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With this note, we want to open a discussion on the duality theory for Interval Optimization. We begin by showing that some duality results Wu ( J Optim Theory Appl 144:615-628, ) are useless. [ABSTRACT FROM AUTHOR]

A memetic algorithm based on MOEA/D is presented to deal with the uncapacitated multiobjective examination timetabling problem in this paper. The examination timetabling problem is considered as a two-objective optimization problem in this paper, while it is modeled as a single-objective optimization problem generally. The framework of a multiobjective evolutionary algorithm with decomposition (MOEA/D) is first employed to guide the evolutionary process. Two special local search operators are designed to find better individuals. The proposed algorithm is tested on 11 benchmark examination timetabling instances. Experimental results prove that the proposed algorithm can produce a promising set of nondominated solutions for each examination timetabling instance. [ABSTRACT FROM AUTHOR]

The article presents a preface for the conference papers are being submitted to the conference on Variational Analysis and Optimization held on 18 May to 22 May 2015 in Limoges, France.

This paper studies $$\varepsilon $$ -efficiency in multiobjective optimization by using the so-called coradiant sets. Motivated by the nonlinear separation property for cones, a similar separation property for coradiant sets is investigated. A new notion, called Bishop-Phelps coradiant set is introduced and some appropriate properties of this set are studied. This paper also introduces the notions of $$\varepsilon $$ -dual and augmented $$\varepsilon $$ -dual for Bishop and Phelps coradiant sets. Using these notions, some scalarization and characterization properties for $$\varepsilon $$ -efficient and proper $$\varepsilon $$ -efficient points are proposed. [ABSTRACT FROM AUTHOR]

The critical data we share through computer network gets stolen by unethical means. This unethical way of accessing one's data without proper authentication becomes intrusion. To solve this issue, in this paper we propose a new network intrusion detection method, Multi-Class Classification Multiple Criteria Linear Programming (MCC-MCLP) model. MCLP is a mathematical classification technique that is used widely to solve real-time data mining problems. So far, the literature discusses only about binary classification MCLP. But in this paper we propose a Multi-Class Classification MCLP model. We use PSO for fine-tuning the parameters of MCC-MCLP. KDD CUP 99 data set is used for performance evaluation of the proposed method. Our MCC-MCLP method classifies the data better and helps in fine-tuning the parameters with the help of PSO. The results clearly show that the proposed model performs better in terms of detection rate, false alarm rate and accuracy. [ABSTRACT FROM AUTHOR]

We consider in this paper a parallel system consisting of $$\eta $$ identical components. Each component works independently of the others and has a Weibull distributed inter-failure time. When the system fails, we assume that the repair maintenance is imperfect according to the Arithmetic Reduction of Age models ( $$ARA_{m}$$ ) proposed by Doyen and Gaudoin. The purpose of this paper is to generate a simulated failure data of the whole system in order to forecast the behavior of the failure process. Besides, we estimate the maintenance efficiency and the reliability parameters of an imperfect repair following $$ARA_{m}$$ models using maximum likelihood estimation method. Our method is tested with several data sets available from related sources. The real data set corresponds to the time between failures of a compressor which is tested by Likelihood Ratio Test (LR). An analysis of the importance and the effect of the memory order of imperfect repair classes ( $$ARA_{m}$$ ) will be discussed using LR test. [ABSTRACT FROM AUTHOR]

This paper focuses on the design and implementation of green heterogeneous scheduling algorithm based on the theory and technology hotspot of energy saving and emission reduction in cloud computing, since intelligent scheduling algorithms often have defects such as insufficient dynamic optimization power or poor parallel framework design. Following that, a distributed swarm intelligence optimization algorithm for greener heterogeneous scheduling, is proposed, including ⓵ an optimal blending pattern of particle swarm intelligence and nonlinear geometric Brownian motion, based on the related physical sciences and stochastic mathematical analyses, and ⓶ a parallel fusion model that is suitable for the management server processor hybrid system, i.e., coarse-grained parallelism between nodes and CPU/GPU master-slave in a single node. Then, a large number of experimental results are given, whose evaluation indicators can be divided into two categories: static and dynamic optimization performance. Compared with most newly published scheduling algorithms, there are significant advantages of the proposed algorithm on the dynamic optimization performance for consistent or semiconsistent and large inconsistent scheduling instances, although with the lower improvement for the small inconsistent instances. [ABSTRACT FROM AUTHOR]

A synergistic perspective to selecting the right project portfolio is the key to realizing strategic organizational objectives. Based on the theory of the composite system, this paper identifies the factors that affect the science of project portfolio selection (PPS) and investigates the synergistic relationship among the organizational strategic objective subsystem, the project portfolio subsystem and the environment subsystem, which collectively constitute the composite system of project portfolio selection. Utilizing the concept of order parameters, an order parameter system is built for each subsystem to reflect the synergistic relationships in the composite system of PPS. The PPS model is constructed based on the degree of synergy in the composite system by describing the order degree of the subsystem and the synergy degree of the composite system, which helps to quantify the synergistic relationships among these subsystems. The effectiveness and feasibility of the proposed model are demonstrated and validated by a case study. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the possibility of covariate shift adaptation in off-policy temporal difference learning for the class of fast mixing Markov chains. Off-policy evaluation algorithms in reinforcement learning such as off-policy least squares temporal difference (LSTD) deal with the problem of evaluating a target policy different from the sampling (or behavior) policy. Off-policy LSTD may result in poor quality of solution due to the shift among stationary distributions of the chains induced by following the target and behavior policies. Previous works—least squares temporal difference–distribution optimization (LSTD-DO) and the recently proposed emphatic TD—each tackles this problem by mapping distribution of states collected following the behavior policy (i.e. off-policy samples) to a new different distribution with better LSTD solution. In this paper, we consider off-policy LSTD in the class of target Markov chains with fast mixing time. For this class of problems, we propose adapting the distribution of off-policy state samples to the distribution of state samples after transition model adaptation, using a regularized covariate shift adaptation algorithm called least squares importance fitting. Empirical evaluations of our proposed approach on two classes of fast mixing chains show promising results in comparison with LSTD-DO and unadapted off-policy LSTD as the number of samples increases. [ABSTRACT FROM AUTHOR]

This paper studies the optimality in the problem of cyclic harvesting of a resource distributed on a circle with a certain prescribed density. The velocity ofmotion of the collecting device and the fraction of the resource harvested at a given time play the role of control. The problem is to choose a control maximizing a given quality functional. The paper presents the maximum principle for this (infinite-dimensional) problem. The maximum principle can be written as two inequalities which can be conveniently verified. The class of problems with a concave profit function is solved completely. At the end of the paper, several examples are considered to illustrate the developed technique. [ABSTRACT FROM AUTHOR]

A composite control law for stabilizing a small-scale helicopter during hover flight mode is proposed in this paper. The proposed method is developed by combining the sliding mode control (SMC) and a nonlinear control law. Parameters of the proposed controller are optimized using particle swarm optimization technique. The SMC ensures robustness under disturbances and parameter uncertainties, while the nonlinear control law improves the transient response of the closed-loop system. Adequacy of the combination of the sliding mode controller and the nonlinear control law is validated using mathematical analysis and simulation experiments. The results illustrate an efficient execution of the proposed controller to stabilize the system by reducing the deviations from the trim state and alleviating the effect of disturbance in the closed-loop response. [ABSTRACT FROM AUTHOR]

In multiple-objective optimization literature, a properly efficient solution has been interpreted as a point in which the trade-offs between all objectives are bounded. In this paper, it is shown that this boundedness does not necessarily hold for problems with three or more objective functions. It is possible that in a properly efficient solution the trade-offs between some objectives are unbounded. To overcome this, in this paper strongly proper efficient solutions are introduced, in which the trade-offs between all objectives are bounded. This notion is defined in different senses, and the relationships between them are investigated. In addition to theoretical discussions, some clarifying examples are given. [ABSTRACT FROM AUTHOR]

Robust analysis is important for designing and analyzing algorithms for global optimization. In this paper, we introduce a new concept, robust constant, to quantitatively characterize the robustness of measurable sets and functions. The new concept is consistent to the theoretical robustness presented in literatures. This paper shows that, from the respects of convergence theory and numerical computational cost, robust constant is valuable significantly for analyzing random global search methods for unconstrained global optimization. [ABSTRACT FROM AUTHOR]

In this paper, a sequential approach, recently addressed by Mueller-Gritschneder et al. [SIAM J Optim 20(2):915-934, ] is studied. After a brief review, some weaknesses of the mentioned approach are pointed out. These weaknesses are due to a redundant assumption and the inability to generate the entire Pareto front. We present two improvements for the mentioned approach, firstly by eliminating the assumption of equality between the Pareto front and the weak Pareto front and secondly by presenting a new definition of the boundary of the Pareto front and establishing some theoretical results about it. [ABSTRACT FROM AUTHOR]

This paper presents new approximation bounds for trilinear and biquadratic optimization problems over nonconvex constraints. We first consider the partial semidefinite relaxation of the original problem, and show that there is a bounded approximation solution to it. This will be achieved by determining the diameters of certain convex bodies. We then show that there is also a bounded approximation solution to the original problem via extracting the approximation solution of its semidefinite relaxation. Under some conditions, the approximation bounds obtained in this paper improve those in the literature. [ABSTRACT FROM AUTHOR]

Effective dimension, an indicator for the difficulty of high-dimensional integration, describes whether a function can be well approximated by low-dimensional terms or sums of low-order terms. Some problems in option pricing are believed to have low effective dimensions, which help explain the success of quasi-Monte Carlo (QMC) methods recently observed in financial engineering. This paper provides a way of studying the structure of effective dimensions by finding a proper space the function of interest belongs to and then determining the effective dimension of that space. To this end, we extend the definitions of effective dimensions to weighted function spaces with product-order-dependent weights and give bounds on norms and variances. Furthermore, we show that the proposed method is applicable to functions arising in option pricing and consequently offers some hints on the performance of QMC methods. [ABSTRACT FROM AUTHOR]

In this paper we consider large fixed point problems and solution with proximal algorithms. We show that for linear problems there is a close connection between proximal iterations, which are prominent in numerical analysis and optimization, and multistep methods of the temporal difference type such as TD(λ), LSTD(λ), and LSPE(λ), which are central in simulation-based exact and approximate dynamic programming. One benefit of this connection is a new and simple way to accelerate the standard proximal algorithm by extrapolation towards a multistep iteration, which generically has a faster convergence rate. Another benefit is the potential for integration into the proximal algorithmic context of several new ideas that have emerged in the approximate dynamic programming context, including simulation-based implementations. Conversely, the analytical and algorithmic insights from proximal algorithms can be brought to bear on the analysis and the enhancement of temporal difference methods. We also generalize our linear case result to nonlinear problems that involve a contractive mapping, thus providing guaranteed and potentially substantial acceleration of the proximal and forward backward splitting algorithms at no extra cost. Moreover, under certain monotonicity assumptions, we extend the connection with temporal difference methods to nonlinear problems through a linearization approach. [ABSTRACT FROM AUTHOR]

In this paper we discuss scenario reduction methods for risk-averse stochastic optimization problems. Scenario reduction techniques have received some attention in the literature and are used by practitioners, as such methods allow for an approximation of the random variables in the problem with a moderate number of scenarios, which in turn make the optimization problem easier to solve. The majority of works for scenario reduction are designed for classical risk-neutral stochastic optimization problems; however, it is intuitive that in the risk-averse case one is more concerned with scenarios that correspond to high cost. By building upon the notion of effective scenarios recently introduced in the literature, we formalize that intuitive idea and propose a scenario reduction technique for stochastic optimization problems where the objective function is a Conditional Value-at-Risk. Numerical results presented with problems from the literature illustrate the performance of the method and indicate the cases where we expect it to perform well. [ABSTRACT FROM AUTHOR]

We study the uniqueness and explicit derivation of the relaxed optimal solutions, corresponding to the minimization of weighted sum of potential energies for a mixture of two isotropic conductive materials on an annulus. Recently, it has been shown by Burazin and Vrdoljak that even for multiple-state problems, if the domain is spherically symmetric, then the proper relaxation of the problem by the homogenization method is equivalent to a simpler relaxed problem, stated only in terms of local proportions of given materials. This enabled explicit calculation of a solution on a ball, while problems on an annulus appeared to be more tedious. In this paper, we discuss the uniqueness of a solution of this simpler relaxed problem, when the domain is an annulus and we use the necessary and sufficient conditions of optimality to present a method for explicit calculation of the unique solution of this simpler proper relaxation, which is demonstrated on an example. [ABSTRACT FROM AUTHOR]

Bayesian optimization has become a widely used tool in the optimization and machine learning communities. It is suitable to problems as simulation/optimization and/or with an objective function computationally expensive to evaluate. Bayesian optimization is based on a surrogate probabilistic model of the objective whose mean and variance are sequentially updated using the observations and an “acquisition” function based on the model, which sets the next observation at the most “promising” point. The most used surrogate model is the Gaussian Process which is the basis of well-known Kriging algorithms. In this paper, the authors consider the pump scheduling optimization problem in a Water Distribution Network with both ON/OFF and variable speed pumps. In a global optimization model, accounting for time patterns of demand and energy price allows significant cost savings. Nonlinearities, and binary decisions in the case of ON/OFF pumps, make pump scheduling optimization computationally challenging, even for small Water Distribution Networks. The well-known EPANET simulator is used to compute the energy cost associated to a pump schedule and to verify that hydraulic constraints are not violated and demand is met. Two Bayesian Optimization approaches are proposed in this paper, where the surrogate model is based on a Gaussian Process and a Random Forest, respectively. Both approaches are tested with different acquisition functions on a set of test functions, a benchmark Water Distribution Network from the literature and a large-scale real-life Water Distribution Network in Milan, Italy. [ABSTRACT FROM AUTHOR]

Time consistency is one of desirable properties for any solution of a cooperative dynamic game. If a solution is time-consistent, the players do not need to break a cooperative agreement. In this paper, we consider the core as the solution and establish conditions for its strong time consistency. When the core is not strongly time-consistent, we show that in some cases its elements can be yielded using a strongly time-consistent imputation distribution procedure. An explicit form of the procedure is given. [ABSTRACT FROM AUTHOR]

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

Hub locations are NP-hard problems used in transportation systems. In this paper, we focus on a single-allocation hub covering location problem considering a queue model in which the number of servers is a decision variable. We propose a model enhanced with a queue estimation component to determine the number and location of hubs and the number of servers in each hub, and to allocate non-hub to hub nodes according to network costs, including fixed costs for establishing each hub and server, transportation costs, and waiting costs. Moreover, we consider the capacity for a queuing system in any hub node. In addition, we present a metaheuristic algorithm based on particle swarm optimization as a solution method. To evaluate the quality of the results obtained by the proposed algorithm, we establish a tight lower bound for the proposed model. Genetic programming is used for lower bound calculation in the proposed method. The results showed better performance of the proposed lower bound compared to a lower bound obtained by a relaxed model. Finally, the computational results confirm that the proposed solution algorithm performs well in optimizing the model with a minimum gap from the calculated lower bound. [ABSTRACT FROM AUTHOR]

This paper proposes a new portfolio selection model, where the goal is to maximize the expected portfolio return and meanwhile minimize the risks of all the assets. The average return of every asset is considered as an interval number, and the risk of every asset is treated by probabilistic measure. An algorithm for solving the portfolio selection problem is given. Then a Pareto-maximal solution could be obtained under order relations between interval numbers. Finally, the empirical analysis is presented to show the feasibility and robustness of the model. [ABSTRACT FROM AUTHOR]

This paper is concerned with the lag quasi-synchronization for memristive neural networks (MNNs) with switching jumps mismatch. The inherent characteristic of MNNs is fully taken into account. Based on Lyapunov-Krasovskii functional and differential inclusions theory, intermittent control approach is utilized to realize the exponential lag quasi-synchronization of the considered model. The error level is closely related to the switching jumps. In addition, a simple design procedure of controller is presented to ensure that the synchronization error between the master system and the slave system converges to a predetermined level. Two numerical examples are offered to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Cuckoo search (CS) is a recently developed meta-heuristic algorithm, which has shown good performance on many continuous optimization problems. In this paper, we present a new CS algorithm, called NCS, for solving flow shop scheduling problems (FSSP). The NCS hybridizes four strategies: (1) The FSSP is a typical NP-hard problem with discrete characteristics. To deal with the discrete variables, the smallest position value (SPV) rule is employed to convert continuous solutions into discrete job permutations; (2) To generate high quality initial solutions, a new method based on the Nawaz-Enscore-Ham (NEH) heuristic is used for population initialization; (3) A modified generalized opposition-based learning (GOBL) is utilized to accelerate the convergence speed; and (4) To enhance the exploitation, a local search strategy is proposed. Experimental study is conducted on a set of Taillard's benchmark instances. Results show that NCS obtains better performance than the standard CS and some other meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Image matting is a fundamental operator in image editing and has significant influence on video production. This paper explores sampling-based image matting technology, with the aim to improve the accuracy of matting result. The result of sampling-based image matting technology is determined by the selected samples. Every undetermined pixel needs both a foreground and background pixel to estimate whether the undetermined one is in the foreground region of the image. These foreground pixels and background pixels are sampled from known regions, which form sample pairs. High-quality sample pairs can improve the accuracy of matting results. Therefore, how to search for the best sample pairs for all undetermined pixels is a key optimization problem of sampling-based image matting technology, termed 'sample optimization problem.' In this paper, in order to improve the efficiency of searching for high-quality sample pairs, we propose a cooperative coevolution differential evolution (DE) algorithm in solution to this optimization problem. Strong-correlate pixels are divided into a group to cooperatively search for the best sample pairs. In order to avoid premature convergence of DE algorithm, a scattered strategy is used to keep the diversity of population. Besides, a simple but effective evaluation function is proposed to distinguish the quality of various candidate solutions. The existing optimization method, original DE algorithm and a popular evolution algorithm are used for comparison. The experimental results demonstrate that the proposed cooperative coevolution DE algorithm can search for higher-quality sample pairs and improve the accuracy of sampling-based image matting. [ABSTRACT FROM AUTHOR]

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

The tolerance of an element of a combinatorial optimization problem with respect to its optimal solution is the maximum change of the cost of the element while preserving the optimality of the given optimal solution and keeping all other input data unchanged. Tolerances play an important role in the design of exact and approximation algorithms, but the computation of tolerances requires additional computational time. In this paper, we concentrate on combinatorial optimization problems for which the computation of all tolerances and an optimal solution have almost the same computational complexity as of finding an optimal solution only. We summarize efficient computational methods for computing tolerances for these problems and determine their time complexity experimentally. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new predictor-corrector interior-point algorithm for linear programming based on a wide neighbourhood. In each iteration, the algorithm computes the Ai-Zhang's predictor direction (SIAM J. Optim. 16(2):400-417, ) and a new corrector direction, in an attempt to improve its performance. We drive that the duality gap reduces in both predictor and corrector steps. Moreover, we also prove that the complexity of the algorithm coincides with the best iteration bound for small neighbourhood algorithms. Finally, some numerical experiments are provided which reveal capability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper a hybrid algorithm for the redundancy allocation problem is presented. The problem is the allocation of redundant components within series-parallel systems. We present an algorithm that deals with the classical formulation, where at least one component per subsystem must be included in the final configuration, as well as the $$k$$ -out-of- $$n$$ formulation, in which at least $$k$$ components per subsystem must be included in the final network configuration. We propose a three-phase scheme in which the cross entropy method, the corridor method and a dynamic programming-based scheme are effectively intertwined. Computational results on well-known benchmark instances as well as on randomly generated large scale instances are presented, proving the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The focus of this paper is on devising designer-friendly hull-form variations coupled with optimization algorithms. Hull-form variations are carried out through parametric modification functions. Two kinds of representative optimization algorithms are considered here. One is the well-known sequential quadratic programming which is the derivative based. The other is particle swarm optimization which is the derivative free. The results applying these two algorithms to typical hull-form optimization problems are discussed in the paper. The technique using the parametric modification functions has been developed for modifying the ship's geometry according to the widely recognized naval architect's design practice. An original geometry can be easily deformed through the change of the variables of the modification functions; and useful information about the effect of the parameters is immediately obtained. Moreover, the variables of the modification functions can be considered as the design variables in the formulation of the optimization problem. For the performance prediction of the hull form, WAVIS version 1.3 is used for the potential-flow and RANS solver. Computational results for both single- and multi-objective problems are presented. [ABSTRACT FROM AUTHOR]

This paper presents a novel optimization framework for classic windows using a bio-inspired methodology, the firefly algorithm. Classic windows are widely used as sidelobe control for signals undergoing discrete Fourier transform processing. In general, the lower sidelobes of nonrectangular windows have been achieved at the cost of broadening of the mainlobe width (MW). Here, the novel optimized windows allow a better peak sidelobe ratio without MW broadening. The simulation results show the effectiveness of the proposal for sidelobe reduction for the purpose of radar range imaging while preserving resolution. [ABSTRACT FROM AUTHOR]

In this paper, we present the first full regions-and-effects inference algorithm for explicitly parallel fork-join programs. We infer annotations equivalent to those in Deterministic Parallel Java (DPJ) for type-safe C++ programs. We chose the DPJ annotations because they give the strongest safety guarantees of any existing concurrency-checking approach we know of, static or dynamic, and it is also the most expressive static checking system we know of that gives strong safety guarantees. This expressiveness, however, makes manual annotation difficult and tedious, which motivates the need for automatic inference, but it also makes the inference problem very challenging: the code may use region polymorphism, imperative updates with complex aliasing, arbitrary recursion, hierarchical region specifications, and wildcard elements to describe potentially infinite sets of regions. We express the inference as a constraint satisfaction problem and develop, implement, and evaluate an algorithm for solving it. The region and effect annotations inferred by the algorithm constitute a checkable proof of safe parallelism, and it can be recorded both for documentation and for fast and modular safety checking. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDSs) are the fundamental parts of any network security infrastructure given their role as layers of defense against hackers. However, IDSs generate frequent instances of false alerts and miss a lot of real attacks that block the normal traffic and threaten the network security. It is not possible to identify a missed intrusion using one IDS, so multiple IDSs are used since they respond differently to the same packet trace and produce different alert sets. Actually, an attack missed by an IDS can be detected by another while inspecting the same network traffic. In this paper, we propose a multi-objective optimization process that aims to identify false negatives and false positives from the sets of alerts generated by multiple IDSs. In the first step, low-level alerts are clustered into meta-alerts to give a better understanding of the output of each IDS. Then, a filtering step is performed having as input the distinct meta-alert sets generated by different IDSs and as output the set of potential false negatives collecting the meta-alerts detected by some IDSs and missed by others. Meta-alerts generated by all IDSs are discarded since they cannot be missed attacks. Later, a clustering inter-IDS step is performed to group together similar meta-alerts generated by different IDSs. This clustering step aims to avoid the redundancy between the alerts generated by more than one IDS. Finally, a binary multi-objective optimization problem is used to detect false negatives and false positives. The proposed method is evaluated using a real network traffic, DARPA 1999 and NSL-KDD data sets. Experimental results show that the proposed process outperforms concurrent methods for false negatives and false positives detection. [ABSTRACT FROM AUTHOR]

In this paper, we study an optimal portfolio, consumption-leisure and retirement choice problem for an infinitely lived economic agent with a CES utility function. Using the dynamic programming method, we obtain the value function and optimal investment, consumption, leisure, and retirement strategies in analytic form. Numerically we observe that the threshold retirement wealth level is an increasing function with respect to the elasticity of substitution. [ABSTRACT FROM AUTHOR]

Creative thinking, which plays an essential role in the progress of human society, has an outstanding problem-solving ability. This paper presents a novel creativity-oriented optimization model (COOM) and algorithm (COOA) inspired by the creative thinking process. At first, COOM is constructed by simplifying the procedure of creative thinking while retaining its main characteristics. And then, COOA is presented for continuous optimization problems. It is a realization of COOM. As a new nature-inspired algorithm, COOA is different from other similar algorithms in terms of the basic principle, mathematical formalization and properties. Features of the COOM and the corresponding algorithm include a powerful processing ability for the complex problems, namely high-dimensional, highly nonlinear and random problems. The proposed approach also has the advantages in terms of the higher intelligence, effectiveness, parallelism and lower computation complexity. The properties of COOA, including convergence and parallelism, are discussed in detail. The numerous simulations on the CEC-2013 real-parameter optimization benchmark functions' problems have shown the effectiveness and parallelism of the proposed approach. [ABSTRACT FROM AUTHOR]

In the present paper, we consider the inequality constrained vector problem with continuously Fréchet differentiable objective functions and constraints. We obtain second-order necessary optimality conditions of Karush-Kuhn-Tucker type for weak efficiency. A new second-order constraint qualification of Zangwill type is introduced. It is applied in the optimality conditions. Some connections with other constraint qualifications are established. [ABSTRACT FROM AUTHOR]

This paper focuses on homology computation over 'cellular' structures that allow multi-incidence between cells. We deal here with combinatorial maps, more precisely chains of maps and subclasses such as maps and generalized maps. Homology computation on such structures is usually achieved by computing simplicial homology on a simplicial analog. But such an approach is computationally expensive because it requires computing this simplicial analog and performing the homology computation on a structure containing many more cells (simplices) than the initial one. Our work aims at providing a way to compute homologies directly on a cellular structure. This is done through the computation of incidence numbers. Roughly speaking, if two cells are incident, then their incidence number characterizes how they are attached. Having these numbers naturally leads to the definition of a boundary operator, which induces a homology. Hence, we propose a boundary operator for chains of maps and provide optimization for maps and generalized maps. It is proved that, under specific conditions, the homology of a combinatorial map as defined in the paper is equivalent to the homology of its simplicial analogue. [ABSTRACT FROM AUTHOR]

The microstrip patch antenna that have more than two feed points or lines is known as differential fed microstrip patch antenna. In this paper, firefly algorithm (FA) and artificial neural network (ANN) has been applied to a 'Flower' shaped differentially fed microstrip patch antenna for optimizing the return loss. This new optimization method is much faster than conventional optimization methods. FA is the new nature-inspired algorithm which is based on the flashing behavior of fireflies in the summer sky in the hot and humid regions. To validate the ability of FA, the results obtained from FA are compared with that obtained using genetic algorithm (GA) and ANN, and it has been observed that FA performs better as compared to GA. [ABSTRACT FROM AUTHOR]

This paper presents a shape sensitivity analysis and what-if study for two-dimensional multi-scale crack propagation problems using bridging scale decomposition. The sensitivity equations are derived in a continuum setting using direct differentiation method based on a continuum variational formulation of the bridging scale. Due to the fact that the crack propagation speed in an atomistic simulation is discrete in design, and cannot be formulated as a continuous function of shape design variables, we propose a hybrid method that combines analytical sensitivity analysis with finite difference approach. The finite difference part of the sensitivity analysis is only intended for calculating the sensitivity of crack growth speed based on the analytically obtained sensitivity coefficients of structural responses. The theoretical development on sensitivity formulation in this paper extends the application of the method to irregular-shaped finite elements and general design velocity fields. Furthermore, we evaluate and compare several performance measures that quantify crack propagation speed based on crack tip locations for sensitivity analysis and ultimately for structural optimization. A two-dimensional beam example is used to verify the accuracy of the proposed sensitivity approach. It is also demonstrated through a what-if study that with an adequate performance measure, the impact of macroscopic shape changes on microscopic crack propagation speed can be accurately predicted. [ABSTRACT FROM AUTHOR]

An accurate mathematical model has a vital role in controlling and synchronization of chaotic systems. But generally in real-world problems, parameters are mixed with mismatches and distortions. This paper proposes two simple but effective estimation methods to detect the unknown parameters of chaotic models. These methods focus on improving the performance of a recently proposed evolutionary algorithm called backtracking search optimization algorithm (BSA). In this research firstly, a new operator to generate initial trial population is proposed. Then a group search ability is provided for the BSA by proposing a shuffled BSA (SBSA). Grouping population into several sets can provide a better exploration of search space, and an independent local search of each group increases exploitation ability of the BSA. Also new proposed operator to generate initial trial population, by providing a deep search, increases considerably the quality of solutions. The superiority of the proposed algorithms is investigated on parameter identification of 10 typical chaotic systems. Practical experiences and nonparametric analysis of obtained results show that both of the proposed ideas to improve performance of original BSA are very effective and robust so that the BSA by aforementioned ideas produces similar and promising results over repeated runs. A considerably better performance of proposed algorithms based on average of objective functions demonstrates that the proposed ideas can evolve robustness and consistence of BSA. A comparison of the proposed algorithms in this study with respect to other algorithms reported in the literature confirms a considerably better performance of proposed algorithms. [ABSTRACT FROM AUTHOR]

The paper gives an overview of a number of mathematical models and problems on planning anti-terrorist and special actions. These are problems of monitoring a territory, protecting critical infrastructure (described by optimization models), interdicting transport and information networks. It is shown that many territory control problems can be reduced to well-known optimization problems on graphs, shortest paths search, and minimal covering on graphs. The problems of protecting critical infrastructure and interdicting networks are reduced to stochastic minimax game problems. [ABSTRACT FROM AUTHOR]

Generalizing both mixed-integer linear optimization and convex optimization, mixed-integer convex optimization possesses broad modeling power but has seen relatively few advances in general-purpose solvers in recent years. In this paper, we intend to provide a broadly accessible introduction to our recent work in developing algorithms and software for this problem class. Our approach is based on constructing polyhedral outer approximations of the convex constraints, resulting in a global solution by solving a finite number of mixed-integer linear and continuous convex subproblems. The key advance we present is to strengthen the polyhedral approximations by constructing them in a higher-dimensional space. In order to automate this extended formulation we rely on the algebraic modeling technique of disciplined convex programming (DCP), and for generality and ease of implementation we use conic representations of the convex constraints. Although our framework requires a manual translation of existing models into DCP form, after performing this transformation on the MINLPLIB2 benchmark library we were able to solve a number of unsolved instances and on many other instances achieve superior performance compared with state-of-the-art solvers like Bonmin, SCIP, and Artelys Knitro. [ABSTRACT FROM AUTHOR]

A chance constrained optimization problem over a finite distribution involves a set of scenario constraints from which a small subset can be violated. We consider the setting where all scenario constraints are mixed-integer convex. Existing works typically consider a mixed integer nonlinear programming (MINLP) formulation of this problem by introducing binary variables to indicate which constraint systems are to be satisfied or violated. A variety of cutting plane approaches for this MINLP formulation have been developed. In this paper we consider a family of cuts in the original space rather than those in the extended space of the MINLP reformulation. These cuts, known as quantile cuts, can be viewed as a projection of the well known family of mixing inequalities for the MINLP reformulation onto the original problem space. We show that the closure of the infinite family of all quantile cuts has a finite description. An important corollary of this result is that for linear chance constrained problems the quantile closure is polyhedral. We further show that a recursive application of quantile closure operations recovers the convex hull of the nonconvex chance constrained set in the limit, and in the pure integer setting the convergence is finite. We show that separation of quantile cuts is in general NP-hard, develop a heuristic separation method, and demonstrate its effectiveness through a computational study. We also study an approximation of the quantile closure and propose a generalization by grouping scenarios. [ABSTRACT FROM AUTHOR]

The zero propellant maneuver (ZPM) is an advanced space station, large angle attitude maneuver technique, using only control momentum gyroscopes (CMGs). Path planning is the key to success, and this paper studies the associated multi-objective optimization problem. Three types of maneuver optimal control problem are formulated: (i) momentum-optimal, (ii) time-optimal, and (iii) energy-optimal. A sensitivity analysis approach is used to study the Pareto optimal front and allows the tradeoffs between the performance indices to be investigated. For example, it is proved that the minimum peak momentum decreases as the maneuver time increases, and the minimum maneuver energy decreases if a larger momentum is available from the CMGs. The analysis is verified and complemented by the numerical computations. Among the three types of ZPM paths, the momentum-optimal solution and the time-optimal solution generally possess the same structure, and they are singular. The energy-optimal solution saves significant energy, while generally maintaining a smooth control profile. [ABSTRACT FROM AUTHOR]

In this paper, we consider a dynamic optimization problem involving a general switched system that evolves by switching between several subsystems of nonlinear delay-differential equations. The optimization variables in this system consist of: (1) the times at which the subsystem switches occur; and (2) a set of system parameters that influence the subsystem dynamics. We first establish the existence of the partial derivatives of the system state with respect to both the switching times and the system parameters. Then, on the basis of this result, we show that the gradient of the cost function can be computed by solving the state system forward in time followed by a costate system backward in time. This gradient computation procedure can be combined with any gradient-based optimization method to determine the optimal switching times and parameters. We propose an effective optimization algorithm based on this idea. Finally, we consider three numerical examples, one involving the 1,3-propanediol fed-batch production process, to illustrate the effectiveness and applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, proper optimality concepts in vector optimization with variable ordering structures are introduced for the first time and characterization results via scalarizations are given. New type of scalarizing functionals are presented and their properties are discussed. The scalarization approach suggested in the paper does not require convexity and boundedness conditions. [ABSTRACT FROM AUTHOR]

Many evolutionary multi-objective optimization (EMO) methodologies have been proposed and performed very well on finding a representative set of Pareto-optimal solutions, but this advantage will be weakened with the increasing number of objectives. And in real applications, what decision makers (DMs) want is a unique solution or a set of solutions rather than the overall Pareto-optimal front. It is a difficult task to solve many-objective problems by using preference information provided by decision maker (DM) during optimization process. In this paper, a new angle-based preference selection mechanism is proposed, which replaces the traditional crowding distance with the aid of preference information provided by the DMs. Particularly, we combine it with a multi-objective immune algorithm with non-dominated neighbor-based selection. The proposed method has been extensively compared with other recently proposed preference-based EMO approaches over DTLZ1, DTLZ2, and DTLZ3 test problems with 4-100 objectives. The results of the experiment indicate that the proposed algorithm can achieve competitive and better results. [ABSTRACT FROM AUTHOR]

We study the Nesting Problem, which aims to determine a configuration of a set of irregular shapes within a rectangular sheet of material of fixed width, such that no overlap among the shapes exists, and such that the length of the sheet is minimized. When both translation and rotation of the shapes are allowed, the problem can be formulated as a nonconvex quadratically constrained programming model that approximates each shape by a set of inscribed circles and enforces that circle pairs stemming from different shapes do not overlap. However, despite many recent advances in today’s global optimization solvers, solving this nonconvex model to guaranteed optimality remains extremely challenging even for the state-of-the-art codes. In this paper, we propose a customized branch-and-bound approach to address the Nesting Problem to guaranteed optimality. Our approach utilizes a novel branching scheme to deal with the reverse convex quadratic constraints in the quadratic model and incorporates a number of problem-specific algorithmic tweaks. Our computational studies on a suite of 64 benchmark instances demonstrate the customized algorithm’s effectiveness and competitiveness over the use of general-purpose global optimization solvers, including for the first time the ability to find global optimal nestings featuring five polygons under free rotation. [ABSTRACT FROM AUTHOR]