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Compared with the conventional two-level dual-active-bridge (DAB) dc–dc converter, the three-level DAB converter could operate with lower voltage rated switching devices and provide more control degrees of freedom, which enhances the control flexibility. This paper proposes a modulation strategy with five control degrees of freedom. In the previous work with multiple control degrees, the optimization strategies for efficiency or inductor current minimization are investigated; however, these strategies all need detailed mode analysis and complex mathematical optimization tools to obtain the optimal duty cycles. In this paper, an optimization strategy for the root-mean-square value of the inductor current is proposed. This method extracts the fundamental and third-harmonic components from the high-frequency voltages on both sides and adjusts the amplitude of the bridge voltages by the vector diagram to obtain the optimal duty cycles. The inductor current can be reduced significantly at light loads with the proposed strategy, so the efficiency of the converter can be improved. Moreover, the optimization process does not need the detailed operation analysis of the converter, nor does it require the complex mathematical optimization tools. Finally, the performance of the three-level DAB and the optimization strategy are verified by the experimental results. [ABSTRACT FROM AUTHOR]

In this paper, a new group of optimization algorithms named Human Strategy Algorithm (HS) is proposed which is inspired by human strategies to problem solving. The main idea of HS is based on human actions to find the problem’s optima by means of accessible instruments. As the environment of an unknown problem assumed to be a black box, it is supposed that the environment of our problem is a dark room occupied by several men named blind men. The main mission of these men is to look for the optimum solution. Each man has at least one instrument as his assistance. Like real life, the instrument might be any tool such as stick, billy, rope, stone, yoyo, sweep. Any instrument by its unique features is suitable in some situations. In fact, this algorithm maps problem space and searches agents to dark room and people, respectively. In this paper, one sample algorithm of the group of human strategy, YOYO Blind Man Algorithm (YOYO-BMA), is introduced which uses yoyos as men’s accessible instruments. The performance of the YOYO-BMA is evaluated on a set of benchmark problems provided for CEC’2010 Special Session and Competition on Large-Scale Global Optimization (Tang et al. 2010). The results show superior performance of proposed algorithm in comparison with others. Moreover, the problem of designing urban traffic network is solve to evaluate the algorithm using a real complex problem. [ABSTRACT FROM AUTHOR]

Medical staff configuration is a critical problem in the management of an emergency department (ED) in Hong Kong (HK). Given the service requirements by HK government, it is imperative for the hospital managers to develop medical staff configuration in a cost-and-time-effective way. In this paper, the medical staff configuration problem in ED is modeled as minimizing the total labor cost while satisfying the service quality requirements. To solve this issue, we propose a highly efficient search method, called random boundary generation with feasibility detection (RBG-FD). The random boundary generation (RBG) is applied to efficiently identify good-quality solutions based on the objective value. The feasibility detection (FD) procedure is used to retain the probability of correct feasibility detection of each sampled solution at the desired level, which intrinsically allocates a reasonable number of simulation replications. To estimate the performance measures of the ED, a discrete-event simulation model is developed to reflect the patient flow. Using these techniques, the efficiency of identifying the optimal staff configuration can be significantly improved. A case study is performed in a public hospital in HK. The numerical results indicate significantly higher practicability and efficiency of the proposed method with different patient arrival rates and service constraints. Note to Practitioners This paper seeks to solve the problem of minimizing the medical staff cost constrained by certain service requirements [i.e., patients’ waiting times for treatment] at an emergency department in Hong Kong. In our formulation, these service requirements are characterized by some stochastic constraints. Most of the existing random search methods concentrate on the computing efforts in the neighborhood of the best-so-far solutions in order to obtain good-quality solutions. Due to the special structure of this problem and ease of computing the objective values, we proposed an efficient random search approach that iteratively identifies a solution with a better objective value than that of the current best solution. Experimental studies demonstrate the significantly higher efficiency of this method. In order to obtain the same solution quality, it is able to reduce the computational time by 90% compared with some existing approaches in the literature. [ABSTRACT FROM PUBLISHER]

The inverse radiative transfer problem finds broad applications in medical imaging, atmospheric science, astronomy, and many other areas. This problem intends to recover optical properties, denoted as absorption and the scattering coefficient of the media, through source-measurement pairs. A typical computational approach is to form the inverse problem as a PDE-constraint optimization, with the minimizer being the to-be-recovered coefficients. The method is tested to be efficient in practice, but it lacks analytical justification: there is no guarantee of the existence or uniqueness of the minimizer, and the error is hard to quantify. In this paper, we provide a different algorithm by levering the ideas from singular decomposition analysis. Our approach is to decompose the measurements into three components, two of which encode the information of the two coefficients, respectively. We then split the optimization problem into two subproblems and use those two components to recover the absorption and scattering coefficients separately. In this regard, we prove the well-posedness of the new optimization, and the error could be quantified with better precision. In the end, we incorporate the diffusive scaling and show that the error is harder to control in the diffusive limit. [ABSTRACT FROM AUTHOR]

Maintenance research catches growing attention for its increasing importance in reality. Spare parts and intermediate buffer are two important factors related to maintenance activity, and both of them may greatly influence the system cost. Although some researchers have considered maintenance and buffer inventory, or maintenance and spare parts inventory together, the joint optimization of the three has not been considered before. This paper is focused on the interaction between maintenance, buffer inventory, and spare parts inventory, to achieve the minimization of the long-term expected cost rate for a production system. The investigated production system of the paper consists of two serial machines, an intermediate buffer, and a spare part inventory. The practical example for this type of system can be an automated work center or an automobile general assembly. The cost model that represents the long-term expected cost rate is developed by mathematical analysis. Four control variables corresponding to decisions made on maintenance, buffer, and spare parts are included. Then by using the genetic algorithm method, the cost model is optimized. The proposed method is applied to different simulation cases to show its efficiency and necessity. Additionally, the related optimization results indicate that the joint optimization can effectively alleviate the influence caused by the change of buffer accumulation speed. [ABSTRACT FROM AUTHOR]

In this paper, an integrated coal gasification combined cycle system for the production of hydrogen and electricity is optimized in terms of energy and exergy efficiencies, and the amount and cost of the produced hydrogen and electricity. The integrated system is optimized by focusing on the conversion process of coal to syngas. A novel optimization process is developed which integrates an artificial neural network with a genetic algorithm. The gasification system is modeled and simulated with Aspen Plus for large ranges of operating conditions, where the artificial neural network method is used to represent the simulation results mathematically. The mathematical model is then optimized using a genetic algorithm method. The optimization demonstrates that the lower is the grade of coal of the three considered coals, the less expensive is the hydrogen and electricity that can be produced by the considered integrated gasification combined cycle (IGCC) system. [ABSTRACT FROM AUTHOR]

Bat algorithm (BA) is a new approach designed by imitating bat's behavior of searching and capturing preys. The existing results have demonstrated the effectiveness and efficiency in comparison with other heuristic algorithms such as genetic algorithms and particle swarm optimization. In this paper, we design a novel framework for bat algorithm named two-stage bat algorithm (TSBA) using a trade-off strategy which balances the relationship between exploration and exploitation at the most extent. Inspired by the multi-population methods (e.g., artificial bee colony), we not only concern some technologies to avoid premature inevitably encountered when using BA, but also use a trade-off strategy to improve the comprehensive search performance for optimization. Some typical test sets which consist of 27 benchmark functions are utilized in comparative experiment, and the simulation results in terms of convergence rate and accuracy illustrate that the TSBA has a competitive performance than other swarm intelligent optimization algorithms. In addition, the proposed algorithm will not lend to the tremendous increase in computing time and thus will be a powerful tool in practical applications. [ABSTRACT FROM AUTHOR]

Component-based software engineering uses components to construct systems, being a means to increase productivity by promoting software reuse. This work deals with the component selection problem. A multiobjective optimization approach is used to formulate the problem. The contribution of the current paper is threefold: the multilevel decomposition approach, the use of both functional and non-functional requirements (cost of a component, thus cost of the solution as sum of the costs of the constituent components) as objectives, and the use of metrics values to evaluate the architecture of the obtained solutions. As advantages over the existing literature we mention: the automatic computation of component interactions and of the constituent components for each module, multiple solutions obtained in a single run, and the new architecture evaluation step based on metrics values that is not present in other approaches. The decomposition offers valuable insights about internal structure of the system which led us to identify metrics to assess coupling and cohesion of the architecture design. The internal structure influences the external quality; thus, a highly cohesive module exhibits high reusability and loosely coupled systems enable easy maintenance. In this context, when selecting the best solution out of a set of available ones, we aim to maximize the cohesion of modules and to minimize the coupling between them, obtaining thus the 'best' reusable and maintenable solution. To evaluate our approach, we discuss a case study for building a reservation system. In order to obtain the best parameters to run the evolutionary approach, three different studies were applied. The tests performed show the potential of evolutionary algorithms for this particular problem and for other similar ones. [ABSTRACT FROM AUTHOR]

Reconstructing emergency communication networks (ECNs) quickly after a disaster occurs is critical so that people can share information and confirm their safety. In recent studies, deployable base stations (DBSs) have demonstrated their ability to reconstruct an ECN. However, considering limited resources, it is impossible to deploy DBSs in the whole disaster area. The above shortage can be covered by deploying small-cell networks (i.e., low-power transmission base stations) in areas with high communication demand, e.g., in refuges. Considering the above two-tier ECN, in this paper, we study its performance and optimization issue with the objective of minimizing the number/density of DBSs while guaranteeing acceptable coverage probabilities for both communication tiers. The majority of current research focuses on scenarios where the base stations follow a homogeneous Poisson point process of coverage probability. It is difficult to transfer the results to other applications, e.g., when communication resources are shared, such as by refugees following a disaster. In such cases, the distribution of users is closer to that of a Poisson cluster process. We then investigate the optimization method to minimize the number/density of DBSs. We used Monte Carlo methods with various parameter choices to evaluate the results and to determine the accuracy of our evaluation. [ABSTRACT FROM PUBLISHER]

In this paper, we present an optimization technique to find the optimal parameters of the AOR iteration, which just needs to minimize the 2-norm of the residual vector and avoids solving the spectral radius of the iteration matrix of the SOR method. Meanwhile, numerical results are provided to indicate that the new method is more robust than the AOR method for larger intervals of the parameters ω and γ. [ABSTRACT FROM AUTHOR]

In this paper are presented the analysis steps to be taken in Solid Works for conducting a preliminary study of parts designed, using Simulation Express module. There are presented the module features and the settings that have to be done for such analysis and the results shown after analysis. [ABSTRACT FROM AUTHOR]

The paper deals with the situation when a low populated area is in need of public transport service. It is necessary to design a bus route, passing through the area and meeting the accessibility and efficiency requirements. The article presents a mathematical formulation of the problem in terms of the network theory together with two exact and several heuristic methods for finding a solution. The paper describes that the problem is NP-hard, and therefore computing experience is outlined. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the multiobjective definition of video compression and its optimization. The optimization will be done using NSGA-II, a well-tested and highly accurate algorithm with a high convergence speed developed for solving multiobjective problems. Video compression is defined as a problem including two competing objectives. We try to find a set of optimal, so-called Pareto-optimal solutions, instead of a single optimal solution. The two competing objectives are quality and compression ratio maximization. The optimization will be achieved using a new patent pending codec, called MIJ2K, also outlined in this paper. Video will be compressed with the MIJ2K codec applied to some classical videos used for performance measurement, selected from the Xiph.org Foundation repository. The result of the optimization will be a set of near-optimal encoder parameters. We also present the convergence of NSGA-II with different encoder parameters and discuss the suitability of MOEAs as opposed to classical search-based techniques in this field. [Copyright &y& Elsevier]

In this paper we have proposed a nonlinear mathematical model for a wind turbine. The objective function maximizes the power of the wind turbine and the constraints are related to the rotor and tower costs. Rotor diameter and hub height are the variables which affect on power of the wind turbine, so we have considered them as decision variable in our mathematical model. By increasing rotor diameter and hub height the power of the turbine will increase but the costs don't let the infinitive increase in rotor diameter and height. The model applied for a typical case study and the results of solving the model for it have shown in the paper. [ABSTRACT FROM AUTHOR]

This paper tackles a power minimization problem of interleave-division multiple-access (IDMA) systems over a fading multiple-access channel. The problem is minimizing the total power received by the receiver while keeping the bit error rates (BERs) of all users below a predefined value. The original formulation of the problem has highly nonlinear and implicitly defined functions, which render most existing optimization methods incapable. A new formulation is proposed in this paper, whose solution can effectively be obtained by a branch-and-bound (B&B) technique. An algorithm is devised based on B&B, and its effectiveness is also demonstrated by numerical experiments of systems with a moderate numbers of users. [ABSTRACT FROM AUTHOR]

This paper considers localization of a source or a sensor from distance measurements. We argue that linear algorithms proposed for this purpose are susceptible to poor noise performance. Instead given a set of sensors/anchors of known positions and measured distances of the source/sensor to be localized from them we propose a potentially nonconvex weighted cost function whose global minimum estimates the location of the source/sensor one seeks. The contribution of this paper is to provide nontrivial ellipsoidal and polytopic regions surrounding these sensors/anchors of known positions, such that if the object to be localized is in this region, localization occurs by globally exponentially convergent gradient descent in the noise free case. Exponential convergence in the noise free case represents practical convergence as it ensures graceful performance degradation in the presence of noise. These results guide the deployment of sensors/anchors so that small subsets can be made responsible for practical localization in geographical areas determined by our approach. [ABSTRACT FROM AUTHOR]

This paper introduces a particle swarm optimization (PSO)-based methodology to implement preemptive scheduling under break and resource-constraints (PSBRC) for construction projects. The PSBRC under study allows the preemptive activities to be interrupted in off-working time and not to resume immediately in the next working period because all the limited resources are to be reallocated during a break. The potential solution to the PSBRC, i.e., a set of priorities deciding the order to start the activities or restart the interrupted activities, is represented by the multidimensional particle position. Hence PSO is applied to search for the optimal schedule for the PSBRC, in which a parallel scheme is adopted to transform the particle-represented priorities to a schedule. Computational analyses are presented to verify the effectiveness of the proposed methodology. This paper provides an attempt to make use of preemption and break for the resource-constrained construction project with the objective of minimizing project duration. [ABSTRACT FROM AUTHOR]

Inverse problems that are constrained by large-scale partial differential equation (PDE) systems demand very large computational resources. Solutions to these problems generally require the solution of a large number of complex PDE systems. Three-dimensional groundwater inverse problems fall under this category. In this paper, we describe the implementation of a parallel simulation-optimization framework for solving PDE-based inverse problems and demonstrate it for the solution of groundwater contaminant source release history reconstruction problems that are of practical importance. The optimization component employs several optimization algorithms, including genetic algorithms (GAs) and several local search (LS) approaches that can be used in a hybrid mode. This hybrid GA-LS optimizer is used to drive a parallel finite-element (FEM) groundwater forward transport simulator. Parallelism is exploited within the transport simulator (fine grained parallelism) as well as the optimizer (coarse grained parallelism) through the exclusive use of the Message Passing Interface (MPI) communication library. Algorithmic and parallel performance results are presented for an IBM SP3 supercomputer. Simulation and performance results presented in this paper illustrate that an effective combination of efficient optimization algorithms and parallel computing can enable solution to three-dimensional groundwater inverse problems of a size and complexity not attempted before. [ABSTRACT FROM AUTHOR]

Power and performance tradeoff optimization is one of the most significant issues on heterogeneous multiprocessor or multicomputer systems (HMCSs) with dynamically variable voltage. In this paper, the problem is defined as energy-constrained performance optimization and performance-constrained energy optimization. Task scheduling for precedence-constrained parallel applications represented by a directed acyclic graph (DAG) in HMCSs is an NP-HARD problem. Over the last three decades, several task scheduling techniques have been developed for energy-aware scheduling. However, it is impossible for a single task scheduling technique to outperform all other techniques for all types of applications and situations. Motivated by these observations, hyper-heuristic framework is introduced. Moreover, a quantum-inspired high-level learning strategy is proposed to improve the performance of this framework. Meanwhile, a fast solution evaluation technique is designed to reduce the computational burden for each iteration step. Experimental results show that the fast solution evaluation technique can improve average algorithm search speed by 38 percent and that the proposed algorithm generally exhibits outstanding convergence performance. [ABSTRACT FROM PUBLISHER]

Waveform design is a pivotal component of the fully adaptive radar construct. In this paper, we consider waveform design for radar space time adaptive processing (STAP), accounting for the waveform dependence of the clutter correlation matrix. Due to this dependence, in general, the joint problem of receiver filter optimization and radar waveform design becomes an intractable, nonconvex optimization problem, Nevertheless, it is, however, shown to be individually convex either in the filter or in the waveform variables. We derive constrained versions of a) the alternating minimization algorithm, b) proximal alternating minimization, and c) the constant modulus alternating minimization, which, at each step, iteratively optimizes either the STAP filter or the waveform independently. A fast and slow time model permits waveform design in radar STAP, but the primary bottleneck is the computational complexity of the algorithms. [ABSTRACT FROM PUBLISHER]

In this paper we study a class of optimization problems under uncertainty, with parameters modeled by stochastic random variables. Interval analysis and multiobjective stochastic programming concepts are introduced. Then these two concepts are combined to build a stochastic programming model, with the coefficients of the constraints and the coefficients of the objective function modeled by interval numbers and discrete interval random variables. This model can be used to solve a portfolio optimization problem. [ABSTRACT FROM AUTHOR]

This paper proposes a new transmission-constrained unit commitment method that combines the cost-efficient but computationally demanding stochastic optimization and the expensive but tractable interval optimization techniques to manage uncertainty on the expected net load. The proposed hybrid unit commitment approach applies the stochastic formulation to the initial operating hours of the optimization horizon, during which the wind forecasts are more accurate, and then switches to the interval formulation for the remaining hours. The switching time is optimized to balance the cost of unhedged uncertainty from the stochastic unit commitment against the cost of the security premium of the interval unit commitment formulation. These hybrid, stochastic, and interval formulations are compared using Monte Carlo simulations on a modified 24-bus IEEE Reliability Test System. The results demonstrate that the proposed unit commitment formulation results in the least expensive day-ahead schedule among all formulations and can be solved in the same amount of time as a full stochastic unit commitment. However, if the range of the switching time is reduced, the hybrid formulation in the parallel computing implementation outperforms the stochastic formulation in terms of computing time. [ABSTRACT FROM PUBLISHER]

An approach helpful when developing an optimized construction of a 6/4 type switched reluctance motor (SRM) is described in the paper. The analytical modeling procedure, based on the reluctance network method and analytical solution of an ordinary differential equation, enables applying a gradient optimization routine and better control of optimization process. The model allows for estimation of the efficiency, torque, and acoustic noise of the motor taking into account the magnetic non-linearity and the control algorithm to keep a constant input power. A bicriterial optimization routine has been applied to find optimal constructions. Eleven geometric and winding parameters are supposed to be the optimization quantities. Analyzed constructions - the initial one and the optimized ones, were validated by means of FEM calculations. The proposed approach can be employed in designing the SRM to be a drive motor in an electrical vehicle, at least as a first attempt. [ABSTRACT FROM AUTHOR]

Abstract: A recursive search algorithm along with mathematical analysis was used in a recent paper (Chiu, Y.-S.P., Chen, K.-K., Ting, C.-K., 2012. Replenishment run time problem with machine breakdown and failure in rework. Expert Systems with Applications, 39, 1291–1297) to determine the optimal operating policy for a specific replenishment run time problem. In its optimization procedure a conditional convexity theorem on the cost function was adopted. This paper presents a direct proof of convexity of the function to enhance quality of their solution process and eliminate unnecessary computational efforts in verifying the conditional convexity. [Copyright &y& Elsevier]

This paper presents an optimization technique to dynamically balance the planar mechanisms in which the shaking forces and shaking moments are minimized using the genetic algorithm (GA). A dynamically equivalent system of point-masses that represents each rigid link of a mechanism is developed to represent link's inertial properties. The shaking force and shaking moment are then expressed in terms of the point-mass parameters which are taken as the design variables. These design variables are brought into the optimization scheme to reduce the shaking force and shaking moment. This formulates the objective function which optimizes the mass distribution of each link. First, the problem is formulated as a single objective optimization problem for which the genetic algorithm produces better results as compared to the conventional optimization algorithm. The same problem is then formulated as a multi-objective optimization problem and multiple optimal solutions are created as a Pareto front by using the genetic algorithm. The masses and inertias of the optimized links are computed from the optimized design variables. The effectiveness of the proposed methodology is shown by applying it to a standard problem of four-bar planar mechanism available in the literature. [ABSTRACT FROM AUTHOR]

We propose a power-efficient Tomlinson-Harashima precoder (THP) in the downlink of multi-user multiple-input single-output (MU-MISO) systems, where a transmit power reduction is achieved by means of interference optimization. The adopted approach is based on adaptively scaling the symbols of a number of users whose received signal-to-noise ratio (SNR) thresholds are known to the transmitter. By doing this, the interference can be better aligned to the symbols of interest, thus reducing the power required to cancel it. The scaling is performed by forming a constrained optimization problem, solved with existing well-known techniques, which entails an increase in the computational complexity at the base station. To quantify this trade-off in performance and complexity, a study of the impact in the signal processing load is carried out by means of a power efficiency analysis. The presented analytical and simulation results in this paper confirm that the proposed technique increases the power efficiency up to 100% with respect to previous THP-based approaches while, at the same time, maintaining the same average performance. [ABSTRACT FROM AUTHOR]

Abstract: We propose a method for model reduction on a given frequency range, without the use of input and output filter weights. The method uses a nonlinear optimization approach to minimize a frequency limited like cost function. An important contribution of the paper is the derivation of the gradient of the proposed cost function. The fact that we have a closed form expression for the gradient and that considerations have been taken to make the gradient computationally efficient to compute enables us to efficiently use off-the-shelf optimization software to solve the optimization problem. [Copyright &y& Elsevier]

Owing to the lack of feasible fault modeling method, hard (open and short) faults, and discretized parametric faults are still the mostly used fault models. These models cannot characterize all soft (parameter shifting) faults because that the parameter of analog element be of continuity character. To address this concern, a complex field fault modeling method is presented first. If fault happens to passive element xi in linear analog circuit, the real (Ur) and imaginary (U\!j) parts of faulty voltage phasor \mathopU\kern0pt^\!\!\!\circ(\mathopU\kern0pt^\!\!\!\circ=Ur+j U\!j) must satisfy binary quadratic function Fi(Ur,U\!j)=0, which is independent from the value of element xi and uniquely determined by its location and the nominal circuit under test. Hence, the binary quadratic function can model any continuous parameter shifting (soft) or hard fault. Second, to avoid calculating the explicit expression of binary quadratic function, a simulation-based method is given to obtain the locus of the change in the function Fz in the complex plane. Besides, the nominal (fault-free) point, the loci determined by the binary quadratic functions might intersect with each other. It is referred to as aliasing problem in this paper. This problem can be solved by adjusting the frequency of the input signal. Therefore, a constraint optimization method is proposed to select optimal test frequencies. The proposed test generation and compression methods, when combined with the complex field modeling method, can pinpoint much more fault components with less test points and frequencies when compared with the other methods. [ABSTRACT FROM PUBLISHER]

The ability of an organization to perform some critical military tasks in a timely manner may depend on the availability of a sufficient number of appropriate vehicles. Therefore, the decision of which is the best military fleet-mix for a given set of requirements should take into consideration, in addition to cost, the ability of the fleet to perform tasks when some of its vehicles are unavailable. In this paper, a measure of the flexibility of military air mobility fleets is presented that evaluates their ability to perform tasks in a timely manner taking into account the possibility that some aircraft in the fleet may be unavailable at any given time. This measure computes the number of aircraft that must be unavailable in order to render a fleet incapable of performing each task in a timely manner. The utility of the flexibility measure is demonstrated by using it as an objective in a multiobjective optimization framework to compute nondominated fleets with respect to cost and flexibility. An artificial data set that is representative of real military air mobility data is used to illustrate how the new flexibility measure may be used to aid decision makers with their fleet mix problems. [ABSTRACT FROM AUTHOR]

Disjunctive Temporal Problems with Preferences (DTPPs) extend DTPs with piece-wise constant preference functions associated to each constraint of the form l ≤ x - y ≤ u, where x, y are (real or integer) variables, and l, u are numeric constants. The goal is to find an assignment to the variables of the problem that maximizes the sum of the preference values of satisfied DTP constraints, where such values are obtained by aggregating the preference functions of the satisfied constraints in it under a 'max' semantic. The state-of-the-art approach in the field, implemented in the DTPP solver MAXILITIS, extends the approach of the DTP solver EPILITIS. In this paper we present an alternative approach that reduces DTPPs to Maximum Satisfiability of a set of Boolean combination of constraints of the form $l \Join x-y \Join u$, $\Join\in\{\lt,\le\}$, that extends previous work that dealt with constant preference functions only. Results obtained with the Satisfiability Modulo Theories (SMT) solver YICES on randomly generated DTPPs show that our approach is competitive to, and can be faster than, MAXILITIS. [ABSTRACT FROM AUTHOR]

Abstract: The 0-1 knapsack problem is an open issue in discrete optimization problems, which plays an important role in real applications. In this paper, a new bio-inspired model is proposed to solve this problem. The proposed method has three main steps. First, the 0-1 knapsack problem is converted into a directed graph by the network converting algorithm. Then, for the purpose of using the amoeboid organism model, the longest path problem is transformed into the shortest path problem. Finally, the shortest path problem can be well handled by the amoeboid organism algorithm. Numerical examples are given to illustrate the efficiency of the proposed model. [Copyright &y& Elsevier]

This paper describes a forward approach to an inverse problem related to detecting the nature of geological substrata which makes use of optimization techniques in a multivariable calculus setting. The true nature of the related inverse problem is highlighted. [ABSTRACT FROM PUBLISHER]

Designing baseline or vertical alignment in railways greatly depends on the designer's experience. An inaccurate design imposes unreasonable costs during construction and operations. This paper presents a program to find the best vertical alignment for a track with a given horizontal layout. First, the vertical alignment is formulated using mathematical equations, and then an algorithm is developed to produce its geometric configuration. Construction and operating costs, which are sensitive to gradient changes, are expressed as vertical alignment variables and included in the model. A direct search method is employed to solve the objective function, and a genetic algorithm encodes solution algorithms in a program, whereas certain heuristic operators are utilized to cover operational situations. The program also provides railway engineers and planners with practical information on the resulting alignment such as time and speed graph, total traction force, and block capacity. Moreover, the model is evaluated using a real example to verify the algorithms' ability to find the optimal alignment. [ABSTRACT FROM AUTHOR]

This paper presents a condition-based maintenance policy considering aspects of maintenance cost and productivity for a single-unit deteriorating production system whose condition is periodically monitored. Imperfect preventive maintenance actions, which restore the production system to better than old, are considered with both deterministic and random intervention gains. Moreover, the system is assumed to be repaired only a limited number of times. In order to assess the performance of the proposed maintenance policy, different types of imperfect preventive maintenance cost functions are investigated. A numerical example of a multi-state production system is finally introduced to illustrate the use and the advantages of the proposed policy. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The necessary optimality conditions are obtained for initial data. Here initial data implies the collection of initial moment, delay parameter in phase coordinates, initial vector and functions. The discontinuous initial condition means that the values of the initial function and trajectory, generally, do not coincide at the initial moment. In this paper, the essential novelties are the optimality conditions of the initial moment and delay and the effect of discontinuity of the initial condition. An example is considered. [ABSTRACT FROM AUTHOR]

Abstract: Accurate kinetic models are of great significance for the simulation and analysis for hydrogen fuel cells. The proton exchange membrane (PEM) fuel cell is a complex nonlinear, multi-variable system. The mathematical modeling of PEM fuel cell usually leads to nonlinear parameter estimation problems which often contain more than one minimum. In this paper, a novel bio-inspired P systems based optimization algorithm, named BIPOA, is proposed to solve PEM fuel cell model parameter estimation problems. In BIPOA, the nested membrane structure and new rules such as adaptive mutation rule, partial migration rule and autophagy rule are combined to improve the algorithm''s global search capacities and convergence accuracy. Studies on some benchmark test functions indicate that the BIPOA outperforms the other two methods (PSOPS and GAs) in both convergence speed and accuracy. In addition, experimental results reveal that the model predictive outputs are in better agreement with the actual experimental data. Therefore, the BIPOA is a helpful and reliable technique for estimating the PEM fuel cell model parameters and is available to other complex parameter estimation problems of fuel cell models. [Copyright &y& Elsevier]

Functional magnetic resonance imaging (fMRI) is increasingly used for studying functional integration of the brain. However, large inter-subject variability in functional connectivity, particularly in disease populations, renders detection of representative group networks challenging. In this paper, we propose a novel technique, “group replicator dynamics” (GRD), for detecting sparse functional brain networks that are common across a group of subjects. We extend the replicator dynamics (RD) approach, which we show to be a solution of the nonnegative sparse principal component analysis problem, by integrating group information into each subject's RD process. Our proposed strategy effectively coaxes all subjects' networks to evolve towards the common network of the group. This results in sparse networks comprising the same brain regions across subjects yet with subject-specific weightings of the identified brain regions. Thus, in contrast to traditional averaging approaches, GRD enables inter-subject variability to be modeled, which facilitates statistical group inference. Quantitative validation of GRD on synthetic data demonstrated superior network detection performance over standard methods. When applied to real fMRI data, GRD detected task-specific networks that conform well to prior neuroscience knowledge. [ABSTRACT FROM PUBLISHER]

Abstract: This paper deals with set-valued vector optimization problems and set-valued vector variational inequalities in topological vector spaces, and provides some scalarization approaches for these problems by means of the polar cone and Gerstewitz’s scalarization functions. [Copyright &y& Elsevier]

Over the past few years, the field of global optimization has been very active, producing different kinds of deterministic and stochastic algorithms for optimization in the continuous domain. These days, the use of evolutionary algorithms (EAs) to solve optimization problems is a common practice due to their competitive performance on complex search spaces. EAs are well known for their ability to deal with nonlinear and complex optimization problems. Differential evolution (DE) algorithms are a family of evolutionary optimization techniques that use a rather greedy and less stochastic approach to problem solving, when compared to classical evolutionary algorithms. The main idea is to construct, at each generation, for each element of the population a mutant vector, which is constructed through a specific mutation operation based on adding differences between randomly selected elements of the population to another element. Due to its simple implementation, minimum mathematical processing and good optimization capability, DE has attracted attention. This paper proposes a new approach to solve electromagnetic design problems that combines the DE algorithm with a generator of chaos sequences. This approach is tested on the design of a loudspeaker model with 17 degrees of freedom, for showing its applicability to electromagnetic problems. The results show that the DE algorithm with chaotic sequences presents better, or at least similar, results when compared to the standard DE algorithm and other evolutionary algorithms available in the literature. [ABSTRACT FROM PUBLISHER]

Abstract: The recent application of the diagonal dominance in the development of the optimization algorithms in the wireless sensor networks design, has been done by Yuan and Yu (2006) , extended in Yu et al. (2006) , and surveyed in Machado and Tekinay . In this paper, we will use the concept of generalized diagonal dominance, to improve the obtained results regarding the power control game, in three directions. We also discuss the applicability of such improvements. [Copyright &y& Elsevier]

Abstract: The railroad blocking problem is one of the most important decision in freight railroads. The objective of this problem is to minimize the costs of delivering all commodities by deciding which inter-terminal blocks to build and specifying the assignment of commodities to these blocks, while observing limits on the number and aggregate volume of the blocks assembled at each terminal. This paper presents a metaheuristic algorithm based on ant colony optimization for solving this problem. To evaluate the efficiency of the proposed algorithm and the quality of solutions, experimental analysis is conducted, using several simulated test problems. The results on the test problems are compared with those of solution generated with CPLEX software. The results show high efficiency and effectiveness of the proposed algorithms. The solution method is applied to build car blocking plan in Islamic Republic of Iran Railways. By applying the presented model, Iran Railways can reduce the operational cost considerably and save the time in shipping the freights as well. [Copyright &y& Elsevier]

Abstract: This paper presents a new general mathematical programming model for the optimal retrofit of material conservation networks considering recycle, reuse and regeneration schemes (notice that most of the previously reported methods only have considered the synthesis case, and nowadays it is very important to have strategies for the retrofit of water networks because several existing water network are functioned under suboptimal conditions because these were synthesized using inefficient approaches or because the process and environmental constraints have changed). The model considers the reconfiguration of existing networks to satisfy stricter process and environmental constraints considering the repiping for the network, the reuse of the existing treatment units, the modification for the capacity and performance of the existing units and the installation of new treatment units to reduce the overall operating cost through the reduction of the use of fresh sources. The objective function accounts for the minimization of the total annual cost associated to the retrofit process. This retrofit process involves simultaneously economic (because the reduction of the fresh sources costs) and environmental (because the reduction of the waste streams discharged to the environment and with a better quality) improvements. The applicability of the proposed model is proved through a set of example problems addressed, where no numerical complications were observed. In addition, the proposed approach is general and it can be applied to any specific case with the information required. [Copyright &y& Elsevier]

This paper presents evolution strategy (ES) assisted by Kriging metamodel to preselect the most promising solutions and reduce the number of fitness function evaluations. The role of Kriging metamodel is to predict an objective function value for a new candidate solution by exploiting information recorded during previous generations. The use of promising solutions for screening the candidate solutions makes it possible to significantly reduce the computational cost of ES. The usefulness of the proposed method is verified by means of mathematical test cases and the optimal rotor structure design of interior permanent magnet synchronous machine. [ABSTRACT FROM AUTHOR]

Abstract: Last few years have witnessed the development of various multi-objective evolutionary algorithms since they allow the generation of the overall Pareto front for multi-objective optimizations. With the problems in the real-world becoming more and more complex, however, no reported work in the literature focuses on the high-dimensional objective optimizations (HOPs). In this paper, we propose an evolutionary algorithm named HOEA (high-dimensional objective evolutionary algorithm) for HOPs. By adopting the concept of nonlinear definition for optimizing object, HOPs can be solved by HOEA, while the well-known multi-objective evolutionary algorithms work poorly on HOPs. We further analyze the nonlinear dynamic properties of HOEA on the basis of martingale theoretical framework. The theoretical results indicate that this new algorithm is indeed capable of achieving convergence. We also conduct experiments on HOEA with two representative test instances. The experimental results either confirm our theoretical results or show that the proposed algorithm is efficient and effective for HOPs. [Copyright &y& Elsevier]

In this paper an inventory system is developed for deteriorating items with multi-variate demand. Here, demand rate is a function of time, stock and selling price. In this study, we have taken production rate is demand dependent. We have also considered the situation, when the retailer has the reserve money but still wants to avail the facility of progressive credit period. Shortages are allowed in inventory which is completely backlogged. An optimization framework has been presented, which derives optimal ordering policies when the present values of total cost has been optimized. [ABSTRACT FROM AUTHOR]

Polychromatic lighting sources that are composed of at least four different colored light-emitting diodes (LEDs) offer versatility in color quality of illumination. In this paper, different methods of assessment of color quality of white light are discussed and a general approach to the solution of the color-mixing problem by means of optimization in respect of several color rendition characteristics is considered. Spectral power distributions of model tetrachromatic solid-state sources obtained by maximizing various figures of merit, such as color rendering index, gamut area index, color quality scale, and indices based on the statistical analysis of the just perceivable chromaticity differences for a large number of test color samples, are demonstrated. A concept tetrachromatic lighting source that can be operated within a dynamical trade-off between two opposing color rendition characteristics, the ability to render colors with high fidelity and the ability to render colors with increased chromatic saturation, is introduced. Such "smart" sources with tailored color quality can meet individual needs and preferences of color vision and find numerous applications in lighting. [ABSTRACT FROM AUTHOR]

The negative effects of pulse width modulated switch-mode inverters on electrical motors, such as failures in the motor windings isolation and electrical discharge machinery leading to motor bearing failures, are well known. Long cables between inverter and motor, very often found in, for example, railway applications, further increase these effects due to the cable reflections. The aim of this paper is to present an easy-to-use tool for practicing engineers, providing mathematical analysis, design, and optimization of the load ratio control common-mode filtering network at the inverter output. This topology has been chosen after a literature research and considering the practical experiences with different common mode filter (CMMF) topologies. First, a CMMF operation is explained. After that the filter is mathematically analyzed using Laplace domain, leading to the definition of the filter optimization criteria, and in the end the simulation and experimental results are presented. This procedure was used on converters on different trains and has confirmed its value in operation. [ABSTRACT FROM PUBLISHER]

very efficient technique to drastically reduce the number of multipliers and adders in narrow transition-band linear-phase finite-impulse response digital filters is to use the one-stage or multistage frequency-response masking (FRM) approach, which has been originally introduced by Lim and further improved by Lim and Lian. In these original synthesis techniques, the subfilters in the overall implementation are separately designed. As shown earlier by the authors of this contribution together with Johansson, the arithmetic complexity in one-stage FRM filter designs can be considerably reduced by using the following two-step technique for simultaneously optimizing all the subfilters. First, a suboptimal solution is found by using a simple design scheme. Second, this solution is used as a start-up solution for further optimization, which is carried out with the aid of an efficient nonlinear optimization algorithm. This paper exploits this approach to synthesizing multistage FRM filters. An example taken from the literature illustrates that both the number of multipliers and the number of adders for the resulting optimized multistage FRM filters are approximately 70 percent compared with those of the filters synthesized using the original multistage FRM filter design schemes. Additional examples are included in order to show the benefits provided by the proposed synthesis scheme over other recently published design techniques, in terms of an improved performance of the resulting solution, a higher accuracy of the solution, and a faster speed required to arrive at the best solution. [ABSTRACT FROM AUTHOR]

Abstract: Recently, a new class of stochastic optimization algorithm called SOMA (self-organizing migrating algorithm) was proposed in the literature. SOMA works on a population of potential solutions called specimen and it is based on the self-organizing behavior of groups of individuals in a “social environment”. This paper proposes a SOMA approach combined with a cultural algorithm (CSOMA) technique based on normative knowledge as an alternative method to solving the economic load dispatch problem of thermal generators with the valve-point effect. The classical SOMA and CSOMA approaches are validated for two test systems consisting of 13 and 40 thermal generators whose non-smooth fuel cost function takes into account the valve-point loading effects. Numerical results indicate that performance of the CSOMA present best results when compared with results of others optimization methods found in the literature in solving load dispatch problems with the valve-point effect. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents a formal methodology for the hull form optimization in calm and rough water using wash waves and selected dynamic responses, respectively. Parametric hull form modeling is used to generate the variant hull forms with some of the form parameters modified, which are evaluated in the optimization scheme based on evolutionary strategies. Rankine-source panel method and strip theories are used for the hydrodynamic evaluation. The methodology is implemented in the optimization of a double-chine, planing hull form. Furthermore, a dual-stage optimization strategy is applied on a modern fast displacement ferry. The effect of the selected optimization parameters is presented and discussed. [Copyright &y& Elsevier]