A dynamic programming-based particle swarm optimization algorithm for an inventory management problem under uncertainty

In order to solve the problem of minimizing cost of power generation calculation in voltage stability constrained optimal power flow optimal of power system, dynamic double-population particle swarm optimization algorithm is used on the basis of the traditional particle swarm optimization algorithm, In this algorithm the particles not only depends on successful experience to move but also get experience from failure cases. And the particles are constantly changing in the process of iteration, which overcomes the local convergence of traditional PSO. The dynamic double-population particle swarm optimization algorithm is applied to the voltage stability constrained optimal power flow calculation to minimizing the generation cost problem, which was tested in a standard IEEE30 system, in order to prove the effectiveness of dynamic double-population particle swarm optimization algorithm, it is compared with genetic algorithm (GA) and results show that, dynamic double-population particle swarm optimization algorithm is better than genetic algorithm in computing power cost minimization problem.

This paper proposes a new application of dynamic particle swarm optimization (PSO) algorithm for parameter identification of vector controlled asynchronous propulsion motor (APM) in electric propulsion ship. The dynamic PSO modifies the inertia weight, learning coefficients and two independent random sequences which affect the convergence capability and solution quality, in order to improve the performance of the standard PSO algorithm. The standard PSO and dynamic PSO algorithms use measurements of the mt-axis currents, voltages of APM as the inputs to parameter identification system. The experimental results obtained compare the identified parameters with the actual parameters. There is also a comparison of the solution quality between standard PSO and dynamic PSO algorithms. The results demonstrate that the dynamic PSO algorithm is better than standard PSO algorithm for APM parameter identification. Dynamic PSO algorithm can improve the performance of ship propulsion motor under abrupt load variation.

This paper proposes a novel application of a dynamic particle swarm optimization (PSO) algorithm for determining a maximum power point (MPP) of a solar photovoltaic (PV) panel. Solar PV cells have a non-linear V-I characteristic with a distinct MPP which depends on environmental factors such as temperature and irradiation. In order to continuously harvest maximum power from the solar PV panel, it always has to be operated at its MPP. The proposed dynamic PSO algorithm is one of the PSO algorithm variants, which modifies the acceleration coefficients of the cognitive and social components in the velocity update equation of the PSO algorithm as linear time-varying parameters to improve the global search capability of particles in the early stage of the optimization process and direct the global optima at the end stage. The obtained simulation results are compared with MPPs achieved using other algorithms such as the standard PSO, and Perturbation and Observation (P&O) algorithms under various atmospheric conditions. The results show that the dynamic PSO algorithm is better than the standard PSO and P&O algorithms for determining and tracking MPPs of solar PV panels.

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.

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.

Image segmentation has considered an important step in image processing. Fuzzy c-means (FCM) is one of the commonly used clustering algorithms because of its simplicity and effectiveness. However, FCM has the disadvantages of sensitivity to initial values, falling easily into local optimal solution and sensitivity to noise. To tackle these disadvantages, many optimization-based fuzzy clustering methods have been proposed in the literature survey. Particle swarm optimization (PSO) has good global optimization capability and a hybrid of FCM and PSO have improved accuracy over tradition FCM clustering. In this paper, a new image segmentation method based on Dynamic Particle swarm optimization (DPSO) and FCM algorithm along with the noise reduction mechanism is proposed. DPSO has the advantages to change the inertia weight and learning parameters dynamically. It adopts the inertia weight according to the fitness value and learning parameters along with time. The proposed method combines DPSO with FCM, using the advantages of global optimization searching and parallel computing of DPSO to find a superior result of the FCM algorithm. Moreover, a noise reduction mechanism based on the surrounding pixels is used for enhancing the anti-noise ability. The synthetic image and Magnetic Resonance Imaging (MRI) have been used for testing the proposed method by introducing different types of noises and the results show that the proposed algorithm has better performance and less sensitive to noise.

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.

The octane number of hydrogenated gasoline is difficult to be obtained in real time in the modeling of finished gasoline blending formula. Considering the problems of XGBOOST algorithm, gradient lifting tree algorithm and random forest regression algorithm network, a dynamic harmonious search hybrid particle swarm optimization (DSHPHO) algorithm was proposed to predict the octane number of finished gasoline. In this algorithm, the improved HS algorithm is embedded into the PSO algorithm, and all the particles are considered as harmonious memory (HM). Search by harmony search (HS) algorithm of randomness and evolution mechanism to improve the diversity of particle swarm, makes more ergodic particle swarm at the beginning of the search, reduce sensitivity to the initial value of the algorithm and keep randomly generated in the whole evolution process of the possibility of new particles, fundamentally solves the particle swarm optimization algorithm in dimension increase diversity is less defects. The algorithm has faster convergence speed and better global search ability. Finally, based on this method and industrial historical data, the octane number prediction model of hydrogenated gasoline components is established. The simulation results show that the dynamic harmonious search hybrid particle swarm optimization algorithm has better prediction performance than the traditional particle swarm optimization algorithm, and can be used to predict the octane number.

For improving the equalization performance of higher-order QAM signals, orthogonal Wavelet transform dynamic Weighted Multi-Modulus blind equalization Algorithm based on the Dynamic Particle Swarm Optimization(DPSO-WWMMA) is proposed. In this proposed algorithm, dynamic particle swarm optimization algorithm and orthogonal wavelet transform are introduced into dynamic Weighted Multi-Modulus blind equalization Algorithm(WMMA). Accordingly, the equalizer weight vector can be optimized by Dynamic Particle Swarm Optimization(DPSO) algorithm, the autocorrelation of the input signals can be reduced via using orthogonal wavelet transform, and the WMMA is used to choose appropriate error model to match QAM constellations. The theoretical analyses and computer simulations in underwater acoustic channels indicate that the proposed algorithm can obtain the fastest convergence rate and the smallest steady mean square error in equalizing high-order QAM signals. So, the proposed algorithm has important reference value in the underwater acoustic communications.

1 Abstract: This paper proposes a dynamic particle swarm optimization (PSO) algorithm for optimal generation rescheduling of a power system including renewable energy sources such as the solar and wind energy sources. The algorithm is to minimize total operating costs of this hybrid power system. The proposed dynamic PSO algorithm is one of the standard PSO algorithm variants, which modifies the acceleration coefficients of the cognitive and social components in the velocity update equation of the PSO algorithm as linear time-varying parameters. The acceleration coefficients are varied during the evolution process of the PSO algorithm to improve the global search capability of particles in the early stage of the optimization process and direct the global optima at the end stage. The dynamic PSO algorithm based optimal generation rescheduling of the power system with and without solar and wind powers is considered on the standard IEEE 30-bus 6-generator 41-transmission line test power system. The numerical results demonstrate the capabilities of the proposed algorithm to generate optimal solutions of the power system considering the renewable energy resources. The comparison with the standard PSO algorithm demonstrates the superiority of the proposed algorithm and confirms its potential to reschedule an optimal generation of the power system including the solar and wind energy sources.

Dynamic Double-population Particle Swarm Optimization (DDPSO) algorithm was presented to solve the problem that the standard PSO algorithm easily fell into a locally optimized point, where the population was divided into two sub-populations varying with their own evolutionary learning strategies and exchanging between them. The algorithm had been applied to power system Unit Commitment (UC). The DDPSO particle consisted of a two-dimensional real number matrix representing the generation schedule. According to the proposed coding manner, the DDPSO algorithm could directly solve UC. Simulation results show that the proposed method performs better in term of precision and convergence property.

This paper investigates a novel inventory and distribution planning model with non-conforming items disposal (NIDPNCID) under fuzzy random environment to minimize the whole process cost. In this process, a certain fraction or a random number of produced items are defective. These non-conforming items are rejected in order to improve the consumer satisfaction. To solve the problem, a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm with fuzzy random simulation is proposed, which can be easy to implement. In more specific terms, DP-based PSO can reduce the dimensions of a particle by using the state equation, which significantly reduced the solution space.

For the difficulty of tuning the PID controller parameter to control a complex chaotic system, in order to overcome the shortage of basic particle swarm optimization, this paper improves the inertia weight and introduces the uncontrolled chaotic system into the PSO algorithm, proposes the improved dynamic chaos particle swarm optimization algorithm. This paper applies the algorithm to the chaos PID controller parameter optimization and takes a simulation study on the PID control method for the chaotic ship steering control, the results show that the controller parameter optimized by the proposed method can quickly stabilize the chaotic system to fixed point, and the control effectiveness is demonstrated.

An innovative method based on dynamic particle swarm optimization (DPSO) algorithm is presented to demodulate the strain profile along a fiber Bragg grating (FBG) from its reflection spectrum, which is calculated by using the modified transfer matrix method. To improve the optimization performances of algorithm itself, the inertia weight of the DPSO algorithm is adjusted dynamically according to the distance between the individual particle and the global optimal particle in the current population. Then the numerical simulation and experimental verification of the reconstruction of nonuniform strain profiles are comprehensively carried out. Both the simulation examples and experimental results verify the feasibility and validity of the present method.

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.