Search

This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain. [ABSTRACT FROM AUTHOR]

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

Abstract: this paper gives them some properties. This paper firstly introduces rough logic, and proposes factorization of model based on rough logic, and proposes the optimal factorization of model, so one complex model could be factorized into several simple models, which are easy to be handled. this paper not only gives them some properties, but also analyze the time complexity of it. [Copyright &y& Elsevier]

Abstract: This paper firstly introduces rough logic, and proposes factorization of model based on rough logic, and proposes the optimal factorization of model, so one complex model could be factorized into several simple models, which are easy to be handled. this paper gives them somae properties. [Copyright &y& Elsevier]

Abstract: This paper presents a new individual based optimization algorithm, which is inspired from asexual reproduction known as a remarkable biological phenomenon, called as asexual reproduction optimization (ARO). ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem; this leads to the fitter individual. ARO adaptive search ability along with its strength and weakness points are fully described in the paper. Furthermore, the ARO convergence to the global optimum is mathematically analyzed. To approve the effectiveness of the ARO performance, it is tested with several benchmark functions frequently used in the area of optimization. Finally, the ARO performance is statistically compared with that of an improved genetic algorithm (GA). Results of simulation illustrate that ARO remarkably outperforms GA. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the honey bee mating optimization (HBMO) algorithm is used to improve the detection of the concave region connected with the control points of active contour. In the traditional active contour model (ACM) method, the updating of control point is based on its local energy within a small searching window. As a result, it always results in the failure of precisely searching the boundary concavities. In order to vanquish these drawbacks, the HBMO-based snake algorithm is applied in this paper to search for the optimal position in a lager searching window around each control point. In this proposed algorithm, to each active contour there is a chromosome that includes several genes as well as the control points of active contour. These control points are moved iteratively by minimizing the total energy of the active contour. Experimental results reveal that the proposed HBMO-based snake algorithm can locate the object boundary of concavity more precisely without requiring large number of computational time. [Copyright &y& Elsevier]

Abstract: In this paper, we consider systems of vector quasi-variational inclusions which include systems of vector quasi-equilibrium problems for multivalued maps, systems of vector optimization problems and several other systems as special cases. We establish existence results for solutions of these systems. As applications of our results, we derive the existence results for solutions of system vector optimization problems, mathematical programs with systems of vector variational inclusion constraints and bilevel problems. Another application of our results provides the common fixed point theorem for a family of lower semicontinuous multivalued maps. Further applications of our results for existence of solutions of systems of vector quasi-variational inclusions are given to prove the existence of solutions of systems of Minty type and Stampacchia type generalized implicit quasi-variational inequalities. The results of this paper can be seen as extensions and generalizations of several known results in the literature. [Copyright &y& Elsevier]

Abstract: This paper is concerned with a new type of the classical Bernstein operators where the function is evaluated at intervals . We also make extensive study simultaneous approximation by the linear combination of these new Bernstein type operators . At the end of this paper we have given an other modification of these operators. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to establish that there exist suitable objective functions for mathematical programming formulations of dynamic user equilibrium assignment in the sense of . Recently, showed that we cannot necessarily calculate a dynamic user equilibrium assignment by minimising the integral over time of Beckmann’s objective function that is widely used to solve for static user equilibrium assignment. In the present paper, two distinct objective functions are presented that result from formulations of assignment either as a variational inequality or as an infinitesimal optimisation: each of these formulations can be solved as a forward dynamic programme. With these formulations and solution approaches, we solve an example based upon Lin and Lo’s one correctly. [Copyright &y& Elsevier]

Oil and gas companies play an important role in the global economy since they supply a large portion of the necessary energy to the world. The optimal production of oil and gas should be performed in an integrated fashion for the whole supply chain. The downstream oil and gas supply chain (OGSC) has attracted the interest of many researchers due to its central role in the world economy. This paper develops an integrated multi-objective OGSC model for medium-term tactical decision making for the OGSC downstream segment. The selected objectives related to downstream activities are the following: minimize the total cost, maximize the total revenue, and maximize the service level. The model includes multi-period and multi-product inputs. The model is verified and solved using an improved augmented ε-constraint algorithm to generate Pareto optimal solutions. The model assists in assessing various trade-offs among different objectives and guides decision makers for the effective management of the downstream OGSC. The utility of the proposed model is demonstrated using a real case from a Saudi Arabian downstream OGSC. Sensitivity analysis is conducted to investigate the effects of input parameters on the set of Pareto optimal solutions. The model is expected to have a positive impact on the future management of this important component of the energy sector. [ABSTRACT FROM AUTHOR]

This paper is discussed with the problem of passivity based synchronization for a class of complex dynamical networks (CDNs) consisting uncertain inner coupling matrix together with successive time-varying delays via a state feedback delayed controller. Due to occurrence of uncertainties in coupling strengths, the considered CDNs take account of an uncertain inner coupling strength which is more general than the previously existing inner coupling strengths. Specifically, the uncertainties encountering in coupling terms are characterized with the aid of interval matrix approach. Also, by introducing a simple linear transformation, the corresponding error system is formulated. Then, based on the information about control delay term, two cases are considered namely, differentiable and non-differentiable. More precisely, by constructing an appropriate Lyapunov–Krasovskii functional (LKF) containing triple integral terms in respect of Kronecker product, for both the cases, some sufficient criteria are established in terms of linear matrix inequalities (LMIs) to guarantee the robust synchronization of the addressed CDNs based on passivity property. And the established criteria optimistically reduce the L 2 gain level from the disturbance to the output vector. Subsequently, the desired state feedback gain matrix is designed in terms of the solution to a convex optimization problem. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we consider the quantized distributed optimization problem over general directed digital multi-agent networks, where the communication channels have limited data transmission rates. To solve the optimization problem, a distributed quantized subgradient algorithm is presented among agents. Based on an encoder–decoder scheme and a zoom-in technique, we can achieve not only a consensus, but also an optimal solution. In particular, we study two cases of the quantization levels of each connected directed digital communication channel. One is under the case that the quantization levels are time-varying at each time step, and the other is under the case of fixed quantization level. Two rigorous theoretical analyses are performed and the optimal solutions can be obtained asymptotically. Moreover, the upper bound of the quantization levels at each time step and the convergence rate are analytically characterized. The effectiveness of proposed algorithm is demonstrated by two illustrative examples. [ABSTRACT FROM AUTHOR]

The state-of-art speaker recognition system employs vocal tract information for modeling through different supervised and unsupervised models. Whereas, the baseline of the paper uses LP-residual as the acoustic feature for the following studies. Fuzzy C-Means (FCM) is used to model the information extracted from LP-residual to develop speaker models. FCM is a well-known unsupervised fuzzy model used in speech recognition. Speakers are modeled in order to develop a text-dependent Automatic Speaker Verification (ASV) system. The performance of FCM model have been observed for different codebook sizes varying from 32 to 1024. Also studies are carried out for different fuzzy membership values varying from 1.39 to 4. For LP-residual features, the performance of FCM remains unchanged even after changing the codebook sizes whereas with the change of fuzzy membership the performance of the system is observed to vary. [ABSTRACT FROM AUTHOR]

The main aim of hierarchical lossless image compression is to improve accuracy, reduce the bit rate and improve the compression efficiency for the storage and transmission of the medical images while maintain an acceptable image quality for diagnosis purpose. The cost and limitation in bandwidth of wireless channels has made compression is necessity in today's era. In medical images, the contextual region is an area which contains an important information and must be transmitted without distortion. In this paper the selected region of the image is encoded with Adaptive Multiwavelet Transform AMWT) using Multi Dimensional Layered Zero Coding (MLZC). Experimental results shows that Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), Mean Structural Similarity Index (MSSIM) performance is high and Root Mean Square Error (RMSE), Mean Absolute Error (MAE) values are low, and moderate Compression Ratio (CR) at high Bits Per Pixel (BPP) when compared to the integer wavelet and multiwavelet transform. [ABSTRACT FROM AUTHOR]

Financial distress and then the consequent failure of a business is usually an extremely costly and disruptive event. Statistical financial distress prediction models attempt to predict whether a business will experience financial distress in the future. Discriminant analysis and logistic regression have been the most popular approaches, but there is also a large number of alternative cutting – edge data mining techniques that can be used. In this paper, a semi-parametric Cox survival analysis model and non-parametric CART decision trees have been applied to financial distress prediction and compared with each other as well as the most popular approaches. This analysis is done over a variety of cost ratios (Type I Error cost: Type II Error cost) and prediction intervals as these differ depending on the situation. The results show that decision trees and survival analysis models have good prediction accuracy that justifies their use and supports further investigation. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we consider the minimum flow problem on network flows in which the lower arc capacities vary with time. We will show that this problem for set {0,1,…, T} of time points can be solved by at most n minimum flow computations, by combining of preflow-pull algorithm and reoptimization techniques (no matter how many values of T are given). Running time of the presented algorithm is O(n 2 m). [Copyright &y& Elsevier]

Abstract: Cross-efficiency evaluation is an extension of Data Envelopment Analysis (DEA) that permits not only the determination of a ranking of Decision Making Units (DMUs) but also the elimination of unrealistic weighting schemes, thereby rescinding the necessity for the inclusion of individual judgements in the models. The main deficiency of the procedure is the non-uniqueness of the optimal weights, which results in the peer evaluations dependences, for instance, on the software used to determine DMU’s efficiencies. This shortfall justifies the inclusion of secondary goals in order to determine cross-efficiency values. In this paper a new proposal of a secondary goal is studied. The idea is related with that proposed in Wu et al. (2009), in which the objective is the optimization of the rank position of the DMU under evaluation. In the procedure proposed here, an incentive to break level-pegging ties between alternatives is introduced by considering that efficiency scores induce a weak order of alternatives. The model is illustrated with a preference-aggregation application. [Copyright &y& Elsevier]

Abstract: In this paper, a mathematical model is proposed to estimate the resonant frequency of a N-sided Regular Polygon Split Ring Resonator (NRPSRR), which is extended to the Circular Split Ring Resonator (CSRR) for N tending to infinity. The model also predicts the variation in the resonant frequency with the angle of rotation (skew) between the inner and the outer polygon. It is mathematically shown that the minimum resonant frequency is obtained at zero skew. Genetic Algorithm Optimization is used over the independent parameters deciding the geometry of the NRPSRR to optimize them for a particular desired resonant frequency by minimizing the relative error between the calculated and desired frequency. NRPSRR metamaterial loaded microstrip antenna could be used to obtain highly directional beam pattern and increased gain for miniaturized (electrically small) antenna systems in the microwave regime. [Copyright &y& Elsevier]

Abstract: This paper provides a solution to address one of the requirements identified in the US Northwest 6th Power Plan, specifically, meeting an increase in the forecasted Pacific Northwest demand with renewable generation. A model is presented which evaluates one source of renewable generation, off-shore wind energy. The model analyzes different wind turbine design characteristics, assigns significance to potential synergistic effects of one design feature on another, and provides an optimized design relative to the cost, environment, robustness, and overall efficiency of an off-shore wind turbine. [Copyright &y& Elsevier]

Abstract: This paper presents a novel mathematical method for incoming target detection in a cluttered background motivated by the robust properties of the human visual system (HVS). The robust detection of small targets is very important in IRST (Infrared Search and Track) applications for self-defense or attacks. HVS shows the best efficiency and robustness for the task of object detection in cluttered backgrounds. The robust properties of HVS include the contrast mechanism of figure-ground, multi-resolution representation of an object, size adaptation of object boundary, and pop-out phenomena in a complex environment. Based on these facts, a plausible computational model integrating these facts is proposed using Laplacian scale-space theory and an optimization method. Simultaneous target signal enhancement and background clutter suppression are achieved by tuning and maximizing the signal-to-clutter ratio (TM-SCR) in Laplacian scale-space. At the first stage, Tune–Max of the signal to background contrast produces candidate targets with estimated target scale. At the second stage, Tune–Max of the signal-to-clutter ratio (SCR) produces maximal SCR that is used to sort the detection results. Especially, the row-directional-local background removal filter (RD-LBRF) is preprocessed in the horizontal region to enhance the TM-SCR method. The evaluation results of incoming target sequence validate the detection capability of the proposed method from dim, small targets to strong, large targets in comparison with the Top-hat method at the same rate of false alarms. The experimental results of various cluttered background images show that the proposed TM-SCR produces less false alarms (4.3 times reduction) compared to that of the Top-hat at the same detection rate. Finally, TM-SCR after RD-LBRF can maximize the detection rate in horizontal regions. [Copyright &y& Elsevier]

Abstract: This paper outlines the basic concept and principles of two simple and powerful swarm intelligence tools: the particle swarm optimization (PSO) and the Bacterial Foraging Optimization (BFO). The adaptive identification of an unknown plant has been formulated as an optimization problem and then solved using the PSO and BFO techniques. Using this new approach efficient identification of complex nonlinear dynamic plants have been carried out through simulation study. [Copyright &y& Elsevier]

Abstract: This paper is concerned with constructing optimal designs for rational models which are used for modeling problems in Agriculture and other disciplines. Homoscedastic and weighted models are considered. An analytical characterization of these designs is obtained as zeros of a polynomial solution of a second order differential equation. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate on a derivative-free optimization approach, simulated annealing (SA), for the optimization of the reactive distillation (RD) column design. Because RD systems exhibit non-monotonic behavior for key design variables, flowsheet optimization using simulator is difficult when derivative-based approach is employed. The SA-based optimization procedure gives an equally good or better design than the optimal flowsheet obtained from the sequential design approach. More importantly, this is achieved with much more efficient computing. [Copyright &y& Elsevier]

Abstract: This paper deals with the complexity of the decomposition of a digital surface into digital plane segments (DPSs for short). We prove that the decision problem (does there exist a decomposition with less than DPSs?) is NP-complete, and thus that the optimization problem (finding the minimum number of DPSs) is NP-hard. The proof is based on a polynomial reduction of any instance of the well-known 3-SAT problem to an instance of the digital surface decomposition problem. A geometric model for the 3-SAT problem is proposed. [Copyright &y& Elsevier]

Abstract: This paper concerns a minimax model to investigate the optimal portfolio selection problem without riskless assets and with or without short sale restriction. A numerical solution to the problem with short sale restriction is obtained by using the maximum entropy algorithm. For the problem without short sale restriction, we derive a analytical expression for the optimal solution, a sufficient condition for the existence and uniqueness of a nonnegative equilibrium price system, and an explicit formula for the price system. Furthermore, a numerical example is given to show the validity of the method. [Copyright &y& Elsevier]

Abstract: In this paper, we present a nonmonotone trust-region method of conic model for unconstrained optimization. The new method combines a new trust-region subproblem of conic model proposed in [Y. Ji, S.J. Qu, Y.J. Wang, H.M. Li, A conic trust-region method for optimization with nonlinear equality and inequality 4 constrains via active-set strategy, Appl. Math. Comput. 183 (2006) 217–231] with a nonmonotone technique for solving unconstrained optimization. The local and global convergence properties are proved under reasonable assumptions. Numerical experiments are conducted to compare this method with the method of [Y. Ji, S.J. Qu, Y.J. Wang, H.M. Li, A conic trust-region method for optimization with nonlinear equality and inequality 4 constrains via active-set strategy, Appl. Math. Comput. 183 (2006) 217–231]. [Copyright &y& Elsevier]

Abstract: In this paper, by introducing a new nonmonotone line search technique and combining it with the spectral projected gradient method, we propose a solution method for solving the bound constrained optimization problems. Under mild conditions, we obtain the global convergence even without requiring a prior of the existence of a limit point. Numerical tests are also given to show the efficiency of the proposed method. [Copyright &y& Elsevier]

Abstract: In this paper, we study the conditions in which the random hill-climbing algorithm (1 + 1)-EA compares favorably to other evolutionary algorithms (EAs) in terms of fitness function distribution at a given iteration and with respect to the average optimization time. Our approach is applicable when the reproduction operator of an evolutionary algorithm is dominated by the mutation operator of the (1 + 1)-EA. In this case one can extend the lower bounds obtained for the expected optimization time of the (1 + 1)-EA to other EAs based on the dominated reproduction operator. This method is demonstrated on the sorting problem with HAM landscape and the exchange mutation operator. We consider several simple examples where the (1 + 1)-EA is the best possible search strategy in the class of the EAs. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a higher-order Mond–Weir dual for a set-valued optimization problem by virtue of higher-order contingent derivatives and discuss their weak duality, strong duality and converse duality properties. [Copyright &y& Elsevier]

Abstract: In many countries the power systems are going to move toward creating a competitive structure for selling and buying electrical energy. These changes and the numerous advantages of the distributed generation units (DGs) in term of their technology enhancement and economical considerations have created more incentives to use these kinds of generators than before. Therefore, it is necessary to study the impact of DGs on the power systems, especially on the distribution networks. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering DGs. The main objective of the DFR is to minimize the deviation of the bus voltage, the number of switching operations and the total cost of the active power generated by DGs and distribution companies. Since the DFR is a nonlinear optimization problem, we apply the particle swarm optimization (PSO) approach to solve it. The feasibility of the proposed approach is demonstrated and compared with other evolutionary methods such as genetic algorithm (GA), Tabu search (TS) and differential evolution (DE) over a realistic distribution test system. [Copyright &y& Elsevier]

Abstract: This paper presents a procedure for robust two-degrees-of-freedom PID synthesis based on a non-convex optimization problem considering the and/or guaranteed costs of the closed-loop transfer matrix as objective functions and constraints together with robust regional pole placement. The uncertain system can be represented by multiple, polytopic or affine parameter-dependent models. The efficiency of the proposed synthesis procedure is illustrated by examples. [Copyright &y& Elsevier]

Abstract: The problem of optimizing a dynamic system in the presence of uncertainty is typically tackled using measurements. The method most widely documented in the literature is based on repetitive optimization of an updated process model. Recently, a computationally less expensive alternative that is based on the real-time adaptation of a solution model using measurements has been proposed. The solution model, which relates elements of the input profiles to the set of active constraints and to appropriate sensitivities, has typically been derived manually from physical insight and intuition. This paper presents a systematic and automated approach to generate a solution model based on recent results in numerical optimization of dynamic systems. This concept provides the first step toward an entirely automated procedure for constrained dynamic optimization of uncertain large-scale processes. [Copyright &y& Elsevier]

In this paper, we consider a class of optimal control problems in which the dynamical system involves both switching and impulsive controls. The number of switching times are supposed to be finite and, without loss of generality, to occur together with the impulses. We show that this class of optimal control problems is equivalent to a special class of optimal parameter selection problems. The gradient formulae for the cost functional and the constraint functional are derived. On this basis, the control parameterization is shown to be applicable for numerical solution. A numerical example is solved for illustration. [Copyright &y& Elsevier]