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EN
This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time.
EN
This paper presents the application of Flexible Alternating Current Transmission System (FACTS) devices based on heuristic algorithms in power systems. The work proposes the Autonomous Groups Particle Swarm Optimization (AGPSO) approach fort he optimal placement and sizing of the Static Var Compensator (SVC) to minimize thetotal active power losses in transmission lines. A comparative study is conducted with other heuristic optimization algorithms such as Particle Swarm Optimization (PSO), Time-varying Acceleration Coefficients PSO (TACPSO), Improved PSO (IPSO), Modified PSO(MPSO), and Moth-Flam Optimization (MFO) algorithms to confirm the efficacy of the proposed algorithm. Computer simulations have been carried out on MATLAB with the MATPOWER additional package to evaluate the performance of the AGPSO algorithm on the IEEE 14 and 30 bus systems. The simulation results show that the proposed algorith moffers the best performance among all algorithms with the lowest active power losses and the highest convergence rate.
EN
Controlling the contact force between the pantograph and the catenary has come to be a requirement for improving the performances and affectivity of high-speed train systems Indeed, these performances can also significantly be decreased due to the fact of the catenary equal stiffness variation. In addition, the contact force can also additionally differ and ought to end up null, which may additionally purpose the loss of contact. Then, in this paper, we current an active manipulate of the minimize order model of pantograph-catenary system. The proposed manipulate approach implements an optimization technique, like particle swarm (PSO), the usage of a frequent approximation of the catenary equal stiffness. All the synthesis steps of the manipulate law are given and a formal evaluation of the closed loop stability indicates an asymptotic monitoring of a nominal steady contact force. Then, the usage of Genetic Algorithm with Proportional-Integral-derivative (G.A-PID) as proposed controller appeared optimum response where, the contacts force consequences to be virtually equal to its steady reference. Finally it seems the advantageous of suggestion approach in contrast with classical manipulate strategies like, Internal mode control(IMC) method, linear quadratic regulator (LQR). The outcomes via the use of MATLAB simulation, suggests (G.A-PID) offers better transient specifications in contrast with classical manipulate.
EN
The paper presents and demonstrates the theorem showing the equivalence of the problem of the verifiability test of a logical expression in the discrete model N of the logic with the search for the minimum value of a continuous function generated by this expression in the structure N, which is a simple extension of M. Theoretical considerations are illustrated by the example of a certain semi-heuristic algorithm seeking the minimum value of function ϕ with a short statistics of its.
EN
The aim of the study is to compare Ziegler-Nichols (Z-N) and Particle Swarm Optimization (PSO) based tuning methods for controller tuning in the driving mechanism of prosthetic limbs. By adopting suitable control strategies like P, PI and PID in the driving system, the positioning of knee and hip joints can be attained in the ideal time of 1.4s for completing one locomotion cycle. The gain constants (KP , KI , and KD) of the controllers were tuned manually and also using Z-N and PSO; thereby appropriate constants were determined so that the joints could be moved to the desired position. The performance of P, PI, and PID controllers were compared and PID was identified as the ideal control strategy which exhibited least error and good stability. It was observed that the conventional Z-N method produced a big overshoot, and so a modern approach called PSO was employed to enhance its capability. The PSO based PID controller optimization resulted in less overshoot as well as it helped in optimizing the gain constants so as to improve the stability of the system when compared to the classical method.
EN
Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences.
EN
The paper presents a three-phase grid-tied converter operated under unbalanced and distorted grid voltage conditions, using a multi-oscillatory current controller to provide high quality phase currents. The aim of this study is to introduce a systematic design of the current control loop. A distinctive feature of the proposed method is that the designer needs to define the required response and the disturbance characteristic, rather than usually unintuitive coefficients of controllers. Most common approach to tuning a state-feedback controller use linear-quadratic regulator (LQR) technique or pole-placement method. The tuning process for those methods usually comes down to guessing several parameters. For more complex systems including multi-oscillatory terms, control system tuning is unintuitive and cannot be effectively done by trial and error method. This paper proposes particle swarm optimization to find the optimal weights in a cost function for the LQR procedure. Complete settings for optimization procedure and numerical model are presented. Our goal here is to demonstrate an original design workflow. The proposed method has been verified in experimental study at a 10 kW laboratory setup.
8
Content available Food delivery based on pso algortihm and google maps
EN
This article presents a solution to deal with the optimization of delivery routes problem for a mobile application focused on the restaurant sector, by using a bioinspired algorithm (PSO) to minimize delivery costs, maximize a greater number of deliveries and recommend an optional route for food delivery. Different computational experiments are carried out by using Google Maps (API) for showing the best delivery route. The results obtained are very promising for offering a good delivery service.
EN
The research considers the optimization of the taps position of voltage transformers to minimize power loss. The Particle Swarm Optimization algorithm is implemented to this optimization problem. The advantage of this algorithm is the ability to adapt to an optimization problem. It was found out that the Particle Swarm Optimization algorithm is more productive than the greedy heuristic algorithm based on the division of this optimization problem into subtasks. Also, the paper studied the influence of particle velocity restriction on the efficiency of the algorithm.
PL
W pracy analizowano metode optymalizacji strat transformatora przez dobór stosunku uzwojeń. Do tego celu wykorzystano algorytm genetyczny PSO. Porównano prace układu z innymi algorytmami adaptacyjnymi.
PL
Artykuł opisuje tło instytucjonalne kryzysu kolei pasażerskiej w Polsce w okresie 1990–2015. Wskazuje etapy przekształceń podmiotów i procedury kształtujące obszar kolejowych przewozów regionalnych. Zwrócono zarazem uwagę na zależności pomiędzy modelami zamawiania i finansowania usług o charakterze służby publicznej a ich jakością i efektywnością. Analiza oparta jest na ocenie zmian o charakterze formalnoprawnym oraz statystyk ilustrujących udziały rynkowe przewoźników. Wnioski wskazują natomiast na zalety modelu opartego na silnym i kompetentnym ośrodku decyzyjnym oraz konkurencyjnym trybie wyboru operatora usług publicznych, i tym samym mogą stać się elementem dyskusji o dalszych przekształceniach sektora kolejowego w Polsce.
EN
The article describes the institutional background of the passenger rail crisis in Poland between 1990 and 2015. It indicates the stages of transformation of entities and procedures. It also draws attention to dependencies, between the models for ordering and financing PSO and their quality and effectiveness. The analysis is based on the assessment of formal and legal changes and statistics illustrating the market share of carriers. The conclusions, on the other hand, indicate the advantages of a model based on a strong and competent decision-making center and a competitive mode of selecting rail operator, and thus constitute a practical recommendation as to further transformations of the railway sector in Poland.
EN
Numerous algorithms have met complexity in recognizing the face, which is invariant to plastic surgery, owing to the texture variations in the skin. Though plastic surgery serves to be a challenging issue in the domain of face recognition, the concerned theme has to be restudied for its hypothetical and experimental perspectives. In this paper, Adaptive Gradient Location and Orientation Histogram (AGLOH)-based feature extraction is proposed to accomplish effective plastic surgery face recognition. The proposed features are extracted from the granular space of the faces. Additionally, the variants of the local binary pattern are also extracted to accompany the AGLOH features. Subsequently, the feature dimensionality is reduced using principal component analysis (PCA) to train the artificial neural network. The paper trains the neural network using particle swarm optimization, despite utilizing the traditional learning algorithms. The experimentation involved 452 plastic surgery faces from blepharoplasty, brow lift, liposhaving, malar augmentation, mentoplasty, otoplasty, rhinoplasty, rhytidectomy and skin peeling. Finally, the proposed AGLOH proves its performance dominance.
PL
Do rozwiązania problemu unikania przeszkód przez poruszający się samolot w przestrzeni powietrznej niezbędne jest wykrycie zagrożenia kolizji oraz wykonanie bezpiecznego manewru w celu ominięcia zagrażających przeszkód. W pracy przedstawiono sposób wykrywania niebezpieczeństwa zderzenia z przeszkodą dla przypadku, gdy w otoczeniu samolotu znajduje się wiele ruchomych obiektów. Zaproponowano sposób wyboru optymalnej trajektorii manewru antykolizyjnego, i potwierdzono jej wykonalność. Wybór trajektorii przeprowadzono rozwiązując zagadnienie optymalizacji metodą roju cząstek (PSO). W tym celu zaproponowano postać funkcji celu i przedstawiono wyniki analizy jej przebiegu dla różnych współczynników wagowych. Wykonane symulacje lotu wzdłuż optymalnej trajektorii manewru antykolizyjnego potwierdziły wykonalność takiego manewru.
EN
For solving the airplane to obstacle collision avoidance problem two methods are necessary: one, for detecting a collision threat, and the other one, for synthesizing a safe manoeuvre avoiding threating obstacles. In the article a method for detecting a threat of collision to obstacle was presented for the case of many obstacles moving within the neighbourhood of the airplane. Methods for optimal anti collision trajectory synthesis and for proving the workability of such a result were proposed too. A solution of an optimisation problem, obtained by the Swarm of Particles Optimization was used for trajectory synthesis. A form of quality index was proposed for this task and the analyses of its behaviour for several values of weighting factors were presented. Results of simulations of flight along an optimal, anti collision manoeuvre trajectory proved that such a manoeuvre is workable.
13
Content available remote Optymalizacja wielokryterialna cyklu roboczego manipulatora antrompomorficznego
PL
Niniejsza praca przybliża zagadnienia związane z zastosowaniem metody roju cząstek (Particle Swarm Optimization – PSO) do poszukiwania optymalnego cyklu roboczego manipulatora antropomorficznego. Zadanie polegało na wyznaczeniu takiego cyklu roboczego, który spełniał jednocześnie dwa kryteria: najkrótsza droga pomiędzy zdefiniowanymi punktami z ominięciem przeszkód oraz najmniejsze zużycie energii elektrycznej przez napędy. W artykule analizowano wpływ wybranych parametrów algorytmu w celu znalezienia optymalnej trajektorii.
EN
This paper introduces issues related to the use of Particle Swarm Optimization (PSO) method to search for the optimum duty cycle of the anthropomorphic manipulator. The task is to determine the work cycle that meets two criteria simultaneously: the shortest path between defined points avoiding obstacles, and lowest energy consumption by drives. The influence of selected algorithm parameters in order to find the optimal value of the objective function has been analyzed in the paper.
EN
Numerous practical engineering applications can be formulated as non-convex, non-smooth, multi-modal and ill-conditioned optimization problems. Classical, deterministic algorithms require an enormous computational effort, which tends to fail as the problem size and its complexity increase, which is often the case. On the other hand, stochastic, biologically-inspired techniques, designed for global optimum calculation, frequently prove successful when applied to real life computational problems. While the area of bio-inspired algorithms (BIAs) is still relatively young, it is undergoing continuous, rapid development. Selection and tuning of the appropriate optimization solver for a particular task can be challenging and requires expert knowledge of the methods to be considered. Comparing the performance of viable candidates against a defined test bed environment can help in solving such dilemmas. This paper presents the benchmark results of two biologically inspired algorithms: covariance matrix adaptation evolution strategy (CMA-ES) and two variants of particle swarm optimization (PSO). COCO (COmparing Continuous Optimizers) – a platform for systematic and sound comparisons of real-parameter global optimization solvers was used to evaluate the performance of CMA-ES and PSO methods. Particular attention was paid to the effciency and scalability of both techniques.
EN
The present article reviews the application of Particle Swarm Optimization (PSO) algorithms to optimize a phrasing model, which splits any text into linguistically-motivated phrases. In terms of its functionality, this phrasing model is equivalent to a shallow parser. The phrasing model combines attractive and repulsive forces between neighbouring words in a sentence to determine which segmentation points are required. The extrapolation of phrases in the specific application is aimed towards the automatic translation of unconstrained text from a source language to a target language via a phrase-based system, and thus the phrasing needs to be accurate and consistent to the training data. Experimental results indicate that PSO is effective in optimising the weights of the proposed parser system, using two different variants, namely sPSO and AdPSO. These variants result in statistically significant improvements over earlier phrasing results. An analysis of the experimental results leads to a proposed modification in the PSO algorithm, to prevent the swarm from stagnation, by improving the handling of the velocity component of particles. This modification results in more effective training sequences where the search for new solutions is extended in comparison to the basic PSO algorithm. As a consequence, further improvements are achieved in the accuracy of the phrasing module.
EN
The paper presents the use of the Particle Swarm Optimization (PSO) algorithm to find the shortest trajectory connecting two defined points while avoiding obstacles. The influence of the inertia weight and the number of population adopted in the first iteration of the PSO algorithm was examined for the length of the sought trajectory. Simulation results showed that the proposed method achieved significant improvement compared to the linearly decreasing method technique that is widely used in literature.
EN
The purpose of this paper is to propose a new method for obtaining tensors expressing certain symmetries, called effective elasticity tensors, and their optimal orientation. The generally anisotropic tensor being the result of in situ seismic measurements describes the elastic properties of a medium. It can be approximated with a tensor of a specific symmetry class. With a known symmetry class and orientation, one can better describe geological structure elements like layers and fissures. A method used to obtain effective tensor in the previous papers (i.e. Danek & Slawinski 2015) is based on minimizing the Frobenius norm between the measured and effective tensor of a chosen symmetry class in the same coordinate system. In this paper, we propose a new approach for obtaining the effective tensor with the assumption of a certain symmetry class. The entry zeroing method assumes the minimization of the target function, being the measure of similarity with the form of the effective tensor for the specific class. The optimization of orientation is made by means of the Particle Swarm Optimization (PSO) algorithm and transformations were parameterised with quaternions. To analyse the obtained results, the Monte-Carlo method was used. After thousands of runs of PSO optimization, values of quaternion parts and tensor entries were obtained. Then, thousands of realizations of generally anisotropic tensors described with normal distributions of entries were generated. Each of these tensors was the subject of separate PSO optimization, and the distributions of rotated tensor entries were obtained. The results obtained were compared with solutions of the method based on the Frobenius distances (Danek et al. 2013).
EN
Particle swarm optimization (PSO) is a population-based stochastic optimization technique that can be applied to solve optimization problems. However, there are some defects for PSO, such as easily trapping into local optimum, slow velocity of convergence. This paper presents the simple butterfly particle swarm optimization algorithm with the fitness-based adaptive inertia weight and the opposition-based learning average elite strategy (SBPSO) to accelerate convergence speed and jump out of local optimum. SBPSO has the advantages of the simple butterfly particle swarm optimizer to increase the probability of finding the global optimum in the course of searching. Moreover, SBPSO benefits from the simple particle swarm (sPSO) to accelerate convergence speed. Furthermore, SBPSO adopts the opposition-based learning average elite to enhance the diversity of the particles in order to jump out of local optimum. Additionally, SBPSO generates the fitness-based adaptive inertia weight ω to adapt to the evolution process. Eventually, SBPSO presents a approach of random mutation location to enhance the diversity of the population in case of the position out of range. Experiments have been conducted with eleven benchmark optimization functions. The results have demonstrated that SBPSO outperforms than that of the other five recent proposed PSO in obtaining the global optimum and accelerating the velocity of convergence.
EN
The article seeks to clarify some concepts and principles that are used in constructing algorithms that utilize particle swarm as a tool for searching extremes of target functions, including heuristic algorithms. The author also draws attention to some philosophical aspects of creating metaphors by ordering basic ways of constructing the transition vectors.
PL
Niniejsza praca przybliża zagadnienia związane z wykorzystaniem metody roju cząstek (Particle Swarm Optimization - PSO) i kilku wariantów algorytmu RRT (Rapidly Exploring Random Tree) do poszukiwania optymalnej trajektorii pracy manipulatora typu SCARA. Zadanie polegało na określeniu najkrótszej drogi łączącej dwa zdefiniowane punkty z jednoczesnym ominięciem przeszkód. Wysokość przeszkód uniemożliwiała przenoszenie elementów ponad nimi, co sprowadziło zagadnienie do problemu dwuwymiarowego. W artykule analizowano wpływ wybranych parametrów każdego z algorytmów w celu znalezienia najkrótszej trajektorii. Na podstawie kinematyki odwrotnej wyznaczono poszczególne położenia ramion manipulatora dla wyznaczonej najkrótszej ścieżki roboczej.
EN
This paper presents application of Particle Swarm Optimization (PSO) method and a few variants of RRT (Rapidly Exploring Random Tree) algorithm to search for the optimal trajectory of SCARA manipulator work. The task was to calculate the shortest path connecting two defined points avoiding obstacles. It is assumed that the transfer of elements above obstacles was impossible, therefore the problem was considered in two-dimensional space. The influence of selected parameters of each method on algorithm performance and the study results has been analyzed. For the shortest trajectory the position manipulator arms has been calculated on the basis of inverse kinematics.
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