Particle Swarm Optimization Analysis Research Articles

Posterior analysis of particle swarm optimization results applied to gravity inversion in sedimentary basins

<abstract><p>As is well known, it is impossible to model reality with its true level of detail. Additionally, it is impossible to make an infinite number of observations, which are always contaminated by noise. These circumstances imply that, in an inverse problem, the misfit of the best estimated model will always be less than that of the true one. Therefore, it is not possible to reconstruct the model that actually generated the collected observations. The best way to express the solution of an inverse problem is as a collection of models that explain the observations at a certain misfit level according to a defined cost function. One of the main advantages of global search methods over local ones is that, in addition to not depending on an initial model, they provide a set of generated models with which statistics can be made. In this paper we present a technique for analyzing the results of any global search method, particularized to the particle swarm optimization algorithm applied to the solution of a two-dimensional gravity inverse problem in sedimentary basins. Starting with the set of generated models, we build the equivalence region of a predefined tolerance which contains the best estimated model, i.e., which involves the estimated global minimum of the cost function. The presented algorithm improves the efficiency of the equivalence region detection compared to our previous works.</p></abstract>

Optimizing IoT Wireless Sensor Networks: A Comparative Analysis of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA)

Optimizing IoT Wireless Sensor Networks: A Comparative Analysis of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA)

A Review on Convergence Analysis of Particle Swarm Optimization

Particle swarm optimization (PSO) is one of the popular nature-inspired metaheuristic algorithms. It has been used in different applications. The convergence analysis is among the key theoretical studies in PSO. This paper discusses major contributions in the convergence analysis of PSO. A systematic classification will be used for the review purpose. Possible future works are also highlighted as to investigate the performance of PSO variants to deal with COPs through theoretical perspective and general discussions on experimental results on merits of the proposed approach.

Optimizing MRT data processing via comparative analysis of SA and PSO algorithms: a simulation and numerical study.

The unknown emissivity still poses a significant challenge in the data processing of Multispectral radiation thermometry (MRT). In order to achieve global optimal solution with fast convergence speed and strong robustness, a systematic comparative analysis of particle swarm optimization (PSO) and simulated annealing (SA) algorithms in the application in MRT is presented in this paper. The simulations of six hypothetical emissivity models were compared, and the results indicate that the PSO algorithm is superior to the SA algorithm in accuracy, efficiency and stability. The measured data of the surface temperature of rocket motor nozzle is simulated by the PSO algorithm, the maximum absolute error and the maximum relative error are 16.27 K and 0.65%, and the calculation time is less than 0.3 s. The superior performance of the PSO algorithm indicates that it can be well used in data processing for accurate temperature measurement in MRT, and the method proposed in this paper can be extended to other multispectral systems and applied to various industrial processes under high temperature conditions.

An evaluative analysis of particle swarm optimization for reinforcement learning in pendulum task

An evaluative analysis of particle swarm optimization for reinforcement learning in pendulum task

Convergence analysis of Particle Swarm Optimization in one dimension

This paper is devoted to a rigorous analysis of one dimensional Particle Swarm Optimization (PSO) for non convex objective function. We provide a quantitative bound on the error between the equilibrium point and the global minimizer of the given objective function. Our strategy does not rely on the Laplace principle. We also investigate the underdamped PSO with linear objective function.

Comparative Analysis of Particle Swarm Optimization and Artificial Neural Network Based MPPT with Variable Irradiance and Load

The escalating demands and increasing awareness for the environment, resulted in deployment of Photovoltaic (PV) system as a viable option. PV system are widely installed for numerous applications. However, the challenges in tracking the maximum power with intermittent atmospheric condition and varying load is significant. Maximum Power Point Tracking (MPPT) algorithms are employed and based on their convergence speed, control of external variations and oscillation, the output power efficiency, and other significant factors viz. the algorithm complexity and implementation cost, novel MPPT approach are preferable than the conventional approach. This paper presents an artificial intelligence-based optimization controller for MPPT in a PV system under varying load and irradiance conditions. Comparative analysis of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) based MPPT is simulated and analysed. The PV system consisting of PV array and boost converter with MPPT controller feeds the DC load. The power conversion and panel efficiency are the significant factors to determine the effectiveness of tracking maximum power point. The simulation results show the performance of these controllers on the PV panel output power and the load side output power under changing loads and irradiance. In addition, the comparison of PV panel efficiency of ANN and PSO based MPPT techniques w.r.t changing loads is carried out. Based on the above analysis, PSO based MPPT algorithm marginally outperforms the ANN based MPPT algorithm. Further, the implementation of hybrid MPPT (ANN &PSO) for higher accuracy and tracking capability can be carried out as future work.

Microcrystalline diamond film evaluation by spectroscopic optical coherence tomography

This study has focused on the microcrystalline diamond film (MCD) thickness evaluation. For this purpose, optical coherence tomography (OCT) enhanced by spectroscopic analysis has been used as a method of choice. The average thickness of the tested layer was about 1.5 µm, which is below the standard 2-point OCT resolution. In this case, the usefulness of the spectroscopic analysis was confirmed for the evaluation of the thickness changes in the submicrometer range. Full Text: PDF ReferencesM.D. Drory, J.W. Hutchinson, "Diamond Coating of Titanium Alloys", Science, 263 (1994). CrossRef J. Wang, J. Zhou, H.Y. Long, Y.N. Xie, X.W. Zhang, H. Luo, Z.J. Deng, Q. Wei, Z.M. Yu, J. Zhang, Z.G. Tang, "Tribological, anti-corrosive properties and biocompatibility of the micro- and nano-crystalline diamond coated Ti6Al4V", Surf. Coat. Technol., 258 (2014). CrossRef P.A. Nistor, P.W. May, F. Tamagnini, A.D. Randall, M.A. Caldwell, "Long-term culture of pluripotent stem-cell-derived human neurons on diamond – A substrate for neurodegeneration research and therapy", Biomaterials, 61 (2015). CrossRef C.A. Love, R.B. Cook, T.J. Harvey, P.A. Dearnley, R.J.K. Wood, "Diamond like carbon coatings for potential application in biological implants—a review", Tribol. Int., 63 (2013). CrossRef P. Strąkowska, R. Beutner, M. Gnyba, A. Zielinski, D. Scharnweber, "Electrochemically assisted deposition of hydroxyapatite on Ti6Al4V substrates covered by CVD diamond films — Coating characterization and first cell biological results", Materials Science and Engineering: C, 59 (2016). CrossRef T.S. Ho, P. Yeh, C.C. Tsai, K.Y. Hsu, S.L. Huang., "Spectroscopic measurement of absorptive thin films by Spectral-Domain Optical Coherence Tomography", Opt. Express 22, 5 (2014). CrossRef N. Bosschaart, T.G. van Leeuwen, M.C.G. Aalders, D.J. Faber, "Quantitative comparison of analysis methods for spectroscopic optical coherence tomography", Biomedical Opt. Express 4, 11 (2013). CrossRef F.E Robles, C. Wilson, G. Grant, A. Wax, "Molecular imaging true-colour spectroscopic optical coherence tomography", Nat. Photonics 5, 12 (2011). CrossRef A.F. Fercher, W. Drexler, C.K. Hitzenberger, T. Lasser, "Optical coherence tomography - principles and applications", Rep. Prog. Phys. 66, 239 (2003). CrossRef A.M. Kamińska, M.R. Strąkowski, J. Pluciński, "Spectroscopic Optical Coherence Tomography for Thin Layer and Foil Measurements", Sensors 20, 19, (2020). CrossRef M. Kraszewski, M. Strąkowski, J. Pluciński, B.B. Kosmowski, "Spectral measurement of birefringence using particle swarm optimization analysis", Appl. Opt. 54, 1 (2015). CrossRef

Stochastic stability analysis of particle swarm optimization with pseudo random number assignment strategy

Particle swarm optimization (PSO) is a population-based optimization method and has been successfully applied to solve many real-world problems. This method belongs to the stochastic optimization method and is mainly driven by two random streams utilized in the stochastic search mechanism, namely, individual (cognition) and social randomness effects. To our best knowledge, no research work has been conducted about the manipulation of the random stream assignment for stochastic search mechanism in the PSO algorithm. In this work, the influences of controlling randomness in the searching scheme of PSO is studied by introducing different pseudo random number (PRN) assignment strategies. The order-1 and order-2 stability analyses for particle dynamics under different PRN assignment strategies are also conducted to understand the influences. Stability analysis is carried out using the stochastic process theory. Our results show that the correlation caused by PRN has no effect on the unbiasedness of the expectation of particle position, but it would reduce or increase the variance of particle dynamics. Second, the convergent conditions of the PSO system under different PRN assignment strategies and the corresponding parameter selection ranges are provided. Finally, an empirical analysis via experimental simulations evaluated by six common swarm diversity measures, eight benchmark test functions, and two parameter tuples is presented.

Improving Particle Swarm Optimization Analysis Using Differential Models

This paper employs the approach of the differential model to effectively improve the analysis of particle swarm optimization. This research uses a unified model to analyze four typical particle swarm optimization (PSO) algorithms. On this basis, the proposed approach further starts from the conversion between the differential equation model and the difference equation model and proposes a differential evolution PSO model. The simulation results of high-dimensional numerical optimization problems show that the algorithm’s performance can be greatly improved by increasing the step size parameter and using different transformation methods. This analytical method improves the performance of the PSO algorithm, and it is a feasible idea. This paper uses simple analysis to find that many algorithms are improved by using the difference model. Through simple analysis, this paper finds that many AI-related algorithms have been improved by using differential models. The PSO algorithm can be regarded as the social behavior of biological groups such as birds foraging and fish swimming. Therefore, these behaviors described above are an ongoing process and are more suitable for using differential models to improve the analysis of PSO. The simulation results of the experiment show that the differential evolution PSO algorithm based on the Runge–Kutta method can effectively avoid premature results and improve the computational efficiency of the algorithm. This research analyzes the influence of the differential model on the performance of PSO under different differenced conditions. Finally, the analytical results of the differential equation model of this paper also provide a new analytical solution.

Introduction of New Anti-dumping Algorithm in EU under WTO Law System Based on Particle Swarm Optimization

Objectives: Anti dumping new algorithm is an innovative ability based on the WTO legal system, which has made an important contribution to the economic development of the EU system. Methods: At present, the operation mode of new antidumping algorithm has some defects, such as structure confusion and incomplete system implementation, which affects the development progress of EU economic growth. Results: Based on the above problems, in this paper, particle swarm algorithm is introduced, based on the optimization analysis of the website structure of the new antidumping algorithm, through the independent screening analysis of particle swarm optimization, combining the WTO economy with the EU status theory, Conclusion: the paper obtains the optimized anti-dumping innovation scheme on the basis of particle swarm algorithm analysis, and finally passes the input test. The feasibility of the scheme is established.

Algebraic Stability Analysis of Particle Swarm Optimization Using Stochastic Lyapunov Functions and Quantifier Elimination

This paper adds to the discussion about theoretical aspects of particle swarm stability by proposing to employ stochastic Lyapunov functions and to determine the convergence set by quantifier elimination. We present a computational procedure and show that this approach leads to a reevaluation and extension of previously known stability regions for PSO using a Lyapunov approach under stagnation assumptions.

Analysis of particle swarm optimization by dynamical systems theory

Analysis of particle swarm optimization by dynamical systems theory

Order-3 stability analysis of particle swarm optimization

Particle swarm optimization (PSO) is a stochastic optimization algorithm, whose distribution is completely determined by its moments. Up to now, most of previous theoretical researches pay attention to the first and/or second moments only to give its stable condition, ignoring the analysis of third moment probably due to its complex third-order recurrence equation. To enrich theoretical cornerstones of PSO, this work analyzes its first, second and third moments, respectively. Built on first-order fixed point and two novel simplified strategies, we present the second and third order standardized recurrence equations for PSO. Their corresponding characteristic equations can be analyzed through the derivative functions to obtain the necessary and sufficient convergence condition. Besides, we show that the specific third-order stable region is slightly less than the second-order one in illustrations.

Performance Analysis of Particle Swarm Optimization for Feature Selection

One of the key task in data mining is the selection of relevant features from datasets with high dimensionality. This is expected to reduce the time and space complexity, and consequently improve the performance of data mining algorithms for tasks such as classification. This study presents an empirical study of the effect of particle swarm optimization as a feature selection technique on the performance of classification algorithms. Two dataset from different domains were used: SMS spam detection and sentiment analysis datasets. Particle swarm optimization is applied on the datasets for feature selection. Both the reduced and raw dataset are separately classified using C4.5 decision tree, k-nearest neighbour and support vector machine. The result of the analysis showed that the improvement of classifier performance is case-dependent; some significant improvements are noticed in the sentiment analysis datasets and not in the SMS spam dataset. Although some marginal effect are observed on performance, it implies that with particle swarm optimization features selection the space complexity is reduced while maintaining the accuracy of the classifiers. Keywords—classification, feature selection, machine learning, particle swarm optimization, text mining

Comparative Analysis of Particle Swarm Optimization Based Routing Protocols for MANET and VANET

Comparative Analysis of Particle Swarm Optimization Based Routing Protocols for MANET and VANET

Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment

Since the beginning of cloud computing technology, task scheduling problem has never been an easy work. Because of its NP-complete problem nature, a large number of task scheduling techniques have been suggested by different researchers to solve this complicated optimization problem. It is found worth to employ heuristics methods to get optimal or to arrive at near-optimal solutions. In this work, a combination of two heuristics algorithms was proposed: particle swarm optimization (PSO) and genetic algorithm (GA). Firstly, we list pros and cons of each algorithm and express its best interest to maximize the resource utilization. Secondly, we conduct a performance comparison approach based on two most critical objective functions of task scheduling problems which are execution time and computation cost of tasks in cloud computing. Thirdly, we compare our results with other existing heuristics algorithms from the literatures. The experimental results was examined with benchmark functions and results showed that the particle swarm optimization (PSO) performs better than genetic algorithm (GA) but they both present a similarity because of their population based search methods. The results also showed that the proposed hybrid models outperform the standard PSO and reduces dramatically the execution time and lower the processing cost on the computing resources.

Improved quantum-behaved particle swarm optimization with local search strategy

Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particles in swarm is different and determined by their fitness values. To minimization problems, the fitness value of one particle is smaller; the selected probability is more and will contribute more information in constructing the super particle. In addition, in order to investigate the influence on algorithm performance with different local search space, four methods of computing the local search radius are applied in local search strategy and propose four variants of local search quantum-behaved particle swarm optimization. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other quantum-behaved particle swarm optimization. The simulation results show that the proposed quantum-behaved particle swarm optimization variants have better advantages over the original quantum-behaved particle swarm optimization.

Conceptual Design for the Performance Optimization of Compound Coaxial Helicopter

The purpose of this paper is to study the conceptual design and optimization of a compound coaxial helicopter. At the conceptual design phase, the compound coaxial helicopter design work was based on the conventional helicopter and fix-wing aircraft design method. The intersection of these aspects makes the design work more complex, thus, a program for the sizing and performance optimization was developed for the aircraft. The program included the total weight design, aerodynamic analysis, flight dynamics analysis, performance calculation and particle swarm optimization analysis. Under the restricted condition of the flight performance requirements, optimize the design parameters which make the weight efficiency factor decrease. Therefore, the study of optimum design process was warranted.

A Comparative Analysis of Particle Swarm Optimization and Support Vector Machines for Devnagri Character Recognition: An Android Application

Devanagari script is widely used in the Indian subcontinent in several major languages such as Hindi, Sanskrit, Marathi and Nepali. Recognition of unconstrained (Handwritten) Devanagari character is more complex due to shape of constituent strokes. Hence character recognition (CR) has been an active area of research till now and it continues to be a challenging research topic due to its diverse applicable environment. As the size of the vocabulary increases, the complexity of algorithms also increases linearly due to the need for a larger search space. Devnagari script recognition systems using Zernike moments, fuzzy rule and quadratic classifier provide less accuracy and less efficiency. Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. In this paper techniques like particle swarm optimization and support vector machines are implemented and compared. An android phone is used for taking input character and MATLAB software for showing the recognized Devnagari character. For the connection between android device and MATLAB we are using PHP language. The particle swarm optimization technique provides accuracy up to 90%.