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EN
This paper addresses the problem of dimensionality reduction while preserving the characteristics of the Pareto set approximation in multiobjective optimization. The real-life engineering design problem for permanent magnet generator is considered. The Pareto front approximations with constraints, ranging from the five objectives to the set of two, are presented and compared.
PL
W artykule przedstawiono rozwiązanie problemu optymalizacji wielokryterialnej poprzez redukcję wymiarów w przestrzeni kryteriów. Rozważono właściwości zbioru Pareto w zadaniu projektowania generatora z magnesami trwałymi. Zaprezentowano i porównano aproksymacje frontu Pareto przy optymalizacji z ograniczeniami przy redukcji z pięciu dwóch kryteriów.
EN
The paper is devoted to the multiobjective shape optimization of cracked structures. The two main goals are: reduction of the negative crack influence of identified cracks and optimal design of structural elements to reduce the risk of crack occurrence and growth. NURBS (Non-Uniform Rational B-Splines) curves are used to model the structure boundaries. Global optimization methods in the form of evolutionary algorithms are employed. As different optimization criteria are considered simultaneously, the efficient multiobjective optimization method are applied. An in-house multiobjective evolutionary algorithm is proposed as an efficient optimization tool. The dual boundary element method is used to solve the boundaryvalue problem.
EN
Thermal ablation surgery serves as one of the main approaches to treat liver tumors. The pretreatment planning, which highly demands the experience and ability of the physician, plays a vital role in thermal ablation surgery. The planning of multiple puncturing is necessary for avoiding the possible interference, destroying the tumor thoroughly and minimizing the damage to healthy tissue. A GPU-independent pretreatment planning method is proposed based on multi-objective optimization, which takes the most comprehensive constraints into consideration. An adaptive decision method of closing kernel size based on Jenks Natural Breaks is utilized to describe the final feasible region more accurately. It should be noted that the reasonable procedure of solving the feasible region and the use of KD tree based high dimensional search approach are used to enhance the computational efficiency. Seven constraints are handled within 7 s without GPU acceleration. The Pareto front points of nine puncturing tests are obtained in 5 s by using the NSGA-II algorithm. To evaluate the maximum difference and similarity between the planning results and the puncturing points recommended by the physician, Hausdorff distance and overlap rate are respectively developed, the Hausdorff distances are within 30 mm in seven out of nine tests and the average value of overlap rate is 73.0% for all the tests. The puncturing paths of high safety and clinical-practice compliance can be provided by the proposed method, based on which the pretreatment planning software developed can apply to the interns' training and ability evaluating for thermal ablation surgery.
PL
Celem artykułu była analiza wielokryterialnego podejścia do planowania sieci łączności bezprzewodowej WLAN (Wireless Local Area Network) IEEE 802.11b/g z wykorzystaniem wybranych rojowych algorytmów optymalizacji. W procesie poszukiwania ekstremów wybranych dwóch i więcej funkcji kryterialnych zastosowano dwa algorytmy rojowe: kukułki MOCS (Multi Objective Cuckoo Search) oraz optymalizacji rojem cząstek MOPSO (Multi Objective Particle Swarm Optimisation). Dodatkowo, zaproponowano wykorzystanie oceny globalnej uzyskanych rozwiązań z zastosowaniem Metody Unitaryzacji Zerowanej MUZ.
EN
The aim of the article is analyze the multicriteria approach to IEEE 802.11b/g wireless LAN planning using selected swarm optimization methods. For this purpose, in the search extremes for two and three objective functions that applied two swarm algorithms: MOCS and MOPSO. In addition, it was proposed to perform a global assessment of solutions using the zero unitarisation method MUZ, the best results were furtheranalysed with performance metric (PM).
PL
Wielokryterialna optymalizacja pozwala na takie rozlokowanie różnych rodzajów infrastruktury zielonej, aby uzyskać najkorzystniejsze relacje pomiędzy kosztami i wszystkimi rozpatrywanymi korzyściami. Takie zadania są obecnie formułowane i rozwiązywane w różnych miejscach na świecie. Opisano tutaj model opracowany przez Agencję Ochrony Środowiska USA i nazywany SUSTAIN i kolejny przez grupę badawczą składającą się głównie z pracowników Swarthmore College oraz Uniwersytetu Johna Hopkinsa i zastosowany w projekcie infrastruktury zielonej obejmującym część miasta Filadelfia. Głównym powodem wybudowania tej infrastruktury była potrzeba zmniejszenia ilości ścieków i ładunków zanieczyszczeń zrzucanych przez ogólnospławny przelew burzowy.
EN
Multiobjective optimization methods are being used to predict the most efficient allocation of financial sources in order to achieve the best trade-off solution that balances costs and the various benefits. These methods have been applied in several locations around the world. The approach developed by the U. S. EPA, called SUSTAIN, which uses heuristic evolutionary optimization, is briefly described here. Also, a simplified multiobjective approach, called StormWISE, which uses linear programming, has been applied to reduce combined sewer overflows (CSO) in Philadelphia. Both approaches are briefly described.
EN
One of the most recent and interesting trends in intelligent scheduling is trying to reduce the energy consumption in order to obtain lower production costs and smaller carbon footprint. In this work we consider the energy-aware job shop scheduling problem, where we have to minimize at the same time an efficiency-based objective, as is the total weighted tardiness, and also the overall energy consumption. We experimentally show that we can reduce the energy consumption of a given schedule by delaying some operations, and to this end we design a heuristic procedure to improve a given schedule. As the problem is computationally complex, we design three approaches to solve it: a Pareto-based multiobjective evolutionary algorithm, which is hybridized with a multiobjective local search method and a linear programming step, a decomposition-based multiobjective evolutionary algorithm hybridized with a single-objective local search method, and finally a constraint programming approach. We perform an extensive experimental study to analyze our algorithms and to compare them with the state of the art.
7
Content available remote Multi-criteria analysis on spatial data of the landscape
EN
The method presented in this paper allows the visualization of spatial relations that could not be obtainedusing existing methods. Depending on the component maps that represent the analysis criteria, it ispossible to indicate certain ways to develop good or bad areas. These maps may contain visibility dataas well as other types of spatial impact. Thanks to this, spatial decision criteria can be broad and multi-branch.
EN
An efficient fuzzy interactive multi-objective optimization method is proposed to select the sub-optimal subset of genes from large-scale gene expression data. It is based on the binary particle swarm optimization (BPSO) algorithm tuned by a chaotic method. The proposed method is able to select the sub-optimal subset of genes with the least number of features that can accurately distinguish between the two classes, e.g. the normal and cancerous samples. The proposed method is evaluated on several publicly available microarray and RNA-sequencing gene expression datasets such as leukemia, colon cancer, central nervous system, lung cancer, ovarian cancer, prostate cancer and RNA-seq lung disease. The results indicate that the proposed method can identify the minimum number of genes to achieve the most accuracy, sensitivity and specificity in the classification process. Achieving 100% accuracy in six out of the seven datasets investigated in this study, demonstrates the high capacity of the proposed algorithm to find the sub-optimal subset of genes. This approach is useful in clinical applications to extract the most influential genes on a disease and to find the treatment procedure for the disease.
EN
The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance.
EN
P300 speller-based brain-computer interface (BCI) allows a person to communicate with a computer using only brain signals. In order to achieve better reliability and user continence, it is desirable to have a system capable of providing accurate classification with as few EEG channels as possible. This article proposes an approach based on multi-objective binary differential evolution (MOBDE) algorithm to optimize the system accuracy and number of EEG channels used for classification. The algorithm on convergence provides a set of pareto-optimal solutions by solving the trade-off between the classification accuracy and the number of channels for Devanagari script (DS)-based P300 speller system. The proposed method is evaluated on EEG data acquired from 9 subjects using a 64 channel EEG acquisition device. The statistical analysis carried out in the article, suggests that the proposed method not only increases the classification accuracy but also increases the over-all system reliabil-ity in terms of improved user-convenience and information transfer rate (ITR) by reducing the EEG channels. It was also revealed that the proposed system with only 16 channels was able to achieve higher classification accuracy than a system which uses all 64 channel's data for feature extraction and classification.
EN
A concept of the formed suction intake design obtained with an algorithm for vertical axial{ ow pumps is presented. The design methodology is a part of works conducted within project no. N N513 460240 supported by the Polish National Science Center. The proposed procedure is used to optimize intakes. The results of steady ow numerical computations in the suction intake as well as applications of the design optimization in the aspect of ful lling two objective functions are discussed. The objective functions given by the authors concern the optimal in ow of the uid into the impeller.
EN
The paper deals with the multiobjective and multiscale optimization of heterogeneous structures by means of computational intelligence methods. The aim of the paper is to find optimal properties of composite structures in a macro scale modifying their microstructure. At least two contradictory optimization criteria are considered simultaneously. A numerical homogenization concept with a representative volume element is applied to obtain equivalent macro-scale elastic constants. An in-house multiobjective evolutionary algorithm MOOPTIM is applied to solve the considered optimization tasks. The finite element method is used to solve the boundary-value problem in both scales. A numerical example is attached.
13
Content available remote Multiobjective optimization in two-scale thermoelastic problems for porous solids
EN
The multiobjective optimization of a two-scale thermoelastic problem is considered in this paper. To compute the solutions, direct thermoelastic analysis with the representative volume element (RVE) and the finite element method (FEM) analysis are performed. Evolutionary algorithms (EAs) are used to find a set of Pareto-optimal solutions. The design variables of the optimization problem are defined so as to describe the microstructure of a porous solid, whereas the optimization criteria are defined on the basis of macro-scale thermal and mechanical quantities. A numerical example of optimization is included.
EN
We consider the generalized Nash equilibrium as a solution concept for multiobjective optimal control problems governed by elliptic partial differential equations with constraints not only for the control but also for the state variables. In the first part, we present a constructive proof of the existence of a generalized Nash equilibrium via an approximating sequence of suitable finite dimensional discretizations. In the second part, we propose a variant of a potential reduction algorithm for the numerical solution of these discretized problems. In contrast to the existing numerical approaches ours does not require the computation of the control–to–state mapping. Instead we introduce different state variables and guarantee that they become equal at a solution. We prove sufficient conditions for the convergence of our algorithm to a solution. Furthermore, some numerical results showing the applicability are provided.
EN
We present in this paper a novel distributed solution to a security-aware job scheduling problem in cloud computing infrastructures. We assume that the assignment of the available resources is governed exclusively by the specialized brokers assigned to individual users submitting their jobs to the system. The goal of this scheme is allocating a limited quantity of resources to a specific number of jobs minimizing their execution failure probability and total completion time. Our approach is based on the Pareto dominance relationship and implemented at an individual user level. To select the best scheduling strategies from the resulting Pareto frontiers and construct a global scheduling solution, we developed a decision-making mechanism based on the game-theoretic model of Spatial Prisoner’s Dilemma, realized by selfish agents operating in the two-dimensional cellular automata space. Their behavior is conditioned by the objectives of the various entities involved in the scheduling process and driven towards a Nash equilibrium solution by the employed social welfare criteria. The performance of the scheduler applied is verified by a number of numerical experiments. The related results show the effectiveness and scalability of the scheme in the presence of a large number of jobs and resources involved in the scheduling process.
EN
The paper presents a methodology for the optimization of a Brushless Direct Current motor (BLDC). In particular it is focused on multiobjective optimization using a genetic algorithm (GA) developed in Matlab/Optimization Toolbox coupled with Maxwell from ANSYS. Optimization process was divided into two steps. The aim of the first one was to maximize the RMS torque value and to minimize the mass. The second part of the optimization process was to minimize the cogging torque by selecting proper magnet angle. The paper presents the methodology and capabilities of scripting methods rather than specific optimization results for the applied geometry.
EN
Delta connection of three-phase windings of brushless DC motors with the surface-mounted magnets contributes to rise of power loss due to the zero-sequence voltage induced by triplen (3, 9, 15, 21, …) harmonics of main flux. Partial control over this undesired effect can be accomplished by means of modification of winding distribution or an application of larger than normally angle of stack skew. This work attempts to reduce these harmonics by means of application of small skew angle along with the magnetic circuit design using a finite element model. The surrogacy-assisted two-objective genetic optimization of motor's magnetic circuit ensures small losses due to zero-sequence current and locates the motor efficiency at 91 per cent of that of basic motor configuration with phases connected in wye.
PL
Skojarzenie pasm uzwojeń w trójfazowych silnikach bezszczotkowych wzbudzanych magnesami trwałymi w trójkąt skutkuje wzrostem strat wywołanych składową zerową siły elektromotorycznej rotacji indukowanej przez tzw. potrojone (3, 9, 15, 21, …) harmoniczne strumienia głównego. Redukcja tego zjawiska może być osiągnięta za pomocą odpowiedniego rozłożenia cewek lub zastosowania większego niż normalnie kąta skosu rdzenia. W niniejszej pracy analizowana jest także możliwość redukcji składowej zerowej prądu oraz zachowania wartości parametrów eksploatacyjnych maszyny metodą optymalizacji obwodu magnetycznego. Optymalizację przeprowadzono przy zastosowaniu wielokryterialnego algorytmu genetycznego oraz metamodelu utworzonego metodą Krigingu na podstawie obliczeń polowych. W wyniku optymalizacji uzyskano silnik o uzwojeniu skojarzonym w trójkąt, której sprawność jest mniejsza o 9 % od sprawności maszyny pracującej z uzwojeniem skojarzonym w gwiazdę.
18
Content available A multivariable multiobjective predictive controller
EN
Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the Non- Dominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.
EN
In present study the problem of an alarm systems optimization was presented and the stages of the multicriteria structured optimization method in cost-functional characteristics of the alarm system were also described. The method represents a combination of the proposed reflection and selection schemes in the genetic algorithm that solves the extreme combinatorics problem.
PL
W proponowanym badaniu przedstawiono problem optymalizacji systemów alarmowych oraz zostały opisane etapy metody wielokryterialnej strukturalnej cenowo-funkcjonalnej optymalizacji systemu alarmowego. Metoda stanowi kombinację refleksji i planu wyboru algorytmu genetycznego, który rozwiązuje problem ekstremalnej kombinatoryki.
EN
Among Evolutionary Multiobjective Optimization Algorithms (EMOA) there are many which find only Paretooptimal solutions. These may not be enough in case of multimodal problems and non-connected Pareto fronts, where more information about the shape of the landscape is required. We propose a Multiobjective Clustered Evolutionary Strategy (MCES) which combines a hierarchic genetic algorithm consisting of multiple populations with EMOA rank selection. In the next stage, the genetic sample is clustered to recognize regions with high density of individuals. These regions are occupied by solutions from the neighborhood of the Pareto set. We discuss genetic algorithms with heuristic and the concept of well-tuning which allows for theoretical verification of the presented strategy. Numerical results begin with one example of clustering in a single-objective benchmark problem. Afterwards, we give an illustration of the EMOA rank selection in a simple two-criteria minimization problem and provide results of the simulation of MCES for multimodal, multi-connected example. The strategy copes with multimodal problems without losing local solutions and gives better insight into the shape of the evolutionary landscape. What is more, the stability of solutions in MCES may be analyzed analytically.
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