A Boosted Particle Swarm Method for Energy Efficiency Optimization of PRO Systems

The analytical solutions of complex dynamic PRO systems pose challenges to ensuring that maximum power can be harvested in stable, rapid, and efficient ways in response to varying operational environments. In this paper, a boosted particle swarm optimization (BPSO) method with enhanced essential coefficients is proposed to enhance the exploration and exploitation stages in the optimization process. Moreover, several state-of-the-art techniques are utilized to evaluate the proposed BPSO of scaled-up PRO systems. The competitive results revealed that the proposed method improves power density by up to 88.9% in comparison with other algorithms, proving its ability to provide superior performance with complex and computationally intensive derivative problems. The analysis and comparison of the popular and recent metaheuristic methods in this study could provide a reference for the targeted selection method for different applications.

Swarm intelligence in the problems of detecting anomalies and states of natural-technical systems and objects

An approach to solving the problem of detecting and classifying anomalies and states of natural-technical systems and objects using swarm intelligence methods is considered. The main directions of development of the proposed approach include ant algorithms, bee swarm algorithms, and the particle swarm method. The structure of the swarm intelligence system of decision support based on collective preference rules is proposed. The application of the proposed approach makes it possible to optimize the processes of processing, analysis, integration of heterogeneous data, to increase the sensitivity, reliability and efficiency of decisions made.

Magnetic anomaly interpretation for a 2D fault-like geologic structures utilizing the global particle swarm method

We establish a method to elucidate the magnetic anomaly due to 2D fault structures, with an evaluation of first moving average residual anomalies utilizing filters of increasing window lengths. After that, the buried fault parameters are estimated using the global particle swarm method. The goodness of fit among the observed and the calculated models is expressed as the root mean squared (RMS) error. The importance of studying and delineating the fault parameters, which include the amplitude factor, the depth to the upper edge, the depth to the lower edge, the fault dip angle, and the position of the origin of the fault, is: (i) solving many problem-related engineering and environmental applications, (ii) describing the accompanying mineralized zones with faults, (iii) describing geological deformation events, (iv) monitoring the subsurface shear zones, (v) defining the environmental effects of the faults before organizing any investments, and (vi) imaging subsurface faults for different scientific studies. Finally, we show the method applied to two theoretical models including the influence of the regional background and the multi-fault effect and to real field examples from Australia and Turkey. Available geologic and geophysical information corroborates our interpretations.

APPLICATION OF GLOBAL OPTIMIZATION TO PREDICT STRAINS IN RC COMPRESSED COLUMNS

In this paper, the results of an application of global and local optimization methods to solve a problem of determination of strains in RC compressed structure members are presented. Solutions of appropriate sets of nonlinear equations in the presence of box constraints have to be found. The use of the least squares method leads to finding global solutions of optimization problems with box constraints. Numerical examples illustrate the effects of the loading value and the loading eccentricity on the strains in concrete and reinforcing steel in the a cross-section.Three different minimization methods were applied to compute them: trust region reflective, genetic algorithm tailored to problems with real double variables and particle swarm method. Numerical results on practical data are presented. In some cases, several solutions were found. Their existence has been detected by the local search with multistart, while the genetic and particle swarm methods failed to recognize their presence.

Color spectra algorithm of hyperspectral wood dyeing using particle swarm optimization

To improve the accuracy and practicality of the intelligent color-matching application of wood dyeing technology, Fraxinus mandshurica veneer was selected as the dyeing material. First, based on the Friele model and Stearns–Noechel model, the model parameters were cyclically assigned to calculate the optimal fixed parameter values and predictions. Then, particle swarm algorithm was used to optimize two algorithm models, the obtained reflectance curve was fit, and the color differences were calculated according to the human eye-based CIEDE2000 color difference evaluation standard formula. Last, the two formulas to predict the color difference and spectral reflectance were compared. First, the two optimization algorithms were compared according to the size of the fitted color difference value, and then, the most accurate optimization algorithm was selected. When the model parameters were fixed, the average fitted color difference was 0.8202. After optimizing the Friele model, the average fitted color difference was 0.7287, and after optimizing the Stearns–Noechel model, the average fitted color difference was 0.6482. It was concluded that the improved Stearns–Noechel model based on particle swarm method was more accurate than the Friele model for wood color matching.

Fed-Batch Sucrose Crystallization Model for the B Massecuite Vacuum Pan, Solution by Deterministic and Heuristic Methods

Fed-batch crystallization is a crucial step for sugar production. In order to relate parameters that are difficult to measure (average diameter of the crystals and total mass formed) to other easier to measure parameters (volume, temperature, and concentration), a model was developed for a B massecuite vacuum pan composed of mass and energy balances together with empirical relations that describe the crystal development inside equipment. The generated system of ordinary differential equations (ODE) had eight parameters which were adjusted through minimization of relative differences between the model results and experimental data. It was solved through the function fmincon, available in MATLABTM, which is a deterministic and gradient-based optimization method. The objective of this paper is to improve the model obtained and, for this purpose, two metaheuristic functions were used: genetic algorithm and particle swarm. To compare the results, the convergence time of each algorithm was used as well as the resulting quadratic deviation. The particle swarm method was the best option among the three used, presenting a shorter execution time and lower quadratic relative deviation.