegler–Nichols (Z-N) method and Dynamic Particle Swarm Optimization (DPSO) algorithm. The dynamic particle swarm optimization algorithm is an improved version of the particle swarm optimization algorithm which identifies and eliminates the dilemma of stagnation and local optima. The findings show that the PIDD2 controller with dynamically tuned parameters is better in controlling the position and trajectory of the single-link manipulator. All the simulations were performed on MATLAB–SIMULINK.

Risk prediction about investor portfolio holdings can provide powerful test of asset pricing theories. In this paper, we present dynamic Particle Swarm Optimization (PSO) algorithm to Markowitz portfolio selection problem, and improved the algorithm in pseudo code as well as implement in computer program. Furthermore in order to prevent blindness in operation and selection of investment, we tried to make risk least and seek revenue most in investment and so do in the program. As used in practice, it showed great application value.

Many problems in the real-world have more than one objective, with at least two objectives in conflict with one another. In addition, at least one objective changes over time. These kinds of problems are called dynamic multi-objective optimisation problems (DMOOPs). Studies have shown that both the quantum particle swarm optimisation (QPSO) and charged particle swarm optimisation (CPSO) algorithms perform well in dynamic environments, since they maintain swarm diversity. Therefore, this paper investigates the effect of using either QPSOs or CPSOs in the sub-swarms of the dynamic vector-evaluated particle swarm optimisation (DVEPSO) algorithm. These DVEPSO variations are then compared against the default DVEPSO algorithm that uses gbest PSOs and DVEPSO using heterogeneous PSOs that contain both charged and quantum particles. Furthermore, all of the aforementioned DVEPSO configurations are compared against the dynamic multi-objective optimisation (DMOPSO) algorithm that was the winning algorithm of a comprehensive comparative study of dynamic multi-objective optimisation algorithms. The results indicate that charged and quantum particles improve the performance of DVEPSO, especially for DMOOPs with a deceptive POF and DMOOPs with a non-linear POS.

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.

Cable-driven parallel robots (CDPRs) have been widely used in engineering fields because of their significant advantages including high load-bearing capacity, large workspace, and low inertia. However, the impact of convergence speed and solution accuracy of optimization approaches on optimal performances can become a key issue when it comes to the optimal design of CDPR applied to large storage space. An adaptive adjustment inertia weight particle swarm optimization (AAIWPSO) algorithm is proposed for the multi-objective optimal design of CDPR. The kinematic and static models of CDPR are established based on the principle of virtual work. Subsequently, two performance indices including workspace and dexterity are derived. A multi-objective optimization model is established based on performance indices. The AAIWPSO algorithm introduces an adaptive adjustment inertia weight to improve the convergence efficiency and accuracy of traditional particle swarm optimization (PSO) algorithm. Numerical examples demonstrate that final convergence values of the objective function by the AAIWPSO algorithm can almost be 14∼20% and 19∼40% higher than those by the PSO algorithm and genetic algorithm (GA) for the optimal design of CDPR with different configurations and masses of end-effectors, respectively.

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

Biometric models are typically designed a priori using limited number of samples acquired from complex environments that change in time during operations. Therefore, these models are often poor representatives of the biometric trait to be recognized. To circumvent this problem, ensemble of classifiers can be used to integrate solutions obtained from multiple diverse classifiers. In this paper, two dynamic particle swarm optimization (DPSO) algorithms are compared for the evolution of classifier ensembles during supervised incremental learning of newly-acquired data samples in video-based face recognition. Using the properties of these population-based optimization algorithms, an incremental DPSO learning strategy for adaptive classification systems (ACSs) is employed to evolve a pool of fuzzy ARTMAP classifiers while an heterogeneous ensemble is selected through a greedy search process that seeks to maximize both performance and diversity. The performance of dynamic niching PSO (DNPSO) and speciation PSO (SPSO) algorithms is assessed in terms of classification rate, resource requirements and diversity for different incremental learning scenarios of new data blocks extracted from real-world video streams. Simulation results indicate that both DPSO algorithms can efficiently create accurate ensembles while reducing computational complexity. In addition, directly selecting representative subswarm particles to form diversified classifier ensembles significantly reduces the computational complexity.

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.

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).

In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed, where a new velocity updating mechanism is designed to adjust the personal best position and the global best position according to a distance-based dynamic neighborhood to make full use of the population evolution information among the entire swarm. In addition, a novel switching learning strategy is introduced to adaptively select the acceleration coefficients and update the velocity model according to the searching state at each iteration, thereby contributing to a thorough search of the problem space. Furthermore, the differential evolution algorithm is successfully hybridized with the particle swarm optimization (PSO) algorithm to alleviate premature convergence. A series of commonly used benchmark functions (including unimodal, multimodal, and rotated multimodal cases) is utilized to comprehensively evaluate the performance of the DNSPSO algorithm. The experimental results demonstrate that the developed DNSPSO algorithm outperforms a number of existing PSO algorithms in terms of the solution accuracy and convergence performance, especially for complicated multimodal optimization problems.

Improved Adaptive PSO-based Gain Tuning for PID Controllers in Utility Boilers

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

There are some problems in the field of text clustering and particle swarm optimization (PSO) technology, for example, the researchers need to determine the appropriate number of clusters before clustering, the clustering results are sensitive to the initial cluster center, and the PSO algorithm has precocious convergence and easy to fall into local optimality defects. We propose a dynamic topology adaptive PSO (DTA-PSO) algorithm that combines the K-means algorithm PSO to realize automatic text clustering. In DTA-PSO, we adopt an improved comprehensive dynamic learning strategy from social group experience the source of the social experience of the particle group is not only the particle with the best performance among all its neighbors, according to the performance of all its neighbors and the relative position with the particle, the target position is calculated in the next iteration. We choose three data sets in the knowledge base of the machine learning open data set and part of the Chinese corpus of Fudan University. Many experiments are carried out on the automatic cluster of DTA-PSO, the text automatic clustering algorithm has a certain improvement on the K value determination and the clustering effect by the contrast analysis.

Due to the rapid population growth and economic development, water environmental protection pressures has been increasing recently. This paper focuses on the pollution of water quality, building a water quality assessment model to analyze the water quality level, and makes an objective further prediction of the trend of its factors. In this paper, the mutation factor of genetic algorithm is introduced into the PSO algorithm. The Least Squares Support Vector Machine (LS-SVM) based on adaptive Particle Swarm Optimization (PSO) algorithm used to optimize the hyper-parameter builds one water quality classification assessment model. The fuzzy information granulation method is combined with the Least Square Support Regression (LS-SVR) to set up a water quality time series model, which can predict the trend of changes in water quality data in three days. With the help of the theoretical analysis and experimental data, this assessment model and the prediction algorithm are faster in training speed and higher in accuracy, compared with the traditional BP neural network.

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