Geometric Particle Swarm Optimization Research Articles

Channel Development and Electric Parameter Characteristics of Regular Pulse Bursts in Lightning

AbstractObservations of a regular pulse burst (RPB) at the end of a K‐event are analyzed utilizing a simple geometric model and particle swarm optimization (PSO) to estimate the currents and propagation speeds of successive pulses of the RPB. The results show that the current of successive pulses is strongly overlapped and, for typical speeds of continuously propagating K‐events, are unphysically large (88 kA), exceeding the currents of most strokes to ground. By default, the unphysical nature of the result, coupled with very high frequency interferometer observations of an RPB in Florida, shows that the propagation speed of the pulses is significantly faster than expected, namely ∼0.6–1.8 × 108 m/s. This reduces the inferred current from 88 kA down to 6–18 kA, typical of intracloud events. The fast propagation speed of the stepping is explained by successive pulses retracing much of the path of the preceding pulses due to the successive pulses being strongly overlapped.

Virtual Reality Image Technology Assists Training Optimization of Motion Micro-Time Scale

Virtual reality image local features are highly repetitive and are not easily affected by target occlusion, so research on virtual reality image local feature extraction techniques has received increasing attention. For the purpose of global scale selection, this article considers the virtual reality image in the global scope from the theoretical perspectives of the continuity of virtual reality images, the repeatability of virtual reality image features, the scale of scene virtual reality images in the global scope, and the visual saliency. This paper gives a general framework of multi-scale geometric analysis and particle swarm optimization virtual reality image matching algorithm. For the problem of virtual reality image matching, the original particle swarm optimization algorithm is improved. A more reasonable inertia weight change formula, a method for selecting the optimal solution when two objectives are optimized at the same time, and a method for selecting a reasonable input are designed. In this paper, performance evaluation experiments are performed on several feature description algorithms. The experimental results show that the descriptors constructed by these feature description algorithms are significantly more robust than SIFT (Scale Invariant Feature Transform) descriptors. The improved multi-scale geometric analysis and particle swarm optimization are robust due to the introduction of multiple support domains. It has the best discriminative ability, but at the same time, the introduction of multiple support domains leads to the worst real-time algorithm; DGOH (Dual Gradient Orientation Histogram) algorithm is more robust than FRDOH (Fast Repre-sentation Based on a Double Orientation Histogram) and SIFT algorithm, and DGOH algorithm has the best real-time performance.

A nature-inspired biomarker for mental concentration using a single-channel EEG

We developed a system for measuring the attentional process during the performance of specific activities. The proposed biomarker device is able to estimate the mental concentration using a single-channel EEG. The system captures the EEG signal and several brain waves located in the left orbitofrontal brain region. Furthermore, we extended the input features of the system applying spectrum analysis. We applied two well-known evolutionary algorithms for selecting the best combination of input features: simulated annealing and geometric particle swarm optimization. Besides, we solved the binary classification problem (concentration vs. relaxation) using support vector machines and neural networks. Support vector machines are among the most common instruments for solving binary classification problems. On the other hand, we selected to study a family of neural networks named echo state networks, because the model is ideal for embedded systems and has shown good accuracy in real-world applications. The training and execution are fast, robust, and reliable. The developed system is autonomous, portable, reliable, non-invasive and has a low economic cost. Besides, it can be easily adjusted for each person and for each problem.

Tabu search and particle swarm optimization algorithms for two identical parallel machines scheduling problem with a single server

This paper proposes two efficient algorithms, which are tabu search and particle swarm optimization, for scheduling two identical parallel machines with a single server. The server has to set up the relevant machine before starting job processing. The objective is to minimize the makespan. This problem is considered unary NP-hard problem. The performance of the two proposed algorithms is evaluated using previously solved instances from the literature. The instances were solved using three different algorithms, which are genetic algorithm (GA), simulated annealing algorithm (SA) and I-L algorithm. We used the results of these three algorithms as a benchmark to compare with the two new introduced algorithms, which are tabu search (TS) and geometric particle swarm optimization (GPSO). The obtained results show that the proposed algorithms have a great performance for large instances. Moreover, the obtained results are very close to a lower bound, and in some instances, an optimal solution is achieved. In addition, TS performs better than SA and GA in term of average makespan for large instances and outperforms all algorithms in term of reaching the lower bound for all instances greater than 200 jobs, while GPSO comes second.

Particle swarm cooperative optimization algorithm based on geometric algebra

In order to overcome the demerits that the basic particle swarm optimization (hereinafter referred to as bPSO for short) algorithm is prone to fall into local extremum, slow convergence velocity and low convergence precision in the later stage of evolution, two strategies of Raman geometric relation particle swarm optimization equation and the addition of geometric rotation operator are adopted for the improvement, and Raman geometric relation particle swarm optimization (hereinafter referred to as Raman GPSO for short), geometric rotation particle swarm optimization (hereinafter referred to as grPSO) and the Raman geometric relation particle swarm optimization based on the geometric rotation of both (hereinafter referred to as Raman GPSO based on grPSO) algorithm are proposed. Raman GPSO has eliminated the particle velocity terms of the PSO evolution equation and simplified the original second-order geometric equation to the first-order geometry equation, with the evolutionary process controlled only by the particle position, which can avoid the problem of the slow convergence velocity and low precision in the later stage due to the particle divergence caused by the particle velocity term. grPSO can increase the geometric rotation operator which will accelerate the particle out of the local extreme point and continue the optimization. The experimental results of several classical test functions show that, Raman GPSO can greatly improve the convergence velocity and precision; grPSO can effectively get rid of the local extreme points; the combination of the aforementioned two strategies, Raman GPSO based on geometric rotation of Raman GPSO and grPSO have obtained very excellent optimization effect with smaller population size and evolutionary generations, so as to make the PSO algorithm more optimized.

Fuzzy unordered rule induction algorithm in text categorization on top of geometric particle swarm optimization term selection

Rapid growth of digital information requires automated handling and organization of documents. The two main stages in automated document categorization are (i) term reduction and (ii) classificatio...

Fuzzy unordered rule induction algorithm in text categorization on top of geometric particle swarm optimization term selection

Rapid growth of digital information requires automated handling and organization of documents. The two main stages in automated document categorization are (i) term reduction and (ii) classification. In this paper, we present a novel two-stage term reduction strategy based on Information Gain (IG) theory and Geometric Particle Swarm Optimization (GPSO) search. We evaluate performance of the proposed term reduction approach with use of a new classifier, fuzzy unordered rule induction algorithm (FURIA) to categorize multi-label texts. In order to evaluate the performance of FURIA quantitatively, we compared it against two widely used algorithms, Naïve Bayes and Support Vector Machine (SVM). Text Categorization (TC) performance of the proposed term reduction strategy is validated with use of Reuters-21578 and OHSUMED text collection datasets. The experimental results show that performance of the proposed term reduction method is efficient for document organization tasks.

Self-organizing migration algorithm applied to machining allocation of clutch assembly

Tolerancing is an important issue in product and manufacturing process designs. The allocation of design tolerances between the components of a mechanical assembly and manufacturing tolerances in the intermediate machining steps of component fabrication can significantly affect the quality, robustness and life-cycle of a product. Stimulated by the growing demand for improving the reliability and performance of manufacturing process designs, the tolerance design optimization has been receiving significant attention from researchers in the field. In recent years, a broad class of meta-heuristics algorithms has been developed for tolerance optimization. Recently, a new class of stochastic optimization algorithm called self-organizing migrating algorithm (SOMA) was proposed in literature. SOMA works on a population of potential solutions called specimen and it is based on the self-organizing behavior of groups of individuals in a “social environment”. This paper introduces a modified SOMA approach based on Gaussian operator (GSOMA) to solve the machining tolerance allocation of an overrunning clutch assembly. The objective is to obtain optimum tolerances of the individual components for the minimum cost of manufacturing. Simulation results obtained by the SOMA and GSOMA approaches are compared with results presented in recent literature using geometric programming, genetic algorithm, and particle swarm optimization.

A hybridized approach to optimal tolerance synthesis of clutch assembly

Specifying proper tolerances for manufactured goods results in greater savings and improved performance, which may ultimately determine whether a product succeeds or fails in the marketplace. In the past, tolerance specification has been more an art than a science, and is largely dependent upon experiences. A more scientific and reliable approach is presented in this paper. A hybrid of Nelder-Mead simplex method and particle swarm optimization (NM-PSO) is introduced for the design of tolerance of the machine elements of an overrunning clutch assembly. The objective is to obtain tolerances of the individual components so that the cost of manufacturing and quality loss is minimized. Experimental results demonstrate that hybrid NM-PSO is extremely effective and efficient in locating best-practice optimal solutions compared to geometric programming (GP), genetic algorithm (GA), and particle swarm optimization (PSO) methods.