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EN
The paper presents an evolutionary multi-objective approach to automatically generate morphological filters to solve unknown distances areas, found in depth images used by real-time embedded systems for visually impaired people, and to prevent accidents. It was used Cartesian Genetic Programming as base for the NSGAII multi-objective optimization algorithm proposed to optimize two objectives: low error rates for quality x low complexity for speed. Results showed this approach was able to deliver feasible solutions with good quality and speed to be used in real-time systems.
PL
W artykule zaprezentowano metodę ewolucyjną do automatycznego generowania morfologicznego filtru do określania brakujących danych w obrazach ludzi otrzymywanych on-line. Użyto programu Cartesian Genetic do optymalizacji algorytmu. Zastosowane rozwiązanie umożliwiało dostarczanie poprawę szybkości o dokładności przetwarzania obrazu.
EN
By the green point of view, supply chain management (SCM), which contains supplier and location selection, production, distribution, and inventory decisions, is an important subject being examined in recent years by both practitioners and academicians. In this paper, the closed-loop supply chain (CLSC) network that can be mutually agreed by meeting at the level of common satisfaction of conflicting objectives is designed. We construct a multi-objective mixed-integer linear programming (MOMILP) model that allows decision-makers to more effectively manage firms’ closed-loop green supply chain (SC). An ecological perspective is brought by carrying out the recycling, remanufacturing and destruction to SCM in our proposed model. Maximize the rating of the regions in which they are located, minimize total cost and carbon footprint are considered as the objectives of the model. By constructing our model, the focus of customer satisfaction is met, as well as the production, location of facilities and order allocation are decided, and we also carry out the inventory control of warehouses. In our multi-product multi-component multi-time-period model, the solution is obtained with a fuzzy approach by using the min operator of Zimmermann. To illustrate the model, we provide a practical case study, and an optimal result containing a preferable level of satisfaction to the decision-maker is obtained.
EN
As neuron models become more plausible, fewer computing units may be required to solve some problems; such as static pattern classification. Herein, this problem is solved by using a single spiking neuron with rate coding scheme. The spiking neuron is trained by a variant of Multi-objective Particle Swarm Optimization algorithm known as OMOPSO. There were carried out two kind of experiments: the first one deals with neuron trained by maximizing the inter distance of mean firing rates among classes and minimizing standard deviation of the intra firing rate of each class; the second one deals with dimension reduction of input vector besides of neuron training. The results of two kind of experiments are statistically analyzed and compared again a Mono-objective optimization version which uses a fitness function as a weighted sum of objectives.
4
Content available remote Bi-objective routing in a dynamic network: An application to maritime logistics
EN
A bi-objectiveMILP model for optimal routing in a dynamic network with moving targets (nodes) is developed, where all targets are not necessarily visited. Hence, our problem extends the moving target travelling salesman problem. The two objectives aim at finding the sequence of targets visited in a given time horizon by minimizing the total travel distance and maximizing the number of targets visited. Due to a huge number of binary variables, such a problem often becomes intractable in the real life cases. To reduce the computational burden, we introduce a measure of traffic density, based on which we propose a time horizon splitting heuristics. In a real-world case study of greenhouse gas emissions control, using Automatic Identification System data related to the locations of ships navigating in the Gulf of Finland, we evaluate the performance of the proposed method. Different splitting scenarios are analysed numerically. Even in the cases of a moderate scale, the results show that near-efficient values for the two objectives can be obtained by our splitting approach with a drastic decrease in computational time compared to the exact MILP method. A linear value function is introduced to compare the Pareto solutions obtained by different splitting scenarios. Given our results, we expect that the present study is valuable in logistic applications, specifically maritime management services and autonomous navigation.
5
Content available remote The algorithm of multi-objective optimization of PM synchronous motors
EN
This paper presents multi-objective algorithm for optimal designing of permanent magnet synchronous motors. The special attention is paid on the formulation the optimization problem, especially on the correct selection of the partial criteria which constitute multi-objective function and constraints. It is pointed out that connection of multimodal parameter (cogging torque) and unimodal parameter (electromagnetic torque) in one multi-objective compromise function can lead to erroneous operation of optimization algorithm. Therefore, decomposition of the optimization task into two-level is proposed. The optimization calculation has been executed for permanent magnet synchronous motor structure with hybrid excitation system.
PL
W artykule przedstawiono algorytm do optymalizacji magnetoelektrycznych silników synchronicznych. Przedstawiono rozważania dotyczące poprawnego formułowania kompromisowych funkcji celu, w szczególności odpowiedniego doboru kryteriów cząstkowych. Wykazano, że włączenie do kompromisowej funkcji jednocześnie członu reprezentującego elektromagnetyczny moment użyteczny i moment zaczepowy może prowadzić do błędnego działania algorytmu optymalizacji. Zaproponowano dekompozycję zadania optymalizacji na dwa etapy. Przedstawiono i omówiono wybrane wyniki obliczeń optymalizacyjnych dla magnetoelektrycznego silnika synchronicznego z hybrydowym układem wzbudzenia.
6
Content available remote Automated optimal design of wells for electromagnetic cell stimulation
EN
In the paper, a device for in vitro electromagnetic stimulation of cells at low frequency (75 Hz) is considered. In particular, shape and position of a well-plate are identified in order to obtain a homogeneous stimulation and to maximize the space allotted to cell culture. To this end, the BiMO and  -BiMO optimization algorithms, which have shown good performances in multi-objective optimization of electromagnetic devices, are applied.
PL
W artykule opisano urządzenie do elektromagnetycznej stymulacji komórek metodą in vitro z wykorzystaniem sygnału o niskiej częstotliwości (75 Hz). W szczególności rozważane były kształt i położenie płytki do hodowli komórkowej w celu uzyskania jednorodnej stymulacji i maksymalizacji przestrzeni obejmującej hodowlę komórkową. W tym celu zastosowano algorytmy optymalizacji BiMO i  -BiMO, które umożliwiły optymalizację wielokryterialną urządzeń elektromagnetycznych.
EN
Combinatorial optimization challenges are rooted in real-life problems, continuous optimization problems, discrete optimization problems and other significant problems in telecommunications which include, for example, routing, design of communication networks and load balancing. Load balancing applies to distributed systems and is used for managing web clusters. It allows to forward the load between web servers, using several scheduling algorithms. The main motivation for the study is the fact that combinatorial optimization problems can be solved by applying optimization algorithms. These algorithms include ant colony optimization (ACO), honey bee (HB) and multi-objective optimization (MOO). ACO and HB algorithms are inspired by the foraging behavior of ants and bees which use the process to locate and gather food. However, these two algorithms have been suggested to handle optimization problems with a single-objective. In this context, ACO and HB have to be adjusted to multiobjective optimization problems. This paper provides a summary of the surveyed optimization algorithms and discusses the adaptations of these three algorithms. This is pursued by a detailed analysis and a comparison of three major scheduling techniques mentioned above, as well as three other, new algorithms (resulting from the combination of the aforementioned techniques) used to efficiently handle load balancing issues.
EN
In this paper we present a complex strategy for the solution of ill posed, in-verse problems formulated as multiobjective global optimization ones. The strategy is capable of identifying the shape of objective insensitivity regions around connected components of Pareto set. The goal is reached in two phases. In the first, global one, the connected components of the Pareto set are localized and separated in course of the multi-deme, hierarchic memetic strategy HMS. In the second, local phase, the random sample uniformly spread over each Pareto component and its close neighborhood is obtained in the specially profiled evolutionary process using multiwinner selection. Finally, each local sample forms a base for the local approximation of a dominance function. Insensitivity region surrounding each connected component of the Pareto set is estimated by a sufficiently low level set of this approximation. Capabilities of the whole procedure was verified using specially-designed two-criterion benchmarks.
PL
Artykuł prezentuje złożoną strategię rozwiązywania źle postawionych problemów odwrotnych sformułowanych jako wielokryterialne zadania optymalizacji globalnej. Opisana strategia umożliwia identyfikację obszarów niewrażliwości funkcji celu wokół spójnych składowych zbioru Pareto. Cel jest osiągany w dwu etapach. W pierwszym z nich — globalnym — składowe spójne zbioru Pareto są lokalizowane i separowane przy pomocy wielopopulacyjnej hierarchicznej strategii memetycznej HMS. W etapie drugim — lokalnym — przy użyciu specjalnie sprofilowanego procesu ewolucyjnego wykorzystującego operator selekcji wyborczej z wieloma zwycięzcami produkowana jest losowa próbka rozłożona jednostajnie na każdej składowej i jej bliskim otoczeniu. Finalnie każda lokalna próbka jest użyta jako baza do zbudowania lokalnej aproksymacji funkcji dominacji. Zbiory poziomicowe tej aproksymacji dla odpowiednio niskich poziomów stanowią przybliżenie zbiorów niewrażliwości wokół składowych spójnych. Możliwości strategii zostały zweryfikowane przy użyciu specjalnie zaprojektowanych dwukryterialnych funkcji testowych.
9
EN
In this paper a scaling approach for the solution of 2D FE models of electric machines is proposed. This allows a geometrical and stator and rotor resistance scaling as well as a rewinding of a squirrel cage induction machine enabling an efficient numerical optimization. The 2D FEM solutions of a reference machine are calculated by a model based hybrid numeric induction machine simulation approach. In contrast to already known scaling procedures for synchronous machines the FEM solutions of the induction machine are scaled in the stator-current-rotor-frequency-plane and then transformed to the torque-speed-map. This gives the possibility to use a new time scaling factor that is necessary to keep a constant field distribution. The scaling procedure is validated by the finite element method and used in a numerical optimization process for the sizing of an electric vehicle traction drive considering the gear ratio. The results show that the scaling procedure is very accurate, computational very efficient and suitable for the use in machine design optimization.
PL
Celem artykułu było porównanie trzech wybranych algorytmów optymalizacji wielokryterialnej w planowaniu sieci WLAN standardu 802.11b/g w środowisku wewnątrzbudynkowym z infrastrukturą. Zaproponowano wykorzystanie Metody Unitaryzacji Zerowanej (MUZ) do wyboru algorytmu i parametrów jego działania oraz najlepszego rozwiązania.
EN
The aim of the article was to compare three selected algorithms of multi-criteria indoor WLAN 802.11b/g planning with infrastructure. It has been proposed to use method (MUZ) for both the choice of algorithm and its operating parameters as well as the best solution.
EN
Finding an acceptable compromise between various objectives is a necessity in the design of contemporary microwave components and circuits. A primary reason is that most objectives are at least partially conflicting. For compact microwave structures, the design trade-offs are normally related to the circuit size and its electrical performance. In order to obtain comprehensive information about the best possible trade-offs, multi-objective optimization is necessary that leads to identifying a Pareto set. Here, a framework for fast multi-objective design of compact micro-strip couplers is discussed. We use a sequential domain patching (SDP) algorithm for numerically efficient handling of the structure bandwidth and the footprint area. Low cost of the process is ensured by executing SDP at the low-fidelity model level. Due to its bi-objective implementation, SDP cannot control the power split error of the coupler, the value of which may become unacceptably high along the initial Pareto set. Here, we propose a procedure for correction of the S-parameters’ characteristics of Pareto designs. The method exploits gradients of power split and bandwidth estimated using finite differentiation at the patch centres. The gradient data are used to correct the power split ratio while leaving the operational bandwidth of the structure at hand intact. The correction does not affect the computational cost of the design process because perturbations are pre-generated by SDP. The final Pareto set is obtained upon refining the corrected designs to the high-fidelity EM model level. The proposed technique is demonstrated using two compact microstrip rat-race couplers. Experimental validation is also provided.
EN
In this paper, we are concerned with a multi-objective fractional extremal programming problem. Using the concept of subdifferential of cone-convex set valued mappings, introduced by Baier and Jahn (1999), together with the convex separation principle, we give necessary optimality conditions. An example illustrating the usefulness of our results is also provided.
EN
Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.
EN
Hazardous materials transportation should consider risk equity and transportation risk and cost. In the hazardous materials transportation process, we consider risk equity as an important condition in optimizing vehicle routing for the long-term transport of hazardous materials between single or multiple origin-destination pairs (O-D) to reduce the distribution difference of hazardous materials transportation risk over populated areas. First, a risk equity evaluation scheme is proposed to reflect the risk difference among the areas. The evaluation scheme uses standard deviation to measure the risk differences among populated areas. Second, a risk distribution equity model is proposed to decrease the risk difference among populated areas by adjusting the path frequency between O-D pairs for hazardous materials transportation. The model is converted into two sub models to facilitate decision-making, and an algorithm is provided for each sub model. Finally, we design a numerical example to verify the accuracy and rationality of the model and algorithm. The numerical example shows that the proposed model is essential and feasible for reducing the complexity and increasing the portability of the transportation process.
EN
In induction heating the design of the inductor implies the solution of coupled electromagnetic and thermal fields, along with the use of optimal design procedures to identify the best possible device or process. The benchmark model proposed, a graphite disk heated by means of induction, is optimized using different optimization algorithms. The design aim requires to achieve a prescribed and uniform temperature distribution in the workpiece maximizing the system efficiency.
16
Content available remote Modelling of production processes with the use of witness simulator
EN
Customization of final products forces the so-called "make-to-order" production. In the article, there is a presentation of influence of EPEI changes (Every Part Every Interval indicator) on the efficiency and effectiveness of complex production system. The use of Witness System Simulation Modeling helped to create the model of real object which has been validated in the real work parameters. All of the times of the realized processes have been simulated as random variables with certain density of probability distribution.
PL
Customizacja wyrobów finalnych wymusza produkcję na zasadach make - to - order. W artykule przedstawiono wpływ zmiany wskaźnika EPEI na zmiany wydajności i efektywności złożonego systemu produkcyjnego. Z wykorzystaniem programu Witness System Simulation Modeling opracowano model obiektu rzeczywistego, który następnie został poddany walidacji w zbieżnych z rzeczywistością parametrów pracy. Wszystkie czasy realizowanych procesów zostały w programie Witness zasymulowane jako zmienne losowe o właściwych dla siebie gęstościach rozkładu prawdopodobieństwa.
EN
Adaptive robust PID sliding mode control optimized by means of multi-objective genetic algorithm is presented in this paper to control a three-tank liquid level system with external disturbances. While PID constitutes a reliable and stable controller, when compared to sliding mode control (SMC); robustness and tracking performance of SMC are higher than those of the PID control. To use the unique features of both controllers, optimal sliding mode control is executed in terms of a supervisory controller to enhance the performance of optimal adaptive PID control and to provide the necessary control inputs. After the design of the control law, control coefficients of all four involved controllers are optimized by using the multi-objective genetic algorithm so as to minimize errors and the input of the controller. Simulations illustrate that the adaptive robust PID sliding controller based on multi-objective genetic algorithm optimization provides a superior response in comparison to the results obtained separately by PID control, sliding mode control, and adaptive PID control, respectively.
EN
The idea of a new evolutionary algorithm with memory aspect included is proposed to find multiobjective optimized solution of vehicle routing problem with time windows. This algorithm uses population of agents that individually search for optimal solutions. The agent memory incorporates the process of learning from the experience of each individual agent as well as from the experience of the population. This algorithm uses crossover operation to define agents evolution. In the paper we choose as a base the Best Cost Route Crossover (BCRC) operator. This operator is well suited for VPRTW problems. However it does not treat both of parent symmetrically what is not natural for general evolutionary processes. The part of the paper is devoted to find an extension of the BCRC operator in order to improve inheritance of chromosomes from both of parents. Thus, the proposed evolutionary algorithm is implemented with use of two crossover operators: BCRC and its extended-modified version. We analyze the results obtained from both versions applied to Solomon’s and Gehring & Homberger instances. We conclude that the proposed method with modified version of BCRC operator gives statistically better results than those obtained using original BCRC. It seems that evolutionary algorithm with memory and modification of Best Cost Route Crossover Operator lead to very promising results when compared to the ones presented in the literature.
EN
The paper presents a multi-objective optimization framework to the network resource allocation problem, where the aim is to maximize the bitrates of data generated by all agents executed in a distributed system environment. In the proposed approach, the utility functions of agents may have different forms, which allows a more realistic modeling of phenomena occurring in computer networks. A scalarizing approach has been applied to solve the optimization problem.
20
Content available remote System doboru i optymalizacji parametrów przekształtnika sieciowego AC-DC
PL
Artykuł prezentuje system doboru i optymalizacji parametrów przekształtnika sieciowego AC-DC dedykowanego dla układów rozproszonych. Proponowane rozwiązanie bazuje na metodach dyskretnej optymalizacji wielokryterialnej wykorzystujących algorytmy ewolucyjne i jest narzędziem wspierającym proces projektowania przekształtnika energoelektronicznego. W artykule przedstawiono założenia i działanie systemu, proces projektowania oraz optymalizacji parametrów przekształtnika realizowany przez opracowane środowisko optymalizacyjne.
EN
This paper presents system for design and optimization of the parameters of grid connected converter dedicated for distributed systems. Introduced solution is based on multi-objective discrete optimization and supports process of the AC-DC converter design. Paper presents foundations and basic system properties, design and optimization process and selected optimization results.
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