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This paper presents fuzzy ${c}$ -means-based yield estimation (FUZYE), a methodology that reduces the time impact caused by Monte Carlo (MC) simulations in the context of analog integrated circuits (ICs) yield estimation, enabling it for yield optimization with population-based algorithms, e.g., the genetic algorithm (GA). MC analysis is the most general and reliable technique for yield estimation, yet the considerable amount of time it requires has discouraged its adoption in population-based optimization tools. The proposed methodology reduces the total number of MC simulations that are required, since, at each GA generation, the population is clustered using a fuzzy ${c}$ -means (FCMs) technique, and, only the representative individual (RI) from each cluster is subject to MC simulations. This paper shows that the yield for the rest of the population can be estimated based on the membership degree of FCM and RIs yield values alone. This new method was applied on two real circuit-sizing optimization problems and the obtained results were compared to the exhaustive approach, where all individuals of the population are subject to MC analysis. The FCM approach presents a reduction of 89% in the total number of MC simulations, when compared to the exhaustive MC analysis over the full population. Moreover, a ${k}$ -means-based clustering algorithm was also tested and compared with the proposed FUZYE, with the latest showing an improvement up to 13% in yield estimation accuracy. [ABSTRACT FROM AUTHOR]

In many diagnostic imaging settings, including positron emission tomography (PET), images are typically used for multiple tasks such as detecting disease and quantifying disease. Unlike conventional image reconstruction that optimizes a single objective, this work proposes a multi-objective optimization algorithm for PET image reconstruction to identify a set of images that are optimal for more than one task. This work is reliant on a genetic algorithm to evolve a set of solutions that satisfies two distinct objectives. In this paper, we defined the objectives as the commonly used Poisson log-likelihood function, typically reflective of quantitative accuracy, and a variant of the generalized scan-statistic model, to reflect detection performance. The genetic algorithm uses new mutation and crossover operations at each iteration. After each iteration, the child population is selected with non-dominated sorting to identify the set of solutions along the dominant front or fronts. After multiple iterations, these fronts approach a single non-dominated optimal front, defined as the set of PET images for which none the objective function values can be improved without reducing the opposing objective function. This method was applied to simulated 2D PET data of the heart and liver with hot features. We compared this approach to conventional, single-objective approaches for trading off performance: maximum likelihood estimation with increasing explicit regularization and maximum a posteriori estimation with varying penalty strength. Results demonstrate that the proposed method generates solutions with comparable to improved objective function values compared to the conventional approaches for trading off performance amongst different tasks. In addition, this approach identifies a diverse set of solutions in the multi-objective function space which can be challenging to estimate with single-objective formulations. [ABSTRACT FROM AUTHOR]

Dense stereo image matching in remotely sensed images is a challenging problem, though it has been studied for more than two decades, due to occlusions, discontinuities, geometric, and radiometric distortions. A novel multiobjective fitness function-based dense stereo matching approach using genetic algorithms (GAs) is proposed in this paper. The proposed method is useful for estimating dense disparity map with an improved number of inliers for a stereo image pair, despite the constraint of finding correct disparity at depth discontinuities. In this paper, the steps of GA, such as initialization of the population, fitness function, and crossover and mutation operation, are designed and implemented to effectively deal with the problem of dense stereo image matching. To initialize the population, a Scale Invariant Feature Transform (SIFT) descriptor is computed for each pixel and multiple-size window-based matching is performed, using the similarity measures: 1) Euclidean distance and 2) spectral angle mapper. The generated disparity maps are pruned to choose a suitable subset using the designed fitness functions, considering the constraints related to stereo image pair, such as epipolar constraint, which encodes the epipolar geometry and the similarity measure that is useful to decide accuracy of the correspondences. The two objective functions are the number of inliers computed using the fundamental matrix and an energy minimization function, considering discontinuities and occlusions. The usefulness of this approach for remotely sensed stereo image pairs is demonstrated by improving the number of inliers and favorably comparing with state-of-the-art dense stereo image matching methods. [ABSTRACT FROM AUTHOR]

In order to obtain a good optimization method for the electrical transformer design with optimal selection of parameters, performance evaluation of three evolutionary algorithms (EAs), namely, genetic algorithm (GA), differential evolution algorithm, and nondominated sorting GA (NSGA-II), is carried out. The aim of this paper is to optimize parameters of transformer design (core thicknesses, primary-turn number, secondary-turn number, primary conductor area, and secondary conductor area) for minimization of total power losses (no-load losses and load losses) in three-phase transformer topology while maintaining high efficiency and low cost. The method used for this optimization scheme combines the finite-element method (FEM) and EAs to provide an accurate selection of parameters together with the optimized magnetic flux density and decreased loss. Experimental results show that NSGA-II+FEM model successfully provides a global feasible solution by minimizing total loss and related cost while improving the efficiency of three-phase transformer, rendering it suitable for application in the design environment of industrial transformers. [ABSTRACT FROM AUTHOR]

This paper compares three modern and two classical multiobjective optimizers (MOOs) as applied to real-world problems in electromagnetics. The behavior of sophisticated optimizers on simple test functions has been studied exhaustively. In contrast, the algorithms here are tested on practical applications, where the function evaluations are computationally expensive, making the convergence rate a crucial factor. The examples considered include the optimization of a narrowband slot antenna, a mushroom-type electromagnetic bandgap structure, and an ultrawideband Vivaldi antenna. Another popular topic in the literature is in comparing classical MOOs on electromagnetics problems. The modern optimizers chosen in this paper are state of the art and each has a distinct design philosophy. This paper introduces two unique MOOs to the electromagnetics community: BORG, an auto-adaptive genetic algorithm and the Multi-Objective Covariance Matrix Adaptation Evolutionary Strategy (MO-CMA-ES), an extension of the popular single-objective CMA-ES. These algorithms are compared to the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), a Chebysheff scalarization algorithm, and two classical MOOs. This paper will study the behavior of these algorithms on problems in electromagnetics with a limited number of function evaluations using five distinct metrics and will provide useful guidelines and recommended optimizer settings. [ABSTRACT FROM AUTHOR]

This paper presents the fast topology optimization methods for rotating machines based on deep learning. The cross-sectional image of electric motors and their performances obtained during a multi-objective topology optimization based on the finite-element method and genetic algorithm (GA) is used for training of the convolutional neural network (CNN). Two different approaches are proposed: 1) CNN trained by preliminary optimization with a small population for GA is used for the main optimization with a large population and 2) CNN is used for screening of torque performances in the optimization with respect to the motor efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid genetic algorithm (GA) for the optimal shape design of an axially symmetric dual-reflector antenna by combining the GA with the moving least square (MLS), which enhances the convergence rate and the global search performance. The MLS is used to construct local interpolation functions from non-uniform sample data and to estimate new superior positions. By combining these superior positions in the next generation, the MLS-GA shows better search performance for the global optimum and a faster convergence rate than those of the conventional GA. To verify the proposed MLS-GA, we applied it to the optimal shape design of the DRA at the W-band. [ABSTRACT FROM AUTHOR]

Brain storm optimization (BSO) is a swarm intelligence optimization algorithm inspired by the collective behavior of human beings in solving problems. This paper provides a conceptual overview and detailed explanation of the BSO algorithm. For the first time, the effectiveness of BSO in electromagnetic applications is demonstrated using a Yagi–Uda antenna design and a Luneburg lens antenna design, and this effectiveness is further validated via optimization, prototyping, and measurement of a dual-band slotted patch antenna design. In addition, a novel binary version of BSO (BBSO) for optimizing discrete problems is proposed, and the process of transforming the concept of BSO into a binary language is discussed in detail. A method for selecting algorithm parameter values for BBSO is shown. Finally, BBSO is applied successfully to an array thinning example and a pixelated patch antenna design. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid algorithm based on the genetic algorithm (GA) and the evolution strategy (ES) for the electromagnetic optimization problem. The GA is not good enough at times in searching the optimal solution from the view point of the convergence speed and the solution quality, while the ES has the risk of being trapped in a local minimum. The hybrid algorithm is composed of GA and ES in order to make up for these defects. First, we reached the vicinity of optimal solution using the GA. Then, the ES is used to find the accurate optimal solution. The switching point can be a main issue, which is also resolved in this paper. First, the performance of the convergence speed and the solution accuracy are comparatively tested using the known functions. In addition, the optimized design of the 2.45 GHz coplanar waveguide-fed circularly polarized antenna is carried out as a practical application. Only the GA and the hybrid algorithm reach the satisfactory value, and the more rapid convergence can be shown by the ES in this hybrid method after 380 iterations. [ABSTRACT FROM AUTHOR]

Power converters systems aimed at renewable energy applications have become a common option for sustainable electricity and distributed generation, since their performance has improved, and prices have steadily been reduced in the last years. However, there are still several drawbacks that hinder their widespread installation, such as the simultaneous minimization of cost and volume, efficiency maximization, size reduction, etc. Quite often, accomplishing these goals requires dealing with complicated optimization problems, which are difficult to solve by classical methods. Metaheuristic techniques provide a viable alternative to solve complex intricate optimization problems, such as those encountered in the development of power electronics converters. This paper presents a comprehensive coverage of metaheuristic methodologies applied in the area of power converters. The review includes a classification of the methodologies and main objective functions in each paper surveyed. An aim for this paper is to highlight the importance of the optimization tools, and the many benefits they provide to tackle the challenges encountered in the design, operation, and control of power converters. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) have become the standards and paradigms for solving inverse problems. However, their two inherited operations, namely, the crossover and mutation operations, are complicated and difficult, both in theory and in numerical implementations. In this regard, increasing efforts have been devoted to EAs which are based on probabilistic models (EAPMs) to overcome the shortcomings of available EAs. The population-based incremental learning (PBIL) is an EAPM; moreover, it can bridge the gap between machine learning and the EAs, hence enjoying several merits compared with other EAs. However, lukewarm efforts have been devoted to PBILs, especially the real coded PBILs, in the study of inverse problems in electromagnetics. In this regard, a novel real coded PBIL is being proposed in this paper. In the proposed real coded PBIL, a probability matrix is proposed to randomly generate a population, and the updating formulas for this probability matrix using the so far searched best solution and the best solution of the current population are introduced to strike a balance between convergence performance and solution quality. The proposed real coded PBIL algorithm is numerically experimented on several case studies and promising results are reported in this paper. [ABSTRACT FROM PUBLISHER]

An optimum operating strategy is needed for early amortization of expensive hybrid electric vehicle powertrain parts. Especially in the operation of long-haul trucks, fuel costs have a large impact on the total cost of ownership, which is the key entrepreneurial figure in the transportation business. Combined with route information, a predictive powertrain control (PPC) system increases the fuel-saving. In a MATLAB/Simulink model-based generic approach, the operating strategy and the PPC are optimized using a genetic algorithm. The tradeoff between minimizing the fuel consumption and simultaneously maximizing the vehicle speed and gradeability to decrease time-related fixed costs has to be solved. This leads to a multi-optimization problem. The operating strategy is developed for a parallel hybrid topology that includes the fuel-saving functions of regenerative braking, boosting, shifting the load point, and electric drive only operation. The methodology developed answers the search for an optimum control parameter setup combining the operational strategy and the PPC system in long-haul operations. This paper describes the model building, simulation, and optimization of a rule-based control strategy. The route profile and fuel consumption of an internal combustion engine long haul truck were measured in a real-life test run. The recorded data are used for model building and to validate the simulation tool. With an optimized parameter setup, fuel-saving of up to 11% were achieved in theory with acceptable vehicle speed and gradeability. [ABSTRACT FROM AUTHOR]

The structure and trigger control strategy have become the most important factors that restrict the performance of the multistage synchronous induction coilgun (MSSICG). However, it is still a difficult task to design MSSICG under overload constraint due to coupling between the multiple parameters. In this paper, the maximization of the emission efficiency and acceleration stationarity is treated as a multiobjective optimization problem. By analyzing the relationship between the number of turns and the other structural parameters of the launch, the multiobjective optimization model of MSSICG is established by the current filament method which was verified by the experimental data and finite-element method. And then the second generation nondominated sorting genetic algorithm (NSGA-II) and multiobjective particle swarm optimization (MOPSO) were employed to optimize the model in order to maximize the energy transfer efficiency while achieving the smooth acceleration of the armature. With the formulated optimization model, a five-stage synchronous induction coilgun is optimized as a special case. A decision-making procedure based on the fuzzy membership function is used for obtaining best compromise solution from the set of Pareto-solutions obtained through NSGA-II and MOPSO. In addition, the optimization performance of the proposed multiobjective optimization model and the single-objective optimization model of the MSSICG was compared. The result of optimization shows that the proposed multiobjective optimization model of MSSICG can effectively improve the performance of the coilgun compared with the single-objective optimization model which takes of the launch velocity and overload acceleration as the combination objective function or only the launch velocity. [ABSTRACT FROM PUBLISHER]

Fast-charging stations are connected to the electric grid and can fully charge an electric vehicle (EV) in less than half an hour. The capacity and location of the charging stations bring the costs to the electric grid operator, as well as to the station owner and EV user. A zonal approach has been proposed in this paper to determine the optimal place and capacity of fast-charging stations. Station development cost and the expected costs incurred by the EV user and the grid operator due to EV charging have also been included in the proposed approach. The geographic characteristics associated with the electric substations, urban roads, and city zones have also been considered in the proposed approach. EV user behavior is also considered to determine the expected charging demand and the expected EV user cost. The expected cost associated with extra grid loss due to EV charging has also been calculated by ac power flow using hourly electric grid load scenarios. The problem is formulated as a mixed-integer nonlinear (MINLP) problem to minimize the total expected EV charging costs. The problem is solved using the genetic algorithm technique to determine the optimal location and capacity of charging stations. The proposed approach has been applied to study charging station development for the northwest area of Tehran, Iran, and the robustness of the proposed approach has been evaluated. [ABSTRACT FROM PUBLISHER]

With the advancement of radar technology, detecting objects, determining the structure of the target, and estimating the direction and the speed is prominently increasing. There is no doubt that small cross section targets are hardly identified and determined. This problem demands the need for antenna pattern synthesizing with a high gain and highly reduced sidelobe level (SLL). In this paper, an optimization technique called self-adaptive differential evolution (SaDE) that can be able to learn and behave intelligently is integrated to determine an optimal set of excitation weights in the design of elliptical–cylindrical antenna array (ECAA). In this work, the SaDE-optimized hyper beam is proposed to achieve qualified radar performances. Nonuniform excitation amplitudes of the individual array elements of ECAA are performed to obtain reduced SLL, high directivity, and flexible radiation pattern which are crucial in radar applications. With increased antenna gain, small cross section objects can be accurately identified and determined even in volatile environment where there are different natures of interferences. Besides, the proposed work enables steering electronically and it allows scanning very rapidly across a wide range of elevation and azimuth planes. To evaluate the improved performance of the proposed optimization scheme, comparison is done with genetic algorithm (GA), particle swarm optimization (PSO), biogeography-based optimization (BBO), and invasive weed optimization (IWO) against different parameters. In general, the proposed work of pattern synthesis has resulted in much better reduction in SLL and first null beamwidth (FNBW) than both the uniformly excited and thinned ECAA. The results of this study clearly revealed that the SLL is highly reduced at a very directive beamwidth. The proposed antenna array offers high system availability over wide range of radar applications. [ABSTRACT FROM PUBLISHER]

The dynamic economic dispatch problem is a high-dimensional complex constrained optimization problem that determines the optimal generation from a number of generating units by minimizing the fuel cost. Over the last few decades, a number of solution approaches, including evolutionary algorithms, have been developed to solve this problem. However, the performance of evolutionary algorithms is highly dependent on a number of factors, such as the control parameters, diversity of the population, and constraint-handling procedure used. In this paper, a self-adaptive differential evolution and a real-coded genetic algorithm are proposed to solve the dynamic dispatch problem. In the algorithm design, a new heuristic technique is introduced to guide infeasible solutions towards the feasible space. Moreover, a constraint-handling mechanism, a dynamic relaxation for equality constraints, and a diversity mechanism are applied to improve the performance of the algorithms. The effectiveness of the proposed approaches is demonstrated on a number of dynamic economic dispatch problems for a cycle of 24 h. Their simulation results are compared with each other and state-of-the-art algorithms, which reveals that the proposed method has merit in terms of solution quality and reliability. [ABSTRACT FROM AUTHOR]

In this paper, a novel hybrid multi-objective optimization algorithm based on the nondominated sorting genetic algorithm II for large array thinning is presented. The iterative fast Fourier transform (IFFT) technique with a judge factor is introduced into the optimizer to accelerate the convergence. The global characteristics of a genetic algorithm show its optimization capability in the early phase of the optimization process and the powerful local search ability of IFFT works in the late phase. Thus, this proposed algorithm can not only effectively avoid being trapped into the local optimum but also possess a fast convergence for large array thinning. Several representative examples of large planar thinned arrays validate the good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach is proposed to improve the classification performance of a polynomial neural network (PNN). In this approach, the partial descriptions (PDs) are generated at the first layer based on all possible combinations of two features of the training input patterns of a dataset. The set of PDs from the first layer, the set of all input features, and a bias constitute the chromosome of the real-coded genetic algorithm (RCGA). A system of equations is solved to determine the values of the real coefficients of each chromosome of the RCGA for the training dataset with the mean classification accuracy (CA) as the fitness value of each chromosome. To adjust these values for unknown testing patterns, the RCGA is iterated in the usual manner using simple selection, crossover, mutation, and elitist selection. The method is tested extensively with the University of California, Irvine benchmark datasets by utilizing tenfold cross validation of each dataset, and the performance is compared with various well-known state-of-the-art techniques. The results obtained from the proposed method in terms of CA are superior and outperform other known methods on various datasets. [ABSTRACT FROM PUBLISHER]

A novel optimization-enabled electromagnetic transient simulation approach is proposed for calculating the resistor setting of a multistage impulse generator for insulation impulse testing of high-voltage transformers. This approach enables test engineers to overcome most of the major challenges attributed to the use of conventional trial-and-error methods for determining resistor settings, including the excessive time consumption and the potential damage to the test transformer due to a greater number of trial tests. The proposed method uses the frequency response of the test transformer to synthesize a circuit model for it. The test setup, including the test transformer and the impulse generator, is simulated using an electromagnetic-transients-type simulator. A genetic-algorithm-based approach is used to optimize the setting of the impulse generator. Optimized resistor values are then used in impulse testing of a three-phase power transformer. Different cases of impulse generator resistor arrangements are studied in this paper and simulated waveforms are compared with those obtained from measurements. [ABSTRACT FROM AUTHOR]

In this paper, a fast power/ground (P/G) pin assignment method for large-scale high-pin-count ball grid array (BGA) packages using static template is proposed. The static template is generated using genetic algorithm. An algorithm is developed to remove the boundary problem of the static template so that its copies can be directly merged into a large-scale BGA package. The selection of the size of static template is demonstrated. A visual demonstration method is presented to better evaluate the quality of the assignment of signal and P/G pins by human eyes. Using static template method, the pin assignment of a $50\times 50$ BGA package with blocks of core, I/Os and differential pairs can be generated in a few tens of seconds automatically. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to address the maintenance optimization problem when the maintenance models encode stochastic processes, which rely on parameters that are imprecisely known, and when these parameters are only determined through information elicited from experts. A genetic algorithms (GA)-based technique is proposed to deal with such uncertainty setting; this approach requires addressing three main issues: i) the representation of the uncertainty in the parameters and its propagation onto the fitness values; ii) the development of a ranking method to sort the obtained uncertain fitness values, in case of single-objective optimization; and iii) the definition of Pareto dominance, for multi-objective optimization problems. A known hybrid Monte Carlo-Dempster-Shafer Theory of Evidence method is used to address the first issue, whereas two novel approaches are developed for the second and third issues. For verification, a practical case study is considered concerning the optimization of maintenance for the nozzle system of a turbine in the Oil & Gas industry. [ABSTRACT FROM AUTHOR]

An improved nondominated sorting genetic algorithm–II (INSGA-II) has been proposed for optimal planning of multiple distributed generation (DG) units in this paper. First, multiobjective functions that take minimum line loss, minimum voltage deviation, and maximal voltage stability margin into consideration have been formed. Then, using the proposed INSGA-II algorithm to solve the multiobjective planning problem has been described in detail. The improved sorting strategy and the novel truncation strategy based on hierarchical agglomerative clustering are utilized to keep the diversity of population. In order to strengthen the global optimal searching capability, the mutation and recombination strategies in differential evolution are introduced to replace the original one. In addition, a tradeoff method based on fuzzy set theory is used to obtain the best compromise solution from the Pareto-optimal set. Finally, several experiments have been made on the IEEE 33-bus test case and multiple actual test cases with the consideration of multiple DG units. The feasibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been proved. [ABSTRACT FROM AUTHOR]

Lightning is considered one of the main causes of faults in overhead distribution networks. Direct strokes usually lead to flashovers due to the insulation levels that are used. Induced overvoltages caused by indirect lightning are usually lower and can be efficiently reduced by metal–oxide surge arresters. Hence, its associated flashover rate can be reduced. In this paper, a heuristic method is proposed to optimize the number of surge arresters as well as their locations. The method presented is based on genetic algorithms and an economic approach is taken into account by means of evaluating the cost of insulation flashover. [ABSTRACT FROM AUTHOR]

The next-generation wireless communication networks are envisioned to offer many multimedia services such as audio/video clips, mobile TV, web browsing, video conference, etc., with diverse quality of service (QoS) requirements. Multicasting/broadcasting has been recognized as an emerging and enabling technique for such multimedia transmissions over wireless networks. By employing multicast transmission, a base station can transmit the same data content to several groups of users requiring flexible QoS. In this paper, we investigate subchannel and power allocation problems in an OFDM-based wireless multicast system. With the goal of maximizing the total system capacity, a low complexity, novel genetic algorithm aided efficient subchannel allocation scheme taking into account the constraints of total available bandwidth, proportional data rate fairness and total transmit power budget at the base station is proposed. The subchannel allocation is then followed by optimal power allocation. Unlike previous work where either no fairness or fairness based on number of subchannels allocated to the different groups was considered, we incorporate a set of system parameters in the problem formulation such that the ratio of the group data rates strictly follow the set of system parameters after resource allocation. Simulation results show that the proposed method obtains higher sum capacities while maintaining proportional data rate fairness among different multicast groups, without being awfully complex. [ABSTRACT FROM PUBLISHER]

This paper deals with design optimization of air-cored permanent magnet linear synchronous motors with overlapping windings to achieve high thrust per volume, high thrust per coils quantity, high motor constant, and low thrust ripple simultaneously for ultra-precise positioning stage with air-bearing. Based on accurate magnetic field model, motor parameters such as flux density, inductive electromotive force (EMF), thrust per volume, thrust per coils quantity, and thrust ripple are analyzed. A multiobjective optimization method with weight coefficients is proposed by applying the multiple population genetic algorithm. The design optimization is verified by 3-D finite element analysis and experimental results. [ABSTRACT FROM AUTHOR]

The discovery of evolving communities in dynamic networks is an important research topic that poses challenging tasks. Evolutionary clustering is a recent framework for clustering dynamic networks that introduces the concept of temporal smoothness inside the community structure detection method. Evolutionary-based clustering approaches try to maximize cluster accuracy with respect to incoming data of the current time step, and minimize clustering drift from one time step to the successive one. In order to optimize both these two competing objectives, an input parameter that controls the preference degree of a user towards either the snapshot quality or the temporal quality is needed. In this paper the detection of communities with temporal smoothness is formulated as a multiobjective problem and a method based on genetic algorithms is proposed. The main advantage of the algorithm is that it automatically provides a solution representing the best trade-off between the accuracy of the clustering obtained, and the deviation from one time step to the successive. Experiments on synthetic data sets show the very good performance of the method when compared with state-of-the-art approaches. [ABSTRACT FROM PUBLISHER]

Concurrent minimization of project time and project cost is an important issue in construction planning and management. Tradeoff optimization between these two variables is necessary to maximize overall construction project benefit. This paper presents a two-phase differential evolution (DE) model to resolve these problems. This model is able to effectively consider both time–cost effects and resource constraints. First, we introduce a novel multiple-objective algorithm, the chaotic initialized multiple objective differential evolution with adaptive-mutation strategy-based time–cost tradeoff, to determine the execution mode that best optimizes the time–cost balance. Subsequently, we introduce a DE-based resource-constrained method to generate a feasible schedule. A real construction case study is then used to illustrate the application of the proposed algorithm. Performance comparisons done with the nondominated sorting genetic algorithm, multiple objective particle swarm optimization, and multiple objective differential evolution further verify the efficiency and effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In this paper we describe a method for optimizing frequency reconfigurable pixel antennas. The method utilizes a multi-objective function that is efficiently computed by using only one full electromagnetic simulation in the entire genetic algorithm optimization process. Minimization of the number of switches in the design is also attempted. The method is demonstrated using an antenna structure consisting of a rectangular grid of pixels adjacent to a ground plane and using RF MEMS switches for achieving the reconfigurability. The effects of the RF MEMS switches on the antenna performance as well as the control feed lines for them are also addressed. We provide both simulation and experimental results for a reconfigurable dual-band antenna that reconfigures the bands 820–1140 and 1720–1900 MHz to the bands 860–1160 and 1890–2300 MHz with dimensions of 39 mm \times 24 mm on a ground plane of 40 mm \times 65 mm with one switch only. The results demonstrate that reconfigurable antennas can be designed effectively with a minimum number of switches using an efficient optimization method. [ABSTRACT FROM PUBLISHER]

Recommending TV content to groups of viewers is best carried out when relevant information such as the demographics of the group is available. However, it can be difficult and time consuming to extract information for every user in the group. This paper shows how an audio analysis of the age and gender of a group of users watching the TV can be used for recommending a sequence of N short TV content items for the group. First, a state of the art audio-based classifier determines the age and gender of each user in an M-user group and creates a group profile. A genetic recommender algorithm then selects for each user in the profile, a single personalized multimedia item for viewing. When the number of items to be presented is different to the number of viewers in the group, i.e. M = N, a novel adaptation algorithm is proposed that first converts the M-user group profile to an N-slot content profile, thus ensuring that items are proportionally allocated to users with respect to their demographic categorization. The proposed system is compared to an ideal system where the group demographics are provided explicitly. Results using real speaker utterances show that, in spite of the inaccuracies of state-of-the-art age-and-gender detection systems, the proposed system has a significant ability to predict an item with a matching age and gender category. User studies were conducted where subjects were asked to rate a sequence of advertisements, where half of the advertisements were randomly selected, and the other half were selected using the audio-derived demographics. The recommended advertisements received a significant higher median rating of 7.75, as opposed to 4.25 for the randomly selected advertisements. [ABSTRACT FROM PUBLISHER]

The intent of this paper is to propose new methods for the reconstruction of areas obscured by clouds. They are based on compressive sensing (CS) theory, which allows finding sparse signal representations in underdetermined linear equation systems. In particular, two common CS solutions are adopted for our reconstruction problem: the basis pursuit and the orthogonal matching pursuit methods. A novel alternative CS solution is also proposed through a formulation within a multiobjective genetic optimization scheme. To illustrate the performances of the proposed methods, a thorough experimental analysis on FORMOsa SATellite-2 and Satellite Pour l'Observation de la Terre-5 multispectral images is reported and discussed. It includes a detailed simulation study that aims at assessing the accuracy of the methods in different qualitative and quantitative cloud-contamination conditions. Compared with state-of-the-art techniques for cloud removal, the proposed methods show a clear superiority, which makes them a promising tool in cleaning images in the presence of clouds. [ABSTRACT FROM AUTHOR]

This paper considers minimizing makespan for a blocking flowshop scheduling problem, which has important application in a variety of modern industries. A constructive heuristic is first presented to generate a good initial solution by combining the existing profile fitting (PF) approach and Nawaz–Enscore–Ham (NEH) heuristic in an effective way. Then, a memetic algorithm (MA) is proposed including effective techniques like a heuristic-based initialization, a path-relinking-based crossover operator, a referenced local search, and a procedure to control the diversity of the population. Afterwards, the parameters and operators of the proposed MA are calibrated by means of a design of experiments approach. Finally, a comparative evaluation is carried out with the best performing algorithms presented for the blocking flowshop with makespan criterion, and with the adaptations of other state-of-the-art MAs originally designed for the regular flowshop problem. The results show that the proposed MA performs much better than the other algorithms. Ultimately, 75 out of 120 upper bounds provided by Ribas [“An iterated greedy algorithm for the flowshop scheduling with blocking”, OMEGA, vol. 39, pp. 293–301, 2011.] for Taillard flowshop benchmarks that are considered as blocking flowshop instances are further improved by the presented MA. [ABSTRACT FROM AUTHOR]

Morphological and attribute profiles have been proven to be effective tools to fuse spectral and spatial information for classification of remote sensing data. A wide range of filters (i.e., number of levels in the profiles) is usually necessary in order to properly model the spatial information in a remote sensing scene. A dense sampling of the values of the parameters of the filters generates profiles that have both a very large dimensionality (leading to the Hughes phenomenon in classification) and a high redundancy. In this paper, a novel iterative technique based on genetic algorithms (GAs) is proposed to automatically optimize the selection of the optimal features from the profiles. The selection of the filtered images that compose the profile is performed by dividing them into three classes corresponding to high, medium, and low importance. We propose to measure the importance (modeled in terms of discriminative power in the classification task) using a random forest classifier, which provides a rank for each feature with its model. Only the set of images associated with the highest importance is selected, i.e., preserved for classification. The proposed technique is applied to the features labeled with medium importance, whereas the images with the lowest importance are removed from the profile. This method is employed to classify three hyperspectral data sets achieving significantly high classification accuracy values. A parallel computing implementation has been developed in order to significantly reduce the time required for the run of the GAs. [ABSTRACT FROM AUTHOR]

Utilizing battery storage systems (BSSs) can reduce the intermittent output of PV generation systems (PVGSs) and make them dispatchable. The aim of this paper is to design an optimal charging/discharging scheduling for BSSs such that the line loss of distribution systems interconnected with sizeable PVGSs can be minimized. A mathematical model for BSSs which can be used to simulate the charging procedures such as the commonly-used constant current to constant voltage (CC-CV) charging method, the discharging procedures and the state of charge (SOC) is proposed first. The minimum line loss problem considering the intermittent output of PVGSs and the scheduling of BSSs is then formulated based on the BSS mathematical model. The optimal charging/discharging scheduling of BSSs can then be obtained by a genetic algorithm (GA) based method. Test results demonstrate the validity of the proposed mathematical model and optimal charging/discharging scheduling for BSSs. [ABSTRACT FROM PUBLISHER]

In this paper, impedance spectroscopy, gene expression programming (GEP), and genetic algorithms are combined to perform sensor characterization. The process presented is useful when there is no knowledge of the sensor equivalent circuit, and a set of impedance responses can be obtained for different measurand values. These responses are used by the algorithm to determine a suitable equivalent circuit and choose a circuit component that describes the measurand values. From this component, interpolation is used to infer the measurand value from the measured frequency responses. Improvements on the application of GEP to impedance characterization are presented. The method is validated through its application to numerical results of a humidity sensor and measurement results of a viscosity sensor. [ABSTRACT FROM AUTHOR]

This paper considers a system consisting of n linearly ordered multistate components. Each component can have different states: from complete failure, up to perfect functioning. A performance rate is associated with each state. The system fails if in each of at least m consecutive overlapping groups of r consecutive components (windows) the sum of the performance rates of components belonging to the group is lower than a minimum allowable level. It is shown that, in the case of different components, the system reliability depends on their arrangement. The optimal arrangement problem is formulated, and a numerical tool for solving this problem is suggested. The tool uses an extended universal moment generating function technique for system reliability evaluation, and a genetic algorithm for optimization. Examples of system reliability optimization are presented. [ABSTRACT FROM PUBLISHER]

Some recent studies have posed the problem of data clustering as a multiobjective optimization problem, where several cluster validity indices are simultaneously optimized to obtain tradeoff clustering solutions. A number of cluster validity index measures are available in the literature. However, none of the measures can perform equally well in all kinds of datasets. Depending on the dataset properties and its inherent clustering structure, different cluster validity measures perform differently. Therefore, it is important to find the best set of validity indices that should be optimized simultaneously to obtain good clustering results. In this paper, a novel interactive genetic algorithm-based multiobjective approach is proposed that simultaneously finds the clustering solution as well as evolves the set of validity measures that are to be optimized simultaneously. The proposed method interactively takes the input from the human decision maker (DM) during execution and adaptively learns from that input to obtain the final set of validity measures along with the final clustering result. The algorithm is applied for clustering real-life benchmark gene expression datasets and its performance is compared with that of several other existing clustering algorithms to demonstrate its effectiveness. The results indicate that the proposed method outperforms the other existing algorithms for all the datasets considered here. [ABSTRACT FROM PUBLISHER]