Parameter identification of Jiles–Atherton model using the chaotic optimization method

Novel computing paradigms for parameter estimation in Hammerstein controlled auto regressive auto regressive moving average systems

Musculoskeletal modeling allows for the determination of various parameters during dynamic maneuvers by using in vivo kinematic and ground reaction force (GRF) data as inputs. Differences between experimental and model marker data and inconsistencies in the GRFs applied to these musculoskeletal models may not produce accurate simulations. Therefore, residual forces and moments are applied to these models in order to reduce these differences. Numerical optimization techniques can be used to determine optimal tracking weights of each degree of freedom of a musculoskeletal model in order to reduce differences between the experimental and model marker data as well as residual forces and moments. In this study, the particle swarm optimization (PSO) and simplex simulated annealing (SIMPSA) algorithms were used to determine optimal tracking weights for the simulation of a sidestep cut. The PSO and SIMPSA algorithms were able to produce model kinematics that were within 1.4° of experimental kinematics with residual forces and moments of less than 10 N and 18 Nm, respectively. The PSO algorithm was able to replicate the experimental kinematic data more closely and produce more dynamically consistent kinematic data for a sidestep cut compared to the SIMPSA algorithm. Future studies should use external optimization routines to determine dynamically consistent kinematic data and report the differences between experimental and model data for these musculoskeletal simulations.

A new hybrid particle swarm optimization (HPSO) algorithm which is based on particle swarm optimization algorithm and simulated annealing algorithm is proposed in this paper for solving multiple capacitated vehicle routing problem. The basic scheme consists in Particle swarm optimization with time-varying parameters and simulated annealing with memory and tempering features. Results from the computational experiment shows that the hybrid particle swarm algorithm is effective.

In our previous research applied to the job shop scheduling problem (JSSP) for machine tool production, the multi-objective optimisation model based on the particle swarm optimization (PSO) research had an imbalance performance between the convergence rate and the convergence precision. In this article, a new hybrid algorithm using PSO and generic algorithm (GA) is proposed to solve this particular problem. In this new algorithm, named hybrid PSO-GA algorithm (HPGA), the PSO algorithm is redefined and modified by introducing genetic operators, i.e. the crossover operator and the mutation operator, to update the particles in the population. The HPGA is then applied in heavy machinery company with minimising machines’ makespan and minimising jobs’ tardiness as the two optimal objectives. The comparisons with actual application report have illustrated that the proposed HPGA can obtain higher quality of schedule solution for machine tool production. Furthermore, with solution quality and convergence rate as the two estimation measurements metrics, some comparisons are performed in order to illustrate that the HPGA has superiority over the PSO, GA and simulated annealing algorithm (SA). Results have indicated that, with the combination of the merits of PSO and GA, the proposed HPGA approach can achieve not only better solution quality but also faster convergence rate than the PSO, GA and SA, within a reasonable computation time for high dimensions JSSP.

Particle swarm optimization (PSO) is a powerful optimization technique that has been applied to solve a number of complex optimization problems. One such optimization problem is topology design of distributed local area networks (DLANs). The problem is defined as a multi-objective optimization problem requiring simultaneous optimization of monetary cost, average network delay, hop count between communicating nodes, and reliability under a set of constraints. This paper presents a multi-objective particle swarm optimization algorithm to efficiently solve the DLAN topology design problem. Fuzzy logic is incorporated in the PSO algorithm to handle the multi-objective nature of the problem. Specifically, a recently proposed fuzzy aggregation operator, namely the unified And-Or operator (Khan and Engelbrecht in Inf. Sci. 177: 2692---2711, 2007), is used to aggregate the objectives. The proposed fuzzy PSO (FPSO) algorithm is empirically evaluated through a preliminary sensitivity analysis of the PSO parameters. FPSO is also compared with fuzzy simulated annealing and fuzzy ant colony optimization algorithms. Results suggest that the fuzzy PSO is a suitable algorithm for solving the DLAN topology design problem.

Empirical comparison of search algorithms for discrete event simulation

This paper presents a novel combined algorithm based on particle swarm optimization (PSO) and simulated annealing algorithm(SA) for power system reactive optimization. According to the characteristics of PSO and SA, the proposed method which combined PSO and SA efficiently makes good use of easily implementing performance and fast convergence performance of PSO and global convergence performance of SA, and makes them search in phase. This paper detailed explains coding of control variables, selecting of parameters and material steps of combined algorithm for optimizing reactive power. Taking the IEEE 14-bus system, IEEE 57-bus system and IEEE 118-bus system for example, the simulation results demonstrate that the proposed method is simple and easy to implement, and has high computing efficiency and comparing with conventional PSO it can obtain higher quality solutions.

In view of the problem of premature convergence of traditional particle swarm optimization (PSO) algorithm in path planning, which is easy to converge to local optimal solution and poor path quality, some theory about the corresponding PSO algorithm of simulated annealing optimization is studied in this paper. While planning the moving path of the robot, it analyzes the effect of initial temperature and cooling coefficient on path length and iteration times from the major contributing factors of simulated annealing algorithm. Thus deduce the law of its change and seek the optimal parameter matching. Simulated annealing algorithm can not only move the updated particle position on the basis of the particle swarm optimization formula, but also select the updated position with a certain probability. The method is used to avoid the particle converging into the local optimal solution in the whole iterative process. The capabilities of the global optimization is strengthened. Compared with the traditional PSO algorithm, the simulated annealing PSO in complex environment has better optimization ability, shorter path and fewer iterations in the simulation results.KeywordsParticle swarmPath planningSimulated annealingLinear inertia weight

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (DBO) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model’s ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed GDBO algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional DBO algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification.

A fractional-order modelling and parameter identification method via improved driving training-based optimization for piezoelectric nonlinear system

In this paper, a real-valued genetic algorithm (RGA) and a particle swarm optimization (PSO) algorithm with a new fitness function method are proposed to design a PID controller for the Automatic Voltage Regulator (AVR) system. The proposed fitness function can let the RGA and PSO algorithm search a high-quality solution effectively and improve the transient response of the controlled system. The proposed algorithms are applied in the PID controller design for the AVR system. Some simulation and comparison results are presented. We can see that the proposed RGA and PSO algorithm with this new fitness function can find a PID control parameter set effectively so that the controlled AVR system has a better control performance.

Optimization of process parameters in the abrasive waterjet machining using integrated SA–GA

A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery

Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonists.

The PSO (Particle Swarm Optimization) algorithm uses a population of single particles, randomly distributed over the function search space, in order to construct an optimal flight path. During the algorithm iteration execution, each particle evaluates its fitness function according to the actual position. Then, every particle moves towards a direction dependent on its actual best position and the position of the best particle so far. The procedure ends when the iteration limit is reached or fitness criterium is met. In the presented autonomous UAV 3D path planning method, a PSO algorithm is used to build a feasible path, optimized in terms of fuel and threat cost. The process begins when the UAV detects or receives information about threats appearing on its primary trajectory. The population of particles moves in the search space of three variables, which are the angles of pitch, roll and yaw. These angles determine the spatial orientation of the UAV, indicating its direction of movement in each step. Further waypoints are chosen with consideration of the distance to the target and violation of threat areas. As a final result, the PSO algorithm constructs a suboptimal, feasible path which could be used as a reference trajectory for the UAV’s automatic control system. Simulation results turned out to be completely repeatable and indistinguishable, despite the stochastic nature of the algorithm, which proves its great optimization abilities. Moreover, its short execution time (within seconds) allows this procedure to be used in real time applications.