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Copyright of Revista de Ciências da Computação is the property of Universidade Aberta and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this paper is to present a theoretic and numerical study of utilizing squeezing and phase shift in coherent feedback control of linear quantum optical systems. A quadrature representation with built-in phase shifters is proposed for such systems. Fundamental structural characterizations of linear quantum optical systems are derived in terms of the new quadrature representation. These results reveal considerable insights into the issue of the physical realizability of such quantum systems. The problem of coherent quantum linear quadratic Gaussian (LQG) feedback control studied in H. I. Nurdin, M. R. James, and I. R. Petersen, Automatica, IFAC, 45 (2009), pp. 1837-1846; G. Zhang and M. R. James, IEEE Trans. Automat. Control, 56 (2011), pp. 1535-1550 is reinvestigated in depth. First, the optimization methods in these papers are extended to a multistep optimization algorithm which utilizes ideal squeezers. Second, a two-stage optimization approach is proposed on the basis of controller parametrization. Numerical studies show that closed-loop systems designed via the second approach may offer LQG control performance even better than that when the closed-loop systems are in the vacuum state. When ideal squeezers in a closed-loop system are replaced by (more realistic) degenerate parametric amplifiers, a sufficient condition is derived for the asymptotic stability of the resultant new closed-loop system; the issue of performance convergence is also discussed in the LQG control setting. [ABSTRACT FROM AUTHOR]

Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since its introduction in 2004, the structural similarity (SSIM) index has gained widespread popularity as a tool to assess the quality of images and to evaluate the performance of image processing algorithms and systems. There has been also a growing interest of using SSIM as an objective function in optimization problems in a variety of image processing applications. One major issue that could strongly impede the progress of such efforts is the lack of understanding of the mathematical properties of the SSIM measure. For example, some highly desirable properties such as convexity and triangular inequality that are possessed by the mean squared error may not hold. In this paper, we first construct a series of normalized and generalized (vector-valued) metrics based on the important ingredients of SSIM. We then show that such modified measures are valid distance metrics and have many useful properties, among which the most significant ones include quasi-convexity, a region of convexity around the minimizer, and distance preservation under orthogonal or unitary transformations. The groundwork laid here extends the potentials of SSIM in both theoretical development and practical applications. ref refid="fnote1"/ id="fnote1" asterisk="no"paraSome preliminary results of this paper (specifically, parts of refid="sec2"Section II ) were presented at International Conference on Image and Analysis and Recognition, Burnaby, BC, Canada, June 2011.para [ABSTRACT FROM AUTHOR]

Effect-based weapon–target pairing assigns weapons to targets for the given desired effects on such targets. The most obvious and natural effects on targets are represented by the percentages of damage of these targets. In this paper, we focus on the generation of input for effect-based weapon–target pairing optimization. One way to generate such input is based on the Joint Munition Effectiveness Manual (JMEM). JMEM allows the evaluation of the weapons. It is a database that contains many tables, and each table contains many different data fields. Because of the sheer size of JMEM, the optimization of weapon–target pairing based on JMEM is currently focused mainly on one target at a time. In other words, the optimization of weapon–target pairing for many targets and weapons is not directly supported by JMEM, although all the necessary data is there. In this paper, we derive an input based on the given JMEM and desired effect(s), which should be useful in the follow-on effect-based weapon–target pairing optimization that is not limited to a single weapon or target. In particular, effect-based weapon–target pairing will rely on the scanning of the attack guidance table that we derive from JMEM to determine a preferred set of weapon combinations for engaging a given set of targets. [ABSTRACT FROM PUBLISHER]

In this paper, a class of sequencing problems with uncertain parameters is discussed. The uncertainty is modeled by the usage of fuzzy intervals, whose membership functions are regarded as possibility distributions for the values of unknown parameters. It is shown how to use possibility theory to find robust solutions under fuzzy parameters; this paper presents a general framework, together with applications, to some classical sequencing problems. First, the interval sequencing problems with the minmax regret criterion are discussed. The state of the art in this area is recalled. Next, the fuzzy sequencing problems, in which the classical intervals are replaced with fuzzy ones, are investigated. A possibilistic interpretation of such problems, solution concepts, and algorithms for the computation of a solution are described. In particular, it is shown that every fuzzy problem can be efficiently solved if a polynomial algorithm for the corresponding interval problem with the minmax regret criterion is known. Some methods to deal with NP-hard problems are also proposed, and the efficiency of these methods is explored. [ABSTRACT FROM AUTHOR]

This paper focuses on the optimization of a linear actuator by the “output space mapping (OSM)” method. The underlying objective of this work lies in the minimization of the time required for the achievement of this optimal design. Indeed, in addition to the sole costs of optimization processes strictly speaking, the time needed for the developpement of the models is taken into account. In the context of OSM, two different finite-element models of the same actuator are used. This paper presents these modeling solutions and considers their corresponding accuracy. Results of this multi-objective optimization method are presented and compared with those obtained by the sequential simplex (SS) method based solely on the fine model. Both approaches give similar results. However, the comparison of their performances clearly shows that the OSM algorithm is an effective technique for reducing the computation time of optimization studies, even in the case of relatively simple electromagnetic structures. Hence, this approach leads to an original and effective optimization methodology. [ABSTRACT FROM AUTHOR]

In this paper, we consider a robust lattice alignment design for K-user quasi-static multiple-input multiple-output (MIMO) interference channels with imperfect channel knowledge. With random Gaussian inputs, the conventional interference alignment (IA) method has the feasibility problem when the channel is quasi-static. On the other hand, structured lattices can create structured interference as opposed to the random interference caused by random Gaussian symbols. The structured interference space can be exploited to transmit the desired signals over the gaps. However, the existing alignment methods on the lattice codes for quasi-static channels either require infinite signal-to-noise ratio (SNR) or symmetric interference channel coefficients. Furthermore, perfect channel state information (CSI) is required for these alignment methods, which is difficult to achieve in practice. In this paper, we propose a robust lattice alignment method for quasi-static MIMO interference channels with imperfect CSI at all SNR regimes, and a two-stage decoding algorithm to decode the desired signal from the structured interference space. We derive the achievable data rate based on the proposed robust lattice alignment method, where the design of the precoders, decorrelators, scaling coefficients and interference quantization coefficients is jointly formulated as a mixed integer and continuous optimization problem. The effect of imperfect CSI is also accommodated in the optimization formulation, and hence the derived solution is robust to imperfect CSI. We also design a low complex iterative optimization algorithm for our robust lattice alignment method by using the existing iterative IA algorithm that was designed for the conventional IA method. Numerical results verify the advantages of the proposed robust lattice alignment method compared with the time-division multiple-access (TDMA), two-stage maximum-likelihood (ML) decoding, generalized Han–Kobayashi (HK), distributive IA and conventional IA methods in the literature. [ABSTRACT FROM PUBLISHER]

This paper describes the application of a novel multiobjective self-adaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles (HEVs). Based on an electric assist control strategy, the HEV optimal design problem is formulated as a nonlinear constrained multiobjective problem with competing and noncommensurable objectives of fuel consumption and emissions. The driving performance requirements are considered constraints. The proposed MOSADE approach adopts an external elitist archive to retain nondominated solutions that are found during the evolutionary process. To preserve the diversity of Pareto optimal solutions, a progressive comparison truncation operator based on the normalized nearest neighbor distance is proposed. Moreover, a fuzzy set theory is employed to extract the best compromise solution. Finally, the optimization is performed over the following three typical driving cycles that are currently used in the U.S. and European communities: 1) the file transfer protocol; 2) ECE +EUDC; and 3) Urban Dynamometer Driving Schedule. The results demonstrate the capability of the proposed approach to generate well-distributed Pareto optimal solutions of the HEV multiobjective optimization design problem. The comparison with the reported results of genetic-algorithm-based weighting sum approaches and Nondominated Sorting Genetic Algorithm II reveals the superiority of the proposed approach and confirms its potential for optimal HEV design. [ABSTRACT FROM PUBLISHER]

This paper presents a methodology of calculating the optimal torque and speed commands for the engine–generator system of a series hybrid electric vehicle (HEV). In series HEVs, the engine–generator subsystem provides electrical energy to the dc link. This paper proposes an optimal control strategy of the engine–generator subsystem to generate a desired amount of energy within a given period of time. The optimization algorithm, based on trajectory optimization, determines the torque and speed reference signals for the engine–generator subsystem that achieve maximum efficiency. A simplified version of the controller is also presented for online implementation. The proposed control strategy is compared with nonoptimized control techniques, and simulation results show the improvements in energy efficiency. [ABSTRACT FROM PUBLISHER]

The selection of optimum parameters in electromagnetic design usually requires optimization of multimodal, nonlinear functions. This leads to extensive calculations which pose a huge inconvenience in the design process. This paper proposes a novel algorithm for dealing efficiently with this issue. The proposed algorithm interprets the problem as an unexplored terrain for climbing. Through the use of contour line concept coupled with Kriging, the algorithm finds out all the peaks in the problem domain with as few function calls as possible. The efficiency of the proposed algorithm is demonstrated by application to conventional test functions. In this paper, the simulation results show that skewing does not necessarily reduce the cogging torque but may cause it to increase for certain pole-arc to pole-pitch ratio. The developed algorithm is applied to the magnet shape optimization of an axial flux permanent magnet synchronous machine and the cogging torque was reduced to 79.8% of the initial one. [ABSTRACT FROM AUTHOR]

This paper studies the performance of a wireless sensor network (WSN) in the context of binary detection of a deterministic signal. This paper aims to develop a numerical solution for the optimal power allocation scheme via a variation of the biogeography-based optimization (BBO) algorithm, which is called the constrained BBO-DE algorithm. This new stochastic optimization algorithm is a hybridization of a very recently proposed stochastic optimization algorithm, i.e., the BBO algorithm, with another popular stochastic optimization algorithm called the differential evolution (DE) algorithm. The objective is to minimize the total power spent by the whole sensor network under a desired performance criterion, which is specified as the detection error probability. The proposed algorithm has been tested for several case studies, and its performances are compared with those of two constrained versions of the BBO and DE algorithms. [ABSTRACT FROM PUBLISHER]

Automatic differentiation (AD) is introduced as a powerful technique to compute derivatives of functions given in the form of computer programs in high-level programming languages such as FORTRAN, C, or C++. This paper applies AD to compute error-free gradients of electromagnetic device sizing models. Then, the obtained gradients are exploited in optimization to size electromagnetic devices by means of minimizing a cost function with constrained parameters and performances. Often, the electromagnetic devices models have to be described not only by analytical formulas, but also by algorithms. This paper proposes an electromagnetic model of a linear actuator dealing with implicit equations solved by numerical algorithms. The ADOL-C package is considered for automatic differentiation. [ABSTRACT FROM PUBLISHER]

Recent years have witnessed extensive research activity in modeling biological phenomena as well as in developing intervention strategies for such phenomena. S-systems, which offer a good compromise between accuracy and mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena. In this paper, two different intervention strategies, namely direct and indirect, are proposed for the S-system model. In the indirect approach, the prespecified desired values for the target variables are used to compute the reference values for the control inputs, and two control algorithms, namely simple sampled-data control and model predictive control (MPC), are developed for transferring the control variables from their initial values to the computed reference ones. In the direct approach, a MPC algorithm is developed that directly guides the target variables to their desired values. The proposed intervention strategies are applied to the glycolytic–glycogenolytic pathway and the simulation results presented demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM PUBLISHER]

Based on a quantum-inspired evolutionary algorithm for unit commitment, this paper proposed ways to advance the efficiency and robustness of the algorithm so that its capacity for application in large-scale unit commitment problems can be significantly enhanced. The paper develops an advanced quantum-inspired evolutionary unit commitment algorithm by developing a new initialization method based on unit priority list and a special Q-bit expression for ensuring diversity in the initial search area for improving the efficiency of solution searching. Different techniques such as multi-observation, single-search, and group-search are also proposed for incorporation in the advanced algorithm. The advanced algorithm is tested and compared with the earlier quantum-inspired evolutionary algorithm and a number of known methods through its applications to test systems with up to 100 generator units for a 24-h scheduling horizon. [ABSTRACT FROM PUBLISHER]

Cognitive radio (CR) for dynamic spectrum sensing and access has been a hot research topic in recent years. To avoid collision with the primary users, secondary users need to sense the channels before transmitting on them, which is referred to as sensing time overhead. Our previous work shows that the spectral correlations between the channels within the same service are sufficiently high for accurate prediction, which can further be used to reduce the sensing time. With such motivation, in this paper, we propose a new definition, i.e., channel availability vector (CAV), to characterize the state information of a group of licensed channels by introducing spectrum prediction while focusing on the scenario of a single secondary user with multiple channels and leverage it by formulating the throughput optimization problem as a Markov decision process, which is further solved by our optimal sensing scheme and verified with the real spectrum measurement data. The results show that our prediction-based sensing scheme outperforms one existing work. [ABSTRACT FROM PUBLISHER]

In this paper, the use of a hybrid evolutionary optimization algorithm is proposed for global optimization of pavement structural parameters through inverse modeling. Shuffled complex evolution (SCE) is a population-based stochastic optimization technique combining the competitive complex evolution with the controlled random search, the implicit clustering, and the complex shuffling. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem, which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. SCE is especially considered a robust and efficient approach for global optimization of multimodal functions. A desirable characteristic of the SCE algorithm is that it uses information about the nature of the response surface, extracted using the deterministic Simplex geometric shape, to direct the search into regions with higher posterior probability. The hybrid back-calculation system described in this paper combines the robustness of the SCE in global optimization with the computational efficiency of neural networks and advanced pavement system characterization offered by employing finite-element models. This is the first time the SCE approach is applied to real-time nondestructive evaluation of pavement systems required in the routine maintenance and rehabilitation activities for sustainable transportation infrastructure. [ABSTRACT FROM AUTHOR]

In complex grid environment, a control system should consider all applications and coordinate all layers of grid architecture upon any changes in the system. However, this brings large overhead because any changes will invoke a global coordination. The paper proposes a multiple granularity control scheme in grid computing, which balances control scope and control frequency to improve system performance. Multiple granularity control policies are deployed at different levels: system level control at coarse time granularity and application level control at fine time granularity. System level control considers all applications and coordinates three layers of grid architecture in response to large system changes at coarse time granularity; it exploits the interlayer coupling of fabric layer, collective layer, and application layer to achieve a system-wide optimization based on the user's preferences. Application level control adapts a single application to small changes at fine time granularity. The paper presents a multiple granularity control algorithm (MGCA). Simulations are conducted to test the performance of the control algorithm. [ABSTRACT FROM AUTHOR]

Contamination emergency in water distribution systems is a complex situation where optimal operation becomes important for public health. In case of emergency corrective operational actions for flushing the pollutant out of the network are needed, which have to be fast and accurate. Under such a stressful situation, trial-and-error simulation experiments with the hydrodynamic and water quality models cannot be applied since significant number of model evaluations may be required to identify the optimal solution. This paper presents a methodology for finding sets of operational interventions in a supply network for flushing a contaminant by minimizing the impact on the population. The situation is treated as both single- and multiobjective optimization problem, which is solved by using evolutionary optimization approaches, in combination with the EPANET solver engine. The methodology is tested on a simple imaginary network configuration, as well as on a real case study for the city of Villavicencio in Colombia. The results prove the usefulness of the approach for advising the operators and decision makers. [ABSTRACT FROM AUTHOR]

The radio network design (RND) is an NP-hard optimization problem which consists of the maximization the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: noncomparable efficiency. Therefore, the aim of this paper twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve first aim we propose a canonical RND problem formulation driven by two main directives: technology independence a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced design methodology for optimal mixture proportion of concrete composition with respect to accuracy in the case of using prediction models based on a limited database. In proposed methodology, the search space is constrained as the domain defined by a limited database instead of constructing the database covering the region represented by the possible ranges of all variables in the input space. A model for defining the search space which is expressed by the effective region in this paper and evaluating whether a mix proportion is effective is added to the optimization process, yielding highly reliable results. To demonstrate the proposed methodology, a genetic algorithm, an artificial neural network, and a convex hull were adopted as an optimum technique, a prediction model for material properties, and an evaluation model for the effective region, respectively. And then, it was applied to an optimization problem wherein the minimum cost should be obtained under a given strength requirement. Experimental test results show that the mix proportion obtained from the proposed methodology considering the regional characteristics of the database is found to be more accurate and feasible than that obtained from a general optimum technique that does not consider this aspect. [ABSTRACT FROM AUTHOR]

Complex systems are large scale and involve numerous uncertainties, which means that such systems tend to be expensive to operate. Further, it is difficult to analyze systems of this kind in a real environment, and for this reason agent-based modeling and simulation techniques are used instead. Based on estimation methods, modeling and simulation techniques establish an output set against the existing input set. However, as the data set in a given complex systems becomes very large, it becomes impossible to use estimation methods to create the output set desired. Therefore, a new mechanism is needed to optimize data sets in this context. In this paper, the adaptive modified artificial bee colony algorithm is shown to be successful in optimizing the numerical test function and complex system parameter data sets. Moreover, the results show that this algorithm can be successfully adapted to a given problem. Specifically, this algorithm can be more successful in optimizing problem solving than either the artificial bee colony algorithm or the modified artificial bee colony algorithm. The adaptive modified artificial bee colony algorithm performs a search in response to feedback received from the simulation in run-time. Because of its adaptability, the adaptive modified artificial bee colony algorithm is of great importance for its ability to find solutions to multiple kinds of problems across numerous fields. [ABSTRACT FROM AUTHOR]

Economic load dispatch (ELD) is a crucial problem in the power system which is tackled by distributing the required generation power through a set of units to minimize the fuel cost required. This distribution is subject to two main constraints: (1) equality and inequality related to power balance and power output, respectively. In the optimization context, ELD is formulated as a non-convex, nonlinear, constrained optimization problem which cannot be easily solved using calculus-based techniques. Several optimization algorithms have been adapted. Due to the complexity nature of ELD search space, the theoretical concepts of these optimization algorithms have been modified or hybridized. In this paper, the grey wolf optimizer (GWO) which is a swarm intelligence is hybridized with β -hill climbing optimizer (β HC) which is a local search algorithm, to improve convergence properties. GWO is very powerful in a wide search, while β HC is very powerful in deep search. By combining the wide and deep search ability in a single optimization framework, the balance between the exploration and exploitation is correctly managed. The proposed hybrid algorithm is named β -GWO which is evaluated using five different test cases of ELD problems: 3 generating units with 850 MW; 13 generating units with 1800 MW; 13 generating units with 2520 MW; 40 generating units with 10,500 MW; and 80 generating units with 21,000 MW. β -GWO is comparatively measured using 49 comparative methods. The results obtained by β -GWO outperform others in most test cases. In conclusion, the proposed β -GWO is proved to be a powerful method for ELD problem or for any other similar problems in the power system domain. [ABSTRACT FROM AUTHOR]

Ready-to-wear clothing is typically based on the body-shape of human fit models that an apparel company hires. The body-shape difference between a consumer and the fit model of their size results in fit-loss of a certain degree. Aggregate-fit-loss is a concept attempting to quantify and estimate the accumulative fit-loss that a population may encounter. This paper reports on a novel method that minimizes the aggregate-fit-loss of a sizing system for bras, through shape categorization and optimized selection of prototypes (which can be regarded as the most appropriate fit models, or standard dress forms) for the categorized groups. A fit-loss function was introduced that calculates the dissimilarity between any two three-dimensional body scans, via pointwise comparisons of the point-to-origin distances of 9000 points on the scan surface. The within-group aggregate-fit-loss is minimized by an algorithm that returns the optimal prototype for the group. The overall aggregate-fit-loss is reduced by breast shape categorization based on the dissimilarities between the scans. Finally, the constraint of band sizes was brought into the categorization to provide a more feasible solution for improved bra sizing. The findings of this study can also contribute to the optimization of sizing systems for other apparel products. [ABSTRACT FROM AUTHOR]

This paper proposes an improved meta-heuristic algorithm called multivector particle swarm optimization (MVPSO) for solving single-objective optimization problems. MVPSO improves particle swarm optimization (PSO) algorithm by creating more possible solutions for each particle during the optimization process. It proposes a mathematical model and new position vectors for each particle that enhance the particle movement toward the global best value. This improvement emphasizes the exploration and exploitation of the particles in the search space during the optimization process. To test the performance of MVPSO, the algorithm is then benchmarked on 23 well-known test functions including unimodal, multimodal and fixed multimodal functions at different dimensions. These benchmark functions test the exploration, exploitation, local optima avoidance and convergence features of MVPSO. MVPSO has been compared to the state-of-the-art swarm optimization algorithms as well as PSO algorithm. Experimental results indicate that in terms of robustness, stability and quality of the solution obtained, MVPSO is better than original PSO algorithm, especially as the dimension increases. Further, it shows that a MVPSO based on the multivector mathematical model is competitive with the state-of-the-art swarm optimization algorithms. Moreover, the results of the tested benchmark functions, statistical analysis and performance metrics prove that the proposed algorithm is able to explore more solutions and regions in the search space, avoiding local optima points. [ABSTRACT FROM AUTHOR]

Large-Scale Antenna System (LSAS) has played an important role in the emerging fifth-generation mobile systems (5G) due to its potential for excellent spectral efficiency. However, it may cause a mass of pilot overhead that is not conducive to the application of LSAS in massive Machine-Type Communications (mMTC), one of three typical traffic modes of 5G. In this paper, we present a pilot-efficient scheduling strategy for mMTC systems, in which the Base Stations (BS) are equipped with large-scale antennas, from a cross-layer perspective. Our scheme can not only schedule the massive devices to access the spectrum, but also allocate the BS’ power without the need for much pilot overhead. More particularly, the users allowed to access the spectrum can be selected based on their queue state information without any channel estimations, while the power allocation only needs the channel estimations for scheduled users. We shall show the optimality of the presented policy based on the Lyapunov optimization theory. To solve the Lyapunov optimization problem, we present a low complexity two-layer iteration algorithm for more practical purposes. Simulation results demonstrate the substantial gain of our presented method over existing scheduling protocols of massive MIMO. [ABSTRACT FROM AUTHOR]

This paper addresses the formulation of power allocation for cluster based multihop vehicular relaying communications in order to improve the network quality-of-services (QoS). The cluster-to-cluster (C2C) channel is completely depend on the power gain of vehicle-to-vehicle (V2V) channel that increases the difficulties of the power allocation problem. To meet this goal, we have derived a global channel obtained by joint V2V and C2C cooperation. The most challenging aspect is however the joint V2V and C2C power allocation of the global channel due to the association of its multi-variables objective function. To solve this problem, we apply an alternative optimization technique to make the global problem into a series of sub-problems. We then apply a semi-definite programming (SDP)-based iterative gradient descent (SDP-IGD) power allocation to assign power in each relay. The SDP-IGD power allocation algorithm has been extended from Lagrange Multiplier (LM) that provides better performance than separate LM technique. The minimum mean-square error (MMSE)-decision feedback equalizer (MMSE-DFE) is designed at the receiver to improve the signal detection capability under exact channel state information (CSI) in all forward-and-backward links. Finally, simulation results confirm that our proposed solution performs significantly superior compared to other solutions found in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a new comprehensive learning gravitational search algorithm (CLGSA) is proposed to enhance the performance of basic GSA. The proposed algorithm is a new kind of intelligent optimization algorithm which has better ability to choose good elements. An intensive comprehensive learning methodology is proposed to enrich the optimization ability of the GSA. The efficiency of the proposed algorithm was evaluated by 28 benchmark functions which have been proposed in IEEE-CEC 2013 sessions. The results are compared with eight state-of-the-art algorithms IPOP, BIPOP, NIPOP, NBIPOP, DE/rand, SPSRDEMMS, SPSO-2011 and GSA. A variety of ways are considered to examine the ability of the proposed technique in terms of convergence ability, success rate and statistical behavior of algorithm over dimensions 10, 30 and 50. Apart from experimental studies, theoretical stability of the proposed CLGSA is also proved. It was concluded that the proposed algorithm performed efficiently with good results. [ABSTRACT FROM AUTHOR]

Task planning involves automating the creation of the routes for vessels with known coordinates in a confined space. The management of vessel release in a given area affects the time required for a vessel to complete its voyage, and maximizing vessel performance involves identifying the shortest route. A key issue in automating the generation of the optimal (shortest) routes is selecting the appropriate mathematical apparatus. This paper considers an optimization method based on a recursive algorithm using Bellman-Ford routing tasks for large dimensions. Unlike other optimization techniques, the proposed method enables the shortest path to be assessed in a network model with a complex topology, even if there are arcs with negative weights. The practical implementation of the modified Floyd algorithm was demonstrated using a sample automated build and using it to calculate a network model with a complex topology, using an iterative procedure for a program prepared in MATLAB. Implementation of the computer model is simple, and unlike existing models, it eliminates restrictions associated with the presence of negative weights and cycles on a network and automates search shortcuts in ground branch functional means in MATLAB. To confirm the accuracy of the obtained results, we performed an example calculation using the network. The proposed algorithm and recursive procedure are recommended for finding energy-efficient solutions during the management of mobile objects on waterways. [ABSTRACT FROM AUTHOR]

In this paper, a method based on genetic algorithms is proposed for improving the accuracy of solar cell parameters extracted using novel technique. We propose a computational based binary-coded genetic algorithm (GA) to extract the parameters (I 0 , I p ⁢ h , n , R s and R s ⁢ h ) for a single diode model of solar cell from its current-voltage (I–V) characteristic. The algorithm was implemented using Matlab as a programming tool and validated by applying it to the I–V curve synthesized from the literature using reported values. The characterization, current-voltage data used was generated by simulating a one-diode solar cell model of specified parameters. The new approach is based on formulating I–V equation of solar cell, with Lambert function, the parameter extraction as a search and optimization problem. Compared with other optimization techniques in literatures, the approach proposed for the determination of parameters are in good agreement. [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]

Worldwide interoperability for microwave access (WiMAX) is a promising technology for last-mile Internet access, particularly in the areas where wired infrastructures are not available. In a WiMAX network, call admission control (CAC) is deployed to effectively control different traffic loads and prevent the network from being overloaded. In this paper, we propose a framework of a 2-D CAC to accommodate various features of WiMAX networks. Specifically, we decompose the 2-D uplink and downlink WiMAX CAC problem into two independent 1-D CAC problems and formulate the 1-D CAC optimization, in which the demands of service providers and subscribers are jointly taken into account. To solve the optimization problem, we develop a utility- and fairness-constrained optimal revenue policy, as well as its corresponding approximation algorithm. Simulation results are presented to demonstrate the effectiveness of the proposed WiMAX CAC approach. [ABSTRACT FROM AUTHOR]

Flower pollination algorithm (FPA) is a computational intelligence metaheuristic that takes its metaphor from flowers proliferation role in plants. This paper provides a comprehensive review of all issues related to FPA: biological inspiration, fundamentals, previous studies and comparisons, implementation, variants, hybrids, and applications. Besides, it makes a comparison between FPA and six different metaheuristics such as genetic algorithm, cuckoo search, grasshopper optimization algorithm, and others on solving a constrained engineering optimization problem. The experimental results are statistically analyzed with non-parametric Friedman test which indicates that FPA is superior more than other competitors in solving the given problem. [ABSTRACT FROM AUTHOR]

The multi-reservoir systems optimization problem requires defining a set of rules to recognize the water amount stored and released in accordance with the system constraints. Traditional methods are not suitable for complex multi-reservoir systems with high dimensionality. Recently, metaheuristic-based algorithms such as evolutionary algorithms and local search-based algorithms are successfully used to solve the multi-reservoir systems. β-hill climbing is a recent metaheuristic local search-based algorithm. In this paper, the multi-reservoir systems optimization problem is tackled using β-hill climbing. In order to validate the proposed method, four-reservoir systems used in the literature to evaluate the algorithm are utilized. A comparative evaluation is conducted to evaluate the proposed method against other methods found in the literature. The obtained results show the competitiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

An evolutionary algorithm-based approach to identify effective courses of action (COAs) in dynamic uncertain situations is presented. The uncertain situation is modeled using timed influence nets, an instance of dynamic Bayesian networks. The approach makes significant enhancements to the current trial-and-error-based manual technique, which is not only labor intensive but also not capable of modeling constraints among actionable events. The proposed approach is an attempt to overcome these limitations. It automates the process of COA identification. It also allows a system analyst to capture certain types of constraints among actionable events. Because of its parallel search nature, the approach produces multiple COAs that have a similar fitness value. This feature not only gives more flexibility to a decision maker during mission planning, but it can also be used to generalize the COAs if there exists a pattern among them. This paper also discusses a heuristic that further enhances the performance of the approach. [ABSTRACT FROM AUTHOR]

To reduce field-programmable gate array power, Vdd programmability has been recently proposed to select the Vdd level for interconnects and power-gate unused interconnects. However, Vdd-level converters used in the existing Vdd-programmable method consume a large amount of leakage. This paper proposes two ways to avoid using level converters in interconnects, namely; 1) tree-based level converter insertion (TLC) and 2) dual-Vdd tree-based level converter insertion (dTLC). TLC enforces that there is only one Vdd level within each routing tree, while dTLC can have different Vdd levels within a routing tree, but no VddL switch drives VddH switches. Dual-Vdd assignment algorithms were developed considering chip-level time slack allocation for maximum power reduction. The algorithms include TLC-S and dTLC-S, two power sensitivity-based algorithms with implicit time slack allocation, and dTLC-LP, a linear programming (LP)-based algorithm with explicit time slack allocation. All allocate time slack first to interconnects with higher power sensitivity and assign low Vdd to them for more power reduction. Experiments show that dTLC-LP obtains the lowest power consumption. Compared to dTLC-LP, dTLC-S obtains a slightly higher power consumption but runs three times faster. Compared to the existing segment-based level converter insertion for dual Vdd, dTLC-LP reduces interconnect power by 52.90% without performance loss for Microelectronics Center of North Carolina benchmark circuits. [ABSTRACT FROM AUTHOR]

The problem under consideration is to obtain a measurement schedule for training neural networks. This task is perceived as an experimental design in a given design space that is obtained in such a way as to minimize the difference between the neural network and the system being considered. This difference can be expressed in many different ways and one of them, namely, the D-optimality criterion is used in this paper. In particular, the paper presents a unified and comprehensive treatment of this problem by discussing the existing and previously unpublished properties of the optimum experimental design (OED) for neural networks. The consequences of the above properties are discussed as well. A hybrid algorithm that can be used for both the training and data development of neural networks is another important contribution of this paper. A careful analysis of the algorithm is presented and its comprehensive convergence analysis with the help of the Lyapunov method are given. The paper contains a number of numerical examples that justify the application of the OED theory for neural networks. Moreover, an industrial application example is given that deals with the valve actuator. [ABSTRACT FROM AUTHOR]

The network signal design problem (NSDP) seeks the optimal deployment of traffic signals in a growing urban area. This paper is especially concerned with how signals may be optimally deployed over a very long period of time for the purpose of creating realistic networks for travel forecasting. The NSDP is very difficult to solve for long-range problems because of the large number of possible solutions, the high cost of evaluating the merits of just a single solution, and the complexities of how signal delay affects traffic patterns and how traffic patterns affect signal delay. The paper describes the NSDP, introduces a reasonable set of simplifications based on transportation planning and traffic engineering practice, describes experiences with a possible heuristic algorithm for problem solution, and contrasts this method with current planning practice and other research. The long-range algorithm embeds a “strategic” algorithm for finding an optimal deployment for a single time period with constant travel demands. The strategic algorithm draws upon two well-known techniques of combinatorial optimization: a greedy constructive search coupled with a restricted neighborhood search. The strategic algorithm was able to find exact solutions on a small test network with eight stop-controlled intersections. The long-range algorithm is demonstrated on a full-sized planning network with about 380 stop-controlled intersections that could be signalized. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of a dc drive system with elastic joints and different speed controllers. The control structure with one and two speed feedbacks was analyzed. The dynamics of the drive system with classical proportional-integral (P1) and fuzzy-logic (FL) speed controllers was compared. Parameters of the classical PI and FL speed controllers were optimized using the same control indexes. Controllers were parameterised using the hybrid genetic-gradient algorithm. The simulation results for different parameters and operation modes of the drive system were demonstrated and compared. [ABSTRACT FROM AUTHOR]

The paper presents the current development of the Generalized Intelligent Grinding Advisory System (GIGAS), which provides a systematic way of modelling complex grinding processes and finding optimal process conditions while meeting the general class of process requirements. GIGAS provides a way of incorporating three different types of knowledge, including analytical models, experimental data and heuristic knowledge from experts, to describe complex grinding processes. The developed optimization algorithm can handle various optimization problems including different grinding processes and optimization objectives. Case studies are presented for surface grinding and cylindrical plunge-grinding with various optimization objectives to demonstrate its capability of performing optimization. The overall architecture and the developed software with the graphical user interface are described. [ABSTRACT FROM AUTHOR]

Reservoir systems are essential for water resources management. The application and development of optimization techniques for optimal reservoir operation is therefore a valuable undertaking. This paper presents a modified firefly algorithm (MFA) and applies it to optimally solve reservoir operation problems. Three well-known benchmark multireservoir operation problems are optimized for energy production. The results of the MFA are compared with results obtained with other mathematical programming approaches, such as linear programming (LP), differential dynamic programming (DDP), and discrete DDP (DDDP), the genetic algorithm (GA), the multicolony ant algorithm (MCAA), the honey-bee mating optimization (HBMO) algorithm, the water cycle algorithm (WCA), the bat algorithm (BA), and the biogeography-based optimization (BBO) algorithm. The MFA was found to be more effective than alternative optimization methods in solving the test problems demonstrating its strong potential to tackle multireservoir operation problems. This paper's results indicate that the MFA differed by 0.01 and 0.79% with the LP global optimal solutions of a continuous four-reservoir problem (CFP) and a continuous 10-reservoir problem (CTP), respectively. The objective function of a discrete four-reservoir problem (DFP) obtained with the MFA is equal to the LP's objective function. This paper demonstrates that the MFA is a competitive optimization method with which to solve a variety of reservoir operation problems. [ABSTRACT FROM AUTHOR]

In this paper, an improved algorithm of wavelet packet-chaos model for life prediction of space relays based on volterra series is proposed. In the proposed method, the high and low frequency time sequence components of performance parameters are obtained by employing the improved wavelet packet transform to decompose the performance parameters of the relay into multiple scales. Then the optimization algorithm of parameters in volterra series is improved, and is used to construct a chaotic forecasting model for the high and low frequency time sequence components gained by the wavelet packet transform. At last, the chaotic forecasting results of the high and low frequency components are combined by taking the wavelet packet reconstruction approach, so as to predict the lifetime of the studied space relay. The algorithm can predict the life curve of the relay accurately and reflect the characteristics of the relay performance with sufficient accuracy. The proposed method is validated via a case study of a space relay. [ABSTRACT FROM AUTHOR]

Information carried by an image can be distorted due to different image processing steps introduced by different electronic means of storage and communication. Therefore, development of algorithms which can automatically assess a quality of the image in a way that is consistent with human evaluation is important. In this paper, an approach to image quality assessment (IQA) is proposed in which the quality of a given image is evaluated jointly by several IQA approaches. At first, in order to obtain such joint models, an optimisation problem of IQA measures aggregation is defined, where a weighted sum of their outputs, i.e., objective scores, is used as the aggregation operator. Then, the weight of each measure is considered as a decision variable in a problem of minimisation of root mean square error between obtained objective scores and subjective scores. Subjective scores reflect ground-truth and involve evaluation of images by human observers. The optimisation problem is solved using a genetic algorithm, which also selects suitable measures used in aggregation. Obtained multimeasures are evaluated on four largest widely used image benchmarks and compared against state-of-the-art full-reference IQA approaches. Results of comparison reveal that the proposed approach outperforms other competing measures. [ABSTRACT FROM AUTHOR]

IC3 is one of the most successful algorithms for hardware model checking. Since its invention in 2010, several variants of the original algorithm have been published, proposing optimizations and/or alternative procedures for many different steps of the algorithm. In this paper, we present a thorough empirical comparison of a large set of optimizations and procedures for the steps of IC3, considering “high-level” variants/extensions to the basic algorithm, as well as “low-level” optimizations/configuration settings. We implemented each of them in the same tool, optimizing the implementations to the best of our knowledge. This enabled for a flexible experimentation in a controlled environment, and to gain new insights about their most important differences and commonalities, as well as about their performance characteristics. We conducted the experiments using as benchmarks the problems used in the last four editions of the hardware model checking competition. The analysis helped us to identify several settings leading to significant improvements with respect to a basic implementation of IC3. [ABSTRACT FROM AUTHOR]

This paper introduces m -inductiveness over a set of nodes S in sequential circuits. The m -inductive property can be used for equivalence-checking or improved sequential optimization. It allows the behavior of many next state functions (not in S ) to be changed while maintaining correctness at the primary outputs of a circuit. As such, it creates flexibility that can be used for sequential optimization. It is shown that the number of nodes in S is reduced as m, the parameter for the inductiveness, increases. We provide an algorithm for finding a minimal set S , as well as one for using m -inductiveness in optimization. We give examples of such optimized circuits and show that m -inductive-based optimization can result in significant area reduction when applied to industrial designs. [ABSTRACT FROM AUTHOR]

One of the very important topics in discrete optimization, motivated by practical applications, is Internet shopping, which is becoming increasingly popular each year. More classical versions of the Internet shopping optimization problem (ISOP) are closely related to the facility location problem and some scheduling problems and have been intensively studied in the literature. In this paper, extensions of the problem are defined and studied. The issue is to buy all the necessary products for a minimum total possible price. This includes all prices of products as well as shipping costs. Studies in this paper include the ISOP with price sensitive discounts and a newly defined optimization problem: the ISOP including two different discounting functions, namely a shipping cost function as well as a price discounting function. First, these are formulated as mathematical programming problems. Then, some algorithms are constructed and extensively tested in a computational experiment. [ABSTRACT FROM AUTHOR]