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Abstract: Recent advances in artificial intelligence, particularly in the field of multi-agent system theory and techniques, offer great promises in the development of decision support systems. This paper designs an agent-based regional agricultural economy decision support system (RAEDSS) to deal with complex decision problems. It introduces the architecture of the system, including interface agents, management agents, functional agents, model agents, information agents and knowledge agents and their interactions. Since dynamic analysis, evaluation, forecast, optimization and decision of regional agricultural economy are the central task of RAEDSS, this paper gives a detailed discussion on the decision processes and internal mechanisms in the system. Meanwhile, agent-based modeling is introduced to simulate and evaluate policy impact on rural development in different scenarios as an important part in RAEDSS. The simulation result shows that this agent-based agricultural development model is able to perform regeneration and is able to produce likely-to-occur projections of reality. The related issue such as building an agent based on the theory of classifier systems is also surveyed. [Copyright &y& Elsevier]

Abstract: Power loss is one of the most important issues when dealing with distribution networks. Due to the nature of power loss, it can be an inseparable part of these networks. This is while an optimal reconfiguration is a great optimization procedure to power loss reduction in distribution networks. Moreover, to perform optimal dynamic reconfiguration, determining optimal time intervals and detecting the most proper time points greatly affects the total benefit achieved from this process. This benefit includes the cost of reconfiguration and the benefit of power loss reduction. This paper proposes an evolutionary approach for optimal time interval determination. In this paper basic reconfiguration models are discussed to form an optimal time interval model gradually. The Genetic Algorithm (GA) is used to solve the suggested model while the proposed method is implemented on an IEEE-33 Bus network. In order to examine the effectiveness of the proposed method, a comparison is done with other similar procedures. Also, in order to validate the numerical results, further compression is done with a method using a Binary Particle Swarm Optimization (BPSO) algorithm rather than the GA. [Copyright &y& Elsevier]

Abstract: In this paper we investigate the optimal risk control and dividend distribution problem for a diffusion model with a terminal value. Usually the insurer cedes risk by means of a reinsurance contract, and pays dividends out dynamically from the surplus. Consider that the insurer is trying to balance risk control and dividend payout in terms of reinsurance and dividend distribution policies. Then the objective is set to make a dynamic choice of reinsurance policy and dividend distribution policy, which maximizes the sum of the expected discounted dividends up to ruin time and the expected discounted terminal value. There are two novelties in this paper. Firstly, we formulate the optimal control problem in terms of general reinsurance control policies. Each of the proportional reinsurance, the excess-of-loss reinsurance and combination of the two can be treated as a special case. It is shown that, under an expected premium principle, the dynamic excess-of-loss reinsurance is of optimal type within the general reinsurance contracts. Secondly, considering the excess-of-loss reinsurance policy and terminal value, we obtain the explicit expressions for the value function and optimal control policies by solving the HJB equation method. At the end of this paper numerical calculations are done to illustrate the influence of the terminal value on the value function and optimal policies as well. [Copyright &y& Elsevier]

Abstract: The symmetric cone complementarity problem (denoted by SCCP) is a class of equilibrium optimization problems, and it contains the standard linear/nonlinear complementarity problem (LCP/NCP), the second-order cone complementarity problem (SOCCP) and the semidefinite complementarity problem (SDCP) as special cases. In this paper, we present a regularized smoothing Newton algorithm for SCCP by making use of Euclidean Jordan algebraic technique. Under suitable conditions, we obtain global convergence and local quadratic convergence of the proposed algorithm. Some numerical results are reported in this paper, which confirm the good theoretical properties of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: Multi-choice programming solves some optimization problems where multiple information exists for a parameter. The aim of this paper is to select an appropriate parameter from a set of multiple choices, which optimizes the objective function. We consider a linear programming problem where the right hand side parameters are multi-choice in nature. In this paper, the multiple choices of a parameter are considered as functional values of an affine function at some non-negative integer nodes. An interpolating polynomial is formulated using functional values at non-negative integer nodes to take care of any multi-choice parameter. After establishing interpolating polynomials of all multi-choice parameters, a mathematical programming problem is formulated. The formulated problem is treated as a nonlinear programming problem involving mixed integer type variables. It can be solved by using standard nonlinear programming software. Finally, a numerical example is presented to illustrate the solution procedure. [Copyright &y& Elsevier]

Abstract: In this paper, the so-called -approximation approach is used to characterize solvability (in Pareto sense) of a nonlinear multiobjective programming problem with -invex functions with respect to the same function . In this method, an equivalent -approximated vector optimization problem is constructed by a modification of both the objective and the constraint functions in the original multiobjective programming problem at the given feasible point. Moreover, in order to find a (weak) Pareto optimal solution in the original multiobjective problem, it is sufficient to solve its associated -approximated vector optimization problem equivalent to the original multiobjective programming problem in the sense discussed in this paper. [Copyright &y& Elsevier]

Abstract: Products such as IC chips, computers and mobile phones can become out of date due to technological innovation. However, these outdated products can be remanufactured and resold to market. In this paper, a closed-loop supply chain inventory system with multi-manufacturing cycles and multi-remanufacturing cycles is analyzed using sequential and global optimization. In the case of sequential optimization, the decision is made initially by the down-stream player, then by the up-stream player. In the case of global optimization, the decision is made jointly by all the players. The supply chain includes a manufacturer, a retailer, a collector and a material supplier. This paper considers out of date as a type of deterioration and generalizes the following three cases: (i) single manufacturing cycle and single remanufacturing cycle, (ii) single manufacturing cycle and multi-remanufacturing cycles, and (iii) multi-manufacturing cycles and single remanufacturing cycle. The analytical results of this study show that a significant increase in the joint profit will result when the integrated policy is adopted. [Copyright &y& Elsevier]

Abstract: This paper presents an application of Artificial Neural Networks (ANN) to model the failure rate and estimate the optimal replacement time for the individual pipes in an urban water distribution system. The performances of the ANN are examined using a 14-year data set collected in a city in the north of France. The first part of the paper presents the collected data. The second part describes the construction and validation of six ANN models. After a discussion of the performances of these models, they are used for the prediction of water mains failure and the determination of the benefit index, which allows optimization of investment for the rehabilitation and maintenance of urban water mains. The spatial repartition of the risk of degradation is illustrated using a geographic information system, which constitutes an effective tool for the elaboration of strategies of rehabilitation of water distribution systems. [Copyright &y& Elsevier]

Abstract: Different persons often choose different contents in multi-document as summary. To optimize summarization, we will focus on the selection of content and seeking their valuable features. Statistical methods for automatic summarization are very important. In this paper, we research the correlation between the eigenvalue of content unit in the original document cluster and the probability of the content unit to be selected as a human summary based on a statistical method. When a Basic Element and word are considered as a content unit, we draw conclusions, in user-focus summarization. It is excellent that the BE is regarded as content unit granularity, and it is proved that the frequency eigenvalue of the BE is more suitable to embody content units’ weightiness than the TFIDF value. Moreover, the paper reveals that the given topic on user-focus summarization is helpful for the selection of content unit and quality of summarization. They often choose those content units as a summary in which the emerging frequency is relatively high in the sentences including the content unit of a given topic and neighboring sentences. Through researching potential behavioural modes about manual summary, we will put these effect factors of summarization quality into the process of content unit selection and summary generation to optimize automatic summarization. [Copyright &y& Elsevier]

Abstract: Part 1 of this paper proposed a methodology for the efficient determination of gradient information, when optimising a vehicle’s suspension characteristics for ride comfort and handling. The non-linear full vehicle model, and simplified models for gradient information has been discussed, and validated. In this paper, the simplified models presented in Part 1 are used for gradient information simulations. The convergence histories of the optimisation are compared to those obtained when only the full, computationally expensive, vehicle model is used. For illustration of the proposed gradient-based optimisation methodology, up to four design variables are considered in modelling the suspension characteristics. The proposed methodology is found to be an efficient alternative for the optimisation of the vehicle’s suspension characteristics. The undesirable effects associated with noise in the gradient information is effectively reduced, using the simplified models. Substantial benefits are achieved in terms of computational time needed to reach a solution. [Copyright &y& Elsevier]

Abstract: This paper studies the perturbation of dispersion-managed optical solitons in polarization-preserving optical fibers due to the perturbation terms. The types of pulses that are considered in this paper are super-Gaussian. The adiabatic parameter dynamics of such solitons are obtained in the presence of these local as well as non-local perturbation terms. [Copyright &y& Elsevier]

Abstract: Scalarization of the multiobjective programming problems with fuzzy coefficients using the embedding theorem and the concept of convex cone (ordering cone) is proposed in this paper. Since the set of all fuzzy numbers can be embedded into a normed space, this motivation naturally inspires us to invoke the scalarization techniques in vector optimization problems to evaluate the multiobjective programming problems with fuzzy coefficients. Two solution concepts are proposed in this paper by considering different convex cones. [Copyright &y& Elsevier]

Abstract: This paper is concerned with the study of necessary and sufficient optimality conditions for convex–concave generalized fractional disjunctive programming problems for which the decision set is the union of a family of convex sets. The Lagrangian function for such problems is defined and the Kuhn–Tucker Saddle and Stationary points are characterized. In addition, some important theorems related to the Kuhn–Tucker problem for saddle and stationary points are established. Moreover, a general dual problem is formulated and weak, strong and converse duality theorems are proved. Throughout the presented paper illustrative examples are given to clarify and implement the developed theory. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to design flexible decision making models in the distance metric optimization framework for problems including parameters which are represented by fuzzy numbers. Multi-criteria decision making methodologies based on distance functions involve the minimization of some form of distance from a desired point (ideal or specified by a decision marker). If it is assumed that the parameters of problem are fuzzy numbers, then it is natural to expect that this point also is so. Thus, in this paper it is supposed that the desired point is a vector of fuzzy numbers obtained from the imprecise information provided by the decision marker or, alternatively, composed by the individual optimum of each objective under consideration. The methodological proposal is an extension of the distance-based models and relies in the first instance, on the constructing of a fuzzy minimum distance obtained by solving linear programming problems. Secondly, it is shown that this fuzzy minimum distance possesses suitable features with respect to the quality and handling of information such that it can be incorporated in distance-based ordinary models which are necessary in order to determine an optimum decision. To illustrate the suitability of the method, a numerical example has been included. [Copyright &y& Elsevier]

Abstract: The topic of the paper is in the field of sculptured surface machining (SSM) on multi-axis NC machines. It presents novel results of investigation of tool-path generation for sculptured surface machining on multi-axis NC machines. The purpose of the paper is to develop an integral form of solution to the problem of optimal tool-path generation. The concept of time-minimal tool-paths is introduced, as well as the optimization problem being formulated analytically. The problem of optimization is subdivided into the following three partial sub-problems: (a) the problem of local tool-path generation; (b) the problem of regional tool-path generation, and finally, (c) the problem of global tool-path generation. The paper presents a closed-form solution to the first two sub-problems. A solution to the problem of optimal tool-path generation is given in the form of an integral equation. The obtained solution enables one to retain the optimal cutter configuration (i.e., the cutter position, and the cutter orientation), as well as the optimal instant direction of feed-rate at every cutter location-point (further, CC-point). [Copyright &y& Elsevier]

Abstract: This paper proposes a developed bi-objective two-stage stochastic mixed-integer linear programming model for redesigning a reliable warehouse network. In this model, the warehouses capacity which should be phased out, remained, or relocated to other warehouses, and the shipment quantity of the product between the network nodes with a consideration for two delivery lead times is to be determined. Unlike most previous works, considering two delivery lead times, the maximum capacity for each warehouse location, reliability and uncertainty are taken into account in this study. The proposed bi-objective model includes: (1) minimizing average costs including the sum of fixed, production, transportation, relocation and capacity extension costs; (2) maximizing the coverage percentage of customer demand delivered within the preferred delivery lead time. In order to obtain Pareto optimal solutions, a novel version of -constraint method (i.e., the augmented -constraint) is applied. A real industrial case is applied to validate and verify the developed warehouse redesigning network model under reliability concept. [Copyright &y& Elsevier]

Abstract: This paper which is based on neighboring optimal control investigates the application of an analytical feedback guidance scheme for the problems of low-thrust orbital transfer. Neighboring optimal control is one of the methods that has been proposed for the closed-loop optimal guidance. This method focuses on expanding the cost function to second order and examining the robustness of the system by the feedback law gains. At first, the open-loop analytical guidance policy is considered as the optimal thrust steering program that will transfer the vehicle from the inclined low earth orbits to the high earth orbits. Secondly, proper feedback optimal guidance laws are analytically obtained to maintain the trajectories around their optimum with assessed disturbances. The proposed guidance scheme is distinguished for two desired performance indices as minimum-time and minimum-effort. Finally performance indices are compared and the best policy is obtained regarding the robustness of the two performance indices against disturbances. [Copyright &y& Elsevier]

Abstract: To solve the problem caused by the lack of the structure of the nonlinearity in Wiener systems identification, we propose a new approach to identify the linear part and the nonlinear part of a Wiener system in sequence by using the limited information on the nonlinearity. The linear part of the system is identified firstly based on the limited information of the nonlinearity, such as sign information, monotonic information and then, we construct the internal signals to identify the structure and the parameters of the Wiener systems. A novel Particle Swarm Optimization–Rosenbrock (PSO–Rosenbrock) algorithm is developed and the numerical simulation shows that the identification procedure proposed in this paper is effective. [Copyright &y& Elsevier]

Abstract: Since van Groenigen and Stein (1998) proposed the SSA+MMSD (Spatial Simulated Annealing + Minimization of the Mean of Shortest Distances criterion) method, this method has found many applications in the optimization of sampling designs. However, it is computationally inefficient due to the complexity of this method itself. Initially in this paper, we analyze the computational complexity associated with this method from both SSA and MMSD aspects. And then, we propose some corresponding revisions (including the initial solution, perturbation rules, as well as the objective function) accordingly so as to reduce its computations. Finally, we evaluate the efficiency improvement via comparing some efficiency indexes of both original and modified methods (including the total perturbations needed, valid and better candidate designs generating rates of the perturbations, and the rate of objective function decline). Analysis and experimental results indicate that the modified method is much more efficient than the original one; in C implementations, the mean execution time needed for the modified method is only about 1/3 of that of the original. [Copyright &y& Elsevier]

Abstract: The conditioner suspension of the mower conditioner is the most important load-bearing part of the whole machine. In order to reduce the weight of the conditioner suspension, and ensure the sufficient stiffness of the equipment, this paper analyzed and classified the boundary conditions and load cases during the working time. Taking minima of the general flexibility of the conditioner suspension as the objective function, setting volume fraction and modal properties as constraints, optimization of the structural topology of the conditioner suspension design was carried out based on the variable density method. By analyzing the changes in stiffness and natural frequency before and after optimization, contrast to the results shown that the stiffness and dynamics of suspension have greatly improved after being optimized. It shown the effectiveness and feasibility of designing the conditioner suspension of the mower conditioner by using the topology optimization method, and it provides a reference method for technical improvements of similar equipment. It was indicated that the topology optimization method used for the design of conditioner suspension was effective and feasible, which provided a reference method for technical improvements of similar equipment. [Copyright &y& Elsevier]

Abstract: Realizing the internal flow visualization inside the drip irrigation emitter is the basis of optimizing the labyrinth path structure and promoting its anti-clogging capability. The Computational Fluid Dynamics (CFD) method has become an effective approach to research the water movement in the labyrinth path of the drip irrigation emitter. In this paper, the standard k- model and the Large Eddy Simulation method (LES) were applied to analyze the flow characteristic in the drip irrigation emitter. The results showed that the LES model was more effective to simulate the flow characteristics in the flow path of drip irrigation emitters. Based on this model, the energy dissipation mechanism was explained with the velocity and pressure values from local regions to the whole flow path, and the study of the flow path anti-clogging performance was carried out by way of analyzing the velocity distribution characteristics in different cross sections of the same structure unit. [Copyright &y& Elsevier]

Abstract: We consider constrained bi-objective optimization problems. One of the extant issues in this area is that of uniform sampling of the Pareto front. We utilize equispacing constraints on the vector of objective values, as discussed in a previous paper dealing with the unconstrained problem. We present a formulation and a dual formulation based on arc-length homotopy continuation and illustrate this method (using standard nonlinear programming tools) on some problems from the literature. We contrast the performance of our method with the results of three other algorithms, showing several orders of magnitude speed-up with respect to evolutionary algorithms, while simultaneously providing perfectly sampled fronts by construction. We then consider a large-scale application: the variational approach to mesh generation for partial differential equations in complex domains. Balancing multiple criteria leads to significantly improved mesh design. [Copyright &y& Elsevier]

Abstract: The new Spanish regulations allow wind farms to go to the market to sell the energy generated by their facilities. In the case of over- or under-supply, other producers must reduce or increase their production to resolve the so-called deviation, thereby incurring financial losses. Faced with this situation, wind farms have several options. In this paper we consider one promising method: the combined optimization of a pumped-storage hydro-plant and a wind park. First, we are going to present the mathematical modelling of the resulting problem. Second, we shall solve the optimization problem using Optimal Control techniques and finally we shall present several examples of the combined optimization and analyse which strategy is the best one possible. [Copyright &y& Elsevier]

Abstract: This paper considers an infinite buffer queueing system in which servers follow a multi-threshold vacation policy. With such a policy, at a service completion instant, if the number of customers in the system is less than a prefixed threshold value, part of servers together take a single vacation (or leave for a random amount of time doing other secondary job). At the vacation completion instant, they return to the system for serving the customers. Some practical production and inventory systems or call centers could be modeled as this Markovian queue with a multi-threshold vacation policy. Using the Markovian process model, we obtain the exact closed-form expression of rate matrix and the stationary distribution of the number of customers in the system. A cost model is developed to search the joint optimal values of the thresholds of vacation policy and service rate of each server, which minimizes the long-term average cost. Some numerical results are presented to illustrate the optimization procedures. [Copyright &y& Elsevier]

Abstract: In the paper, we consider generalized minimax fractional programming with support functions. We prove optimality conditions for such nonsmooth optimization problems and Schaible, Weir and Bector type weak, strong, converse and strictly converse duality results under -Type I assumptions imposed on the functions constituting considered optimization problems and their duals. In this way, we extend the results existing in the literature for a new class of nonconvex generalized minimax fractional programming problems with support functions. [Copyright &y& Elsevier]

Abstract: Cam-modulated linkages that can achieve superior kinematic performance as compared with pure linkages have been widely applied in a variety of automation machinery. The tolerance analysis and synthesis are thus quite essential in the design and manufacture of precision cam-modulated linkages. This paper presents comprehensive and systematic mathematical tools for dealing with the tolerance analysis and synthesis of cam-modulated linkages. Based on the concept of equivalent linkage and the derived correlation between the radial-dimension errors and the normal-direction errors of the cam profile, the sensitivity analysis method for equivalent linkages is introduced for analyzing the tolerances in cam-modulated linkages, whose output error equations can be derived through an analytical means. Then, by incorporating the sensitivity analysis method and the concept of design for manufacturing and assembly (DFMA), an optimization model for synthesizing the tolerances in cam-modulated linkages is developed. The objective of this optimization model is to maximize the manufacturability and assembility of a cam-modulated linkage while maintaining acceptable kinematic accuracy of its output motion. A practical case study of analyzing and synthesizing the tolerances in a cam-modulated linkage type pick-and-place device is then performed to illustrate the presented methods. It shows that the mathematical tools presented can be helpful to the design and manufacture of precision cam-modulated linkages. [Copyright &y& Elsevier]

Abstract: The fuzzy optimization problem is one of the prominent topics in the broad area of artificial intelligence. It is applicable in the field of non-linear fuzzy programming. Its application as well as practical realization can been seen in all the real world problems. In this paper a large scale non-linear fuzzy programming problem was solved by hybrid optimization techniques like Line Search (LS), Simulated Annealing (SA) and Pattern Search (PS). An industrial production planning problem with a cubic objective function, eight decision variables and 29 constraints was solved successfully using the LS–SA–PS hybrid optimization techniques. The computational results for the objective function with respect to vagueness factor and level of satisfaction has been provided in the form of 2D and 3D plots. The outcome is very promising and strongly suggests that the hybrid LS–SA–PS algorithm is very efficient and productive in solving the large scale non-linear fuzzy programming problem. [Copyright &y& Elsevier]

Abstract: An assembly line is a flow-oriented production system in which the productive units performing the operations, referred to as stations, are aligned in a serial manner. The simple assembly line balancing problem (SALBP) is a fundamental version of the general problem which has attracted attention of researchers and practitioners of operations research for almost half a century. With respect to the objective function, the SALBP is further classified into SALBP-1, SALBP-2, SALBP-E and finally SALBP-F. The types of SALBP may be complemented by a secondary objective which consists of smoothing station loads. This objective guarantees a better flow of material. Although there is a great deal of research addressing this problem, most of them consider smoothing station loads as a primary objective. In this paper, a differential evolution algorithm is developed to minimize workload smoothness index in SALBP-2. Also, the algorithm parameters are optimized using the Taguchi method. To validate the proposed algorithm, the results are compared with those of a published heuristic. This comparison indicates effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: Power loss reduction can be considered as one of the main purposes for distribution system operators, especially for recent non-governmental networks. Reconfiguration is an operation process widely used for this optimization by means of changing the status of switches in a distribution network. Some major points such as time-varying loads and the number of switchings, which are often neglected or not applied simultaneously in most previous studies, are the main motivation behind this study. In this paper, a new probabilistic approach is proposed to perform an optimal reconfiguration in order to reduce the total cost of operation, including the cost of switching and benefit of loss reduction. Considering time-varying loads, the proposed method can obtain an optimal balance between the number of switchings and the power loss. The effectiveness of the suggested method is demonstrated through several experiments and the results are compared with those of other reliable methods in several cases. [Copyright &y& Elsevier]

Abstract: In this paper, we discuss iterative methods for solving univariate nonlinear equations. First of all, we construct a family of methods with optimal convergence rate 4 based upon the Potra–Pták scheme and provide its error equation theoretically. Second, by using this derivative-involved family, a novel derivative-free family of two-step iterations without memory is derived. This derivative-free family agrees with the Kung–Traub conjecture (1974) for building optimal multi-point iterations without memory, since it is proven that each derivative-free method of the family reaches the convergence rate 4 requiring only three function evaluations per full iteration. Finally, numerical test problems are also provided to confirm the theoretical results. [Copyright &y& Elsevier]

Abstract: Many practical control systems can be modeled as switched linear systems with stabilizable and unstabilizable subsystems. In this paper, control synthesis for such a class of systems with actuator saturation is well investigated in the continuous-time and discrete-time cases. Based on the time-dependent switching signals and multiple Lyapunov function method, stabilization conditions are established in terms of LMIs. To obtain an attractive region as large as possible, the optimization problems are formulated. As the applications, the asynchronously switched control and intermittent control under actuator saturation are discussed. Numerical examples and simulations are presented to illustrate the feasibility and values of the proposed results. [Copyright &y& Elsevier]

Abstract: Let be a nonempty closed convex subset of a real Hilbert space . Let be a -contraction. Let be a nonexpansive mapping. Let be two strongly positive bounded linear operators. Consider the triple-hierarchical constrained optimization problem of finding a point such that where is the set of the solutions of the following variational inequality: where is the set of the solutions of the equilibrium problem of finding such that Assume . The purpose of this paper is the solving of the above triple-hierarchical constrained optimization problem. For this purpose, we first introduce an implicit double-net algorithm. Consequently, we prove that our algorithm converges hierarchically to some element in which solves the above triple-hierarchical constrained optimization problem. As a special case, we can find the minimum norm which solves the monotone variational inequality [Copyright &y& Elsevier]

Abstract: The most common use of thermal solar energy has been for water heating systems; this use has been commercialized in many countries in the world. This paper presents a model of a forced circulation solar water heating system for supplying a hot water at a certain temperature for an aquaculture system. The main components of the system are flat plate collector, storage tank, and a biogas auxiliary heater. The optimization problem is carried out using a genetic algorithm, which is one of the modern optimization techniques because of its evolutionary nature; it can handle any kind of objective function and constraint. Genetic algorithms don’t have mathematical requirements about the optimization problem; Also genetic algorithms are very effective in performing a global search (in probability), and provide a great flexibility. The optimal design of flat plate collector area using genetic algorithm is used to optimize the objective function considering the constraints required for the system. As the genetic algorithm is a discrete optimization tool, the number of variables in principle is unlimited. The economic analysis of such system is evaluated with the life cycle cost method. The collector area is equal to 63 m2, at this value the solar fraction reached 98% which is a very high value. Also, sensitivity analysis to solar radiation variation, air temperature variation, and interest rate has been carried out. [Copyright &y& Elsevier]

Abstract: The inertia of a Hermitian matrix is defined to be a triplet composed by the numbers of the positive, negative and zero eigenvalues of the matrix counted with multiplicities, respectively. If we take the inertia and rank of a Hermitian matrix as objective functions, then they are neither differentiable nor smooth. In this case, maximizing and minimizing the inertia and rank of a Hermitian matrix function could be regarded as a continuous-integer optimization problem. In this paper, we use some pure algebraic operations of matrices and their generalized inverses to derive explicit expansion formulas for calculating the global maximum and minimum ranks and inertias of the linear Hermitian matrix function subject to some rank and definiteness restrictions on the variable matrix . Various direct consequences of the formulas in characterizing algebraic properties of are also presented. In particular, solutions to a group of constrained optimization problems on the rank and inertia of a partially specified block Hermitian matrix are given. [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: The recently developed mobile network prefix provisioning (MNPP) enables a direct communication, i.e., Route Optimization (RO), between vehicular mobile routers (VMRs). However, without being secured, MNPP is vulnerable to various threats such as spoofing, replay, and wormhole attacks as well as location tracking. In this paper, possible threats to MNPP are identified with suggestions of security requirements. Then, three different security approaches (five different security mechanisms) for securing MNPP are introduced and compared. [Copyright &y& Elsevier]

Abstract: This paper deals with approximate solutions of general (that is, without any convexity assumption) multi-objective optimization problems (MOPs). In this text, by reviewing some standard scalarization techniques we are interested in finding the relationships between -(weakly, properly) efficient points of an MOP and -optimal solutions of the related scalarized problem. For this purpose, the relationships between and , for a single objective and multi-objective problems, respectively, are analyzed. In fact, necessary and/or sufficient conditions for approximating (weakly, properly) efficient points of a general MOP via approximate solutions of the scalarized problems are obtained. [Copyright &y& Elsevier]

Abstract: In this paper, assuming cooperative behavior of the decision makers, solution methods for decision making problems in hierarchical organizations under fuzzy random environments are considered. To deal with the formulated two-level linear programming problems involving fuzzy random variables, -level sets of fuzzy random variables are introduced and an -stochastic two-level linear programming problem is defined for guaranteeing the degree of realization of the problem. Taking into account the vagueness of judgments of decision makers, fuzzy goals are introduced and the -stochastic two-level linear programming problem is transformed into the problem to maximize the satisfaction degree for each fuzzy goal. Through the use of the fractile criterion optimization model, the transformed stochastic two-level programming problem can be reduced to a deterministic one. Interactive fuzzy programming to obtain a satisfactory solution for the decision maker at the upper level in consideration of the cooperative relation between decision makers is presented. It is shown that all of the problems to be solved in the proposed interactive fuzzy programming can be easily solved by the simplex method, the sequential quadratic programming or the combined use of the bisection method and the sequential quadratic programming. An illustrative numerical example is provided to demonstrate the feasibility and efficiency of the proposed method. [Copyright &y& Elsevier]

Abstract: A fairly reasonable result was obtained for nonlinear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the nonlinear problems to obtain a better output. This paper discusses the use of neuro-genetic hybrid technique to optimize the geological structure mapping which is known as seismic survey. It involves minimization of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimized results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method. [Copyright &y& Elsevier]

Abstract: In this paper, we consider -invex mathematical programming problems and show the efficiency of -invexity notion in proving optimality results for such nonconvex optimization problems. Further, we introduce a new exact penalty function method, called the exact -penalty function method and use it to solve nonconvex mathematical programming problems with -invex functions. In this method, the so-called exact -penalized optimization problem associated with the original optimization problem is constructed. The equivalence between the sets of optimal solutions of the original mathematical programming problem and of its associated -penalized optimization problem is established under suitable -invexity assumptions. Also lower bounds on the penalty parameter are given, for which above this result is true. It turns out that, for some nonconvex optimization problems, it is not possible to prove the same result for the classical penalty function method under invexity assumption. [Copyright &y& Elsevier]

Abstract: A model of an energy selective electron (ESE) heat engine with double resonances which filter electrons according to their total momentum is established in this paper. The optimal performance of the double resonance ESE heat engine is analyzed by using the theory of finite time thermodynamics (FTT). The performance of the double resonance ESE heat engine is compared with that of the single resonance device. It is shown that the double resonance device can generate more power but at the same time becomes less efficient. Performance comparisons are also performed between the total momentum filtered ESE heat engine in which the electrons are transmitted according to the total electron momentum in all the three dimensions and the conventional ESE heat engine where the electrons are filtered according to the momentum in the direction of transport only. It is found that the total momentum filtered ESE heat engine outperforms the conventionally filtered ESE heat engine on both power output and efficiency performance. Moreover, the effects of resonance width, energy spacing of two resonances and cold reservoir temperature on the performance of the total momentum filtered double resonance heat engine are analyzed in detail by numerical calculations. In practical operation of the double resonance ESE heat engine, the values of resonance width, energy spacing and cold reservoir temperature should be small in order for the device to obtain higher efficiency. [Copyright &y& Elsevier]

Abstract: Single-period problem (SPP) is a classical stochastic inventory model that has become very popular recently. In this research, we developed a SPP with fuzzy environment. The demand of each product is considered as LR-fuzzy variables (ranking fuzzy numbers based on the left and right deviation degrees), and multiple constraints (including service level, batch order, budget, space and upper limit for each order). The aim of this paper is to maximize the total expected profit under incremental discount strategy. Five hybrid intelligent algorithms based on fuzzy simulation (FS) and meta-heuristic methods are presented; they are bees colony optimization (BCO), harmony search (HS), particle swarm optimization (PSO), genetic algorithm (GA) and simulated annealing (SA). Three numerical examples are presented to illustrate the performance of the algorithms. Our study shows that the BCO-FS hybrid method performs better than the HS-FS, GA-FS, PSO-FS, and SA-FS hybrid methods. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a new iterative scheme for finding a common element of the set of fixed points of a nonexpansive mapping, the set of solutions to a variational inclusion and the set of solutions to a generalized equilibrium problem in a real Hilbert space. Furthermore, using our new iterative scheme, we prove strong convergence theorems which extend many recent important results. Finally, we apply our results to solve an optimization problem in a real Hilbert space. [Copyright &y& Elsevier]

Abstract: Based on the survey result of the nitrate content of the groundwater samples, this paper studies the variability of nitrate content of the groundwater in the Shandong province by using an optimised sampling model. Meanwhile, it also calculates the sample size required by the survey based on precision (relative error) and guarantying degree of probability. The calculation indicates that the coefficient of variation of nitrate content of groundwater in the Shandong province is 1.12, with the set survey relative error of 10%, confidence probability of 95%, and 482 required survey samples. By using pseudo-random numbers, the residential areas are selected from county level units as the survey sites, and the groundwater sources of the residential sites of villages and townships are taken as the survey targets. The first-order spatial distribution test on the residential sites is conducted by using the nearest neighbor index method, to analyse the spatial relation between the nearest neighbor sites. For the sampling sites which accord with the first-order spatial distribution, the multiple order spatial distribution test is conducted by using Ripley’s function, so as to meet the requirement of spatial random distribution of sample sites. [Copyright &y& Elsevier]

Abstract: As a kernel-based method, whether the selected kernel matches the data determines the performance of support vector machine. Conventional support vector classifiers are not suitable to the imbalanced learning tasks since they tend to classify the instances to the majority class which is the less important class. In this paper, we propose a weighted maximum margin criterion to optimize the data-dependent kernel, which makes the minority class more clustered in the induced feature space. We train support vector classification with the optimal kernel. The experimental results on nine benchmark data sets indicate the effectiveness of the proposed algorithm for imbalanced data classification problems. [Copyright &y& Elsevier]

Abstract: An investigation of a common of simple resource exchange processes between a firm and an economic subsystem with a generalized commodity transfer law [], in which the effects of the price elasticity of supply and demand are introduced, is presented in this paper. Optimal price configurations for minimum capital dissipation (which is analogous to the physical quality of entropy generation in thermodynamics and could be used to measure the irreversibility of economic processes) are obtained by applying optimal control theory. The optimal exchange strategy for the case with the linear commodity transfer law [] coincides with the exchange strategy of constant commodity flow operation, and the optimal exchange strategies for the cases with different commodity transfer laws are significantly different from each other. Numerical examples for the cases with some special commodity transfer laws are provided, and the optimal exchange strategies are compared with the common strategies of constant firm price and constant commodity flow operations. The results show that the differences among the capital dissipation of different exchange strategies decrease with the increase of value of , and the strategy of constant commodity flow operation is closer to the optimal strategy of minimum capital dissipation compared to that of constant firm price operation. The obtained results can provide some theoretical guidelines for the selection of optimal exchange strategies during real economical actions. [Copyright &y& Elsevier]

Abstract: In this paper, we have formulated the resource allocation optimization problem for expanding service and increasing reliability of a grid computing system. The formulated problem is a combinational optimization as well as an NP-hard problem. We firstly decompose this problem into a minimizing budget problem and a maximizing reliability problem. An approximate model is proposed to estimate the service reliability of a resource allocation design within a tolerable computation time. Secondly, we employ an ordinal optimization (OO) based approach to solve the maximizing reliability problem and a bisection method to solve the minimizing budget problem. The proposed OO based approach consists of two stages. A binary particle swarm optimization (BPSO) algorithm is employed in the first stage using the approximate model for fitness evaluation and selects a subset of good enough solutions. Then we proceed with the goal softening searching procedure in the second stage using more refined approximate models to search for a good enough solution from the subset obtained in the first stage. We have demonstrated the test results by the simulation on a 16-node and 25-link large grid computing system including two resource-managing nodes. We used 8 bisection iterations that consumed 20 minutes to obtain a good enough resource allocation design and the corresponding minimum budget. To test the optimality of the solutions obtained by our approach, we also solved the maximizing reliability problem using two competing methods, the GA and classical BPSO algorithm. The good enough solution obtained by the proposed approach is promising in the aspects of solution quality and computational efficiency. [Copyright &y& Elsevier]

Abstract: A new combinatorial optimization problem, the Bandpass problem, was defined in Bell and Babayev (2004) . Recently, this problem was investigated in detail in Babayev et al. (2009) . In this paper, we first present some new mathematical models of the Bandpass problem. Then related to this problem, we introduce a software called OrderMatic which is very useful for teaching permutations. [ABSTRACT FROM AUTHOR]

Abstract: In this paper an optimization-based methodology for the design of the operating equilibrium of a nonlinear dynamic system based on a measure of the extension of its domain of attraction is proposed. The approach consists in maximizing the radius of a ball in the state space contained in the region of negative definiteness of the time derivative of a quadratic Lyapunov function, using a two level optimization strategy. A deterministic global optimization problem is solved at the inner level to ensure proper estimation of the domain of attraction for each feasible realization of the design variables which are optimized at the outer level. In order to cope with the non-differentiable nature of the inner problem, a stochastic algorithm is applied to manipulate the design variables at the outer level. The methodology is applied to several examples to illustrate different aspects of the approach. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an optimization model for minimizing an objective function with single-term exponents subject to fuzzy relational equations specified in max-product composition is presented. The solution set of such a fuzzy relational equation is a non-convex set. First, we present some properties for the optimization problem under the assumptions of both negative and nonnegative exponents in the objective function. Second, an efficient procedure is developed to find an optimal solution without looking for all the potential minimal solutions and without using the value matrix. An example is provided to illustrate the procedure. [ABSTRACT FROM AUTHOR]