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

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

Abstract: In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. The Taguchi method is widely used in the industry for optimizing the product and the process conditions. By adopting the Taguchi method into the tracing mode process of the PCSO method, we propose the EPCSO method with better accuracy and less computational time. In this paper, five test functions are used to evaluate the accuracy of the proposed EPCSO method. The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method. We also apply the proposed method to solve the aircraft schedule recovery problem. The experimental results show that the proposed EPCSO method can provide the optimum recovered aircraft schedule in a very short time. The proposed EPCSO method gets the same recovery schedule having the same total delay time, the same delayed flight numbers and the same number of long delay flights as the . The optimal solutions can be found by the proposed EPCSO method in a very short time. [Copyright &y& Elsevier]

This paper presents a novel metaheuristic algorithm called queuing search (QS), which is inspired from human activities in queuing. Some common phenomena are considered in QS: (1) customers actively follow the queue that provides fast service; (2) each customer service is mainly affected by the staff or customer itself; and (3) a customer can be influenced by others during the service when the queue order is not strictly maintained. The performance of QS is tested on 30 bound-constrained benchmark functions scalable with 30 and 100 dimensions from CEC 2014, 5 standard and 4 challenging constrained engineering optimization problems. Meanwhile, comparisons are performed among the results of QS and some state-of-the-art or well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

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

In this paper, we examine the mathematical structure of chemical reaction networks with the goals of identifying reaction invariant states and determining their physical significance. A combined species-reaction graph/convex analysis approach is developed to find semi-positive invariant states associated with a reaction network. Application of this graphical/algebraic reaction network analysis approach to four different chemical processes reveals that reaction invariants can represent conserved quantities other than elemental balances. [ABSTRACT FROM AUTHOR]

Artificial gas-lift (AGL) technique is commonly used to enhance oil production when the reservoir pressure in wells is not enough to sustain acceptable oil flow rate. However, the gas-lift wells are prone to instability, characterized by regular oscillations of pressure and flow. This phenomenon is known as casing-heading instability. It results in production loss and negative impact on downstream equipment, and has been a challenging problem to both industry and academia. In this paper, a novel concept of optimal tracking control is proposed for stabilization and operating mode transition in gas-lift wells when casing-heading phenomenon occurs. The stability of artificial gas-lift process is ensured by manipulating both gas lift choke and oil production choke, where the openings of both choke valves can vary from fully closed to fully open. Through the simulation of the open-loop system, a stability map of AGL process is produced. Then a trajectory optimization algorithm is developed based on this stability map, which is synthesized with a tracking controller to achieve trajectory optimization control. Also, a nonlinear state observer is designed to ensure estimation of unmeasurable variables. Through simulation studies, the effectiveness of proposed trajectory optimization control is demonstrated. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the design of electronically steerable linear arrays for intelligent antenna systems. The design problem is modeled as a multi-objective optimization problem with non-linear constraints. The well-known NSGA-II and SPEA 2 algorithms are employed as the methodologies to solve the resulting optimization problem. The main goal and contribution of this paper is computation of the trade-off curves between side lobe level and main beam width for steerable linear arrays. The addressed problem considers a driving-point impedance restriction placed on each element in the array. This consideration makes the problem more restrictive and therefore more difficult to solve. Experimental results show the effectiveness of the algorithms for the design of steerable linear arrays. [Copyright &y& Elsevier]

Infinitesimal perturbation analysis (IPA) provides formulas for random gradients (derivatives) of performance measures with respect to parameters of interest, computed from sample paths of stochastic systems. In practice, IPA derivatives may be computed either from simulation runs or from empirical field data (when the formulas are nonparametric). Nonparametric IPA derivatives in fluid-flow queues have been recently derived for the loss volume and time average of buffer occupancy, with respect to buffer size, and arrival-rate or service-rate parameters. Additionally, these IPA derivatives have been shown to be unbiased in the sense that their expectation and differentiation operators commute, while their traditional discrete counterparts have long been known to be generally biased. Recent work has further shown how to map the computation of IPA derivatives from a fluid-flow queue to a compatible discrete counterpart without an appreciable loss of accuracy in performance measures. Thus, this work holds the promise of potential applications of IPA derivatives to gradient-based optimization of objective functions involving performance metrics parameterized by settable parameters in a queueing network context. This paper is an empirical study of IPA derivatives of individual queues within queueing systems which model telecommunications networks and some of their protocols. As a testbed, we used HNS (Hybrid Network Simulator) — a hybrid Java simulator of queueing networks with traffic streams subject to several telecommunications protocols. More specifically, the hybrid feature of HNS admits models with mixtures of discrete (packet) flows and continuous (fluid) flows, and collects detailed statistics and IPA derivatives for all flow types. The paper outlines the mapping of IPA derivatives from the fluid domain to the packet domain as implemented in HNS, and studies the accuracy of IPA derivatives in compatible fluid and packet queueing models, as well as the stabilization of their values in time. Our experimental results lend empirical support to the contention that IPA derivatives can be accurately computed from discrete versions by adopting a fluid-flow view. Furthermore, the long-run values of various IPA derivatives are empirically shown to stabilize quite fast. Finally, the results provide the basis and motivation for IPA applications to the optimization of telecommunications network design and to potential new open-loop protocols that take advantage of IPA information. [Copyright &y& Elsevier]

Abstract: This paper proposes an improvement of an efficient multiobjective optimization algorithm, Non-dominated Sorting Genetic Algorithm II, NSGA-II, that has been here applied to solve the problem of optimal capacitors placement in distribution systems. The studied improvement involves the Crowded Comparison Operator and modifies it in order to handle several constraints. The problem of optimal location and sizing of capacitor banks for losses reduction and voltage profile flattening in medium voltage (MV) automated distribution systems is a difficult combinatorial constrained optimization problem which is deeply studied in literature. In this paper, the efficiency of the proposed Crowded Comparison Operator, CCO1, is compared to the efficiency of another Crowded Comparison Operator, CCO2, whose definition derives from the constraint-domination principle proposed by Deb et al. The two operators are tested on difficult test problems as well as on the optimal capacitors placement problem. [Copyright &y& Elsevier]

Abstract: The problem of Systems Identification starts with a time-series of observed data and tries to determine the simplest model capable of exhibiting the observed behavior. This optimization problem searches the model from a space of possible models. In this paper, we present the theory and algorithms to perform Qualitative and Quantitative Systems Identification for Linear Time-Invariant Dynamic Systems. The methods described here are based on successive elimination of the components of the system''s response. Sinusoidals of high frequencies are eliminated first, then their carrying waves. We continue with the process until we obtain a non-oscillatory carrier. At this point, we determine the order of the carrier. This procedure allows us to determine how many sinusoidal components and exponential components are found in the impulse response of the system under study. The number of components determines the order of the system. The paper is composed of two important parts, the statement of some mathematical properties of the responses of Linear Time Invariant Dynamic Systems, and the proposal of a set of filters that allows us to implement the recognition algorithm. We present the application of the proposed methodology to analyze and model the electrical circuits and electrical power systems. [Copyright &y& Elsevier]

The paper covers the basic idea, mathematical features of conventional Neuro PID controller which is a technique to make any linear or nonlinear system unaffected to unpredictability of system's parameters and disturbances such as noise. Here we suggest a technique to apply neural networks for the tuning of the PID (proportional, integral and derivative) controller's gains in a way human tune the gains depending on the environmental and systems requirements. Error back-propagation method is used as the tuning method for the controller which is also known as BP method and this method works on the local minima algorithm. [ABSTRACT FROM AUTHOR]

In real world, the software systems often need to be continuously modified to satisfy the ever changing requirements and environment. Mostly, it is carried out without following the original design principles of the system. Over a period of time, such a continuous modification deteriorates the structural quality, hence increases the system complexity. To improve the structural quality of whole system, the software clustering seems more feasible technique. Recently, the search – based approach gain more attention to solve the software clustering problem. In this paper, we propose a search – based multi – objective optimization to re-structure the object – oriented software system using different coupling strength scheme such as binary coupling, absolute coupling and relative coupling scheme. The approach is evaluated over four real – world and three random software applications. The experimentation results show that how the use of absolute and relative coupling strength scheme leads to generate more effective solutions compared binary coupling strength. [ABSTRACT FROM AUTHOR]

In this research paper, we propose novel features based on information theory for image retrieval. We propose the novel concept of “probabilistic filtering”. We propose a hybrid approach for image retrieval that combines annotation approach with content based image retrieval approach. Also rough set theory is proposed as a tool for audio/video object retrieval from multi-media databases. [ABSTRACT FROM AUTHOR]

Real world applications, such as news feeds categorization deal with multi-label classification problem, where the objects are associated with multiple class labels and each object is represented by a single instance (feature vector). In this paper, a new algorithm adaptation method called centroid-based multi-label classification using class-based features (CCBF) algorithm has been proposed to tackle the multi-label classification problem. It includes class-based feature vectors generation and local label correlations exploitation. In the testing stage, centroid-based classification algorithm is extended for multi-label classification problem. Experiments on reuters multi-label dataset with 103 labels demonstrate the performance and efficiency of CCBF algorithm and the result is compared with those obtained using other multi-label classification algorithms. The CCBF algorithm obtains competitive F measures with respect to the most accurate algorithms. [ABSTRACT FROM AUTHOR]

A Mobile Ad hoc Network (MANET) is a group of mobile nodes which cooperate in forwarding packets in a multi-hop fashion without any centralized administration. One of its key challenges is finding the malicious node in MANETs. In the literature many techniques have been proposed by researchers. In this paper we have proposed a scheme in which we are sending a control sequence to the neighbour nodes and we are expecting the nodes response. Based on the node response we can identify the malicious node. [ABSTRACT FROM AUTHOR]

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

Modern day's queries are posed on database spread across the globe, this may impose a challenge on processing queries efficiently, and a strategy is required to generate optimal query plans. In distributed relational database systems, due to partitioning or replication on relations at multiple sites, the relations required by a query to answer, may be stored at multiple sites. This leads to an exponential increase in the number of possible equivalent alternatives or query plans for a user query. Though it is not computationally reasonable to explore exhaustively all possible query plans in a large search space, the query plan with most cost-effective option for query processing is measured necessary and must be generated for a given query. In this paper, an attempt has been made to generate such optimal query plans using parameter less optimization technique Teaching-Learner based Optimization (TLBO). The TLBO algorithm was observed to go one better than the other optimization algorithms for the multi-objective unconstrained and constrained benchmark problems. Experimental comparisons of this algorithm with the multi-objective GA based distributed query plan generation algorithm shows that for higher number of relations, the TLBO based algorithm is able to generate comparatively better quality Top-K query plans. [ABSTRACT FROM AUTHOR]

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

Abstract: This work presents a new plant-wide control strategy able to be applied on large scale chemical plants. It is based on an extension of the non square relative gain array (NRG) theoretical concepts, introduced by Chang and Yu (1990), and the generalized relative disturbance gain (GRDG) presented in Chang and Yu (1992). The extension of the NRG is useful for searching the best group of controlled variables (CVs) independently of the problem dimensionality. Meanwhile, the extension of the GRDG allows configure the loops pairing by considering the trade-off between servo and regulator behavior. It can be done thanks to define a proper function, named net load effect, accounting both set point and disturbances effects. Even though these concepts are not new, the main contribution of this paper is the selection of the adequate objective function. It is mathematically expressed in a new way, in terms of Frobenius norm of specific matrices related with the models of the plant and very useful for evaluating the process interaction. Then, it drives the search supported by genetic algorithms (GA), which evaluates all the possible combinations of input–output variables. It allows to solve successfully and with less computational effort the combinatorial optimization problem, even though the high dimension usually involved in large scale chemical plants. The use of the relative gain array (RGA) can also be considered for pairing purpose, but in some cases it could drive to a less effective structure. The use of relative normalized gain array (RNGA) for pairing the selected CVs with the most suitable MVs is able to lead to best control structures only if a dynamic model of the plant is available. Therefore, it must be emphasized that this approach is developed for working in cases where only steady-state plant information is available. However, if a dynamic model is disposable too the algorithm is extended to use it. In addition, a mathematical demonstration is presented so as to understand why is possible to find a well conditioned control structure. The methodology is tested in the Tennessee Eastman (TE) process at the base case proposed by Downs and Vogel (1992), and at an optimized working point presented by Ricker (1995). Both working points show two quite different scenarios. Thus, a set of dynamic simulations for both cases and the hardware requirements compared to the previous suggested are given to proof the capacity of this approach. [Copyright &y& Elsevier]

Abstract: Determining the cells of an arrangement of hyperplanes is a classical problem in combinatorial geometry. In this paper we present an efficient recursive procedure to solve it. In fact, by considering a connection between the latter problem and optimality conditions of unconstrained quadratic optimization problems in the following form: , with , we can show the proposed algorithm solves problems of the form in polynomial time when the rank of the matrix is fixed and the number of its positive diagonal entries is . [Copyright &y& Elsevier]

Abstract: This paper is devoted to the consideration of an algorithm for sequential optimization of paths in directed graphs relative to di_erent cost functions. The considered algorithm is based on an extension of dynamic programming which allows to represent the initial set of paths and the set of optimal paths after each application of optimization procedure in the form of a directed acyclic graph. [Copyright &y& Elsevier]

Abstract: Kernel methods map, usually nonlinearly, the data from input space into a higher-dimensional feature space, in which linear algorithms are performed. In many applications, the inverse mapping is also useful, and the pre-images of some feature vectors need to be found in input space. However, finding pre-images is often a difficult optimization problem. This paper attempts to use evolution strategies (ES) to seek pre-images. This method firstly selects some of the nearest training patterns of an unknown pre-image as the initial group of the ES, then the ES carries out an iterative process to find the pre-images or approximate pre-images. Experimental results based on kernel principal component analysis (KPCA) for pattern denoising show that our proposed method outperforms some conventional techniques, including gradient descent technique, kernel ridge regression, and distance constraint method. Compared to these conventional techniques, the ES-based method is also straightforward to understand, and is easy to implement. [Copyright &y& Elsevier]

Abstract: This paper proposes a new multi-start, stochastic global optimization algorithm that uses dimensional reduction techniques based upon approximations of space-filling curves, simulated annealing and particle swarm optimization, aiming at finding global minima of real-valued functions that are not necessarily well behaved, that is, are not required to be differentiable, continuous, or even satisfying Lipschitz conditions. The overall idea is as follows: given a real-valued function with a multidimensional and compact domain the method builds an equivalent one-dimensional problem by composing with a linearization of searches for a small population of candidates and returns to the original high-dimensional domain, this time with a small set of promising starting points. [Copyright &y& Elsevier]

Abstract: Multiple Objective Evolutionary Algorithms (MOEAs) applied to the Job-Shop Scheduling Problem have been shown to perform better than single objective Genetic Algorithms (GAs). Helper-objectives, representing portions of the main objective, help guide MOEAs in their search process. This paper provides additional understanding of helper-objective methods. The sequence in which helper-objectives are used is examined and we show that problem-specific knowledge can be incorporated to determine a good helper-objective sequence. Computational results demonstrate how carefully sequenced helper-objectives can improve search quality. This dismisses the established practice of picking helper sequence based upon a random order due to lack of knowledge about optimal sequencing. Explanations are provided for how helpers accelerate the search process by distinguishing between otherwise similar solutions and by partial removal of epistasis in one or more dimensions of the solution space. Helper-objective size was also explored to determine if maximal helper divisions are best for the set of problems studied. Helper-objective size appears to be important to the optimization and larger helpers are not necessarily better which implies that methods such as Multi-Objectivization via Segmentation (MOS) may benefit from smaller problem divisions. Lastly, an examination of the non-dominated front size was performed to determine if tuning front size makes sense for this type of algorithm since previous works have established tuning front size as important. No evidence was found to support tuning and the correlation between small front size and effectiveness appears to be a natural part of how helper-objective algorithms work rather than a reason for reducing front size. [Copyright &y& Elsevier]

Abstract: A directed searching optimization algorithm (DSO) is proposed to solve constrained optimization problems in this paper. The proposed algorithm includes two important operations — position updating and genetic mutation. Position updating enables the non-best solution vectors to mimic the best one, which is beneficial to the convergence of the DSO; genetic mutation can increase the diversity of individuals, which is beneficial to preventing the premature convergence of the DSO. In addition, we adopt the penalty function method to balance objective and constraint violations. We can obtain satisfactory solutions for constrained optimization problems by combining the DSO and the penalty function method. Experimental results indicate that the proposed algorithm can be an efficient alternative on solving constrained optimization problems. [Copyright &y& Elsevier]

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

Abstract: This paper considers the -Compressive Sensing problem and presents an efficient algorithm that computes an exact solution. The idea consists in reformulating the problem such that it yields a modified Dantzig–Wolfe decomposition that allows to efficiently apply all standard simplex pivoting rules. Experimental results show the superiority of our approach compared to standard linear programming methods. [Copyright &y& Elsevier]

Abstract: A novel modified differential evolution algorithm (NMDE) is proposed to solve constrained optimization problems in this paper. The NMDE algorithm modifies scale factor and crossover rate using an adaptive strategy. For any solution, if it is at a standstill, its own scale factor and crossover rate will be adjusted in terms of the information of all successful solutions. We can obtain satisfactory feasible solutions for constrained optimization problems by combining the NMDE algorithm and a common penalty function method. Experimental results show that the proposed algorithm can yield better solutions than those reported in the literature for most problems, and it can be an efficient alternative to solving constrained optimization problems. [Copyright &y& Elsevier]

Abstract: In this paper, an algorithm is proposed to integrate the unsupervised learning with the optimization of the Finite Mixture Models (FMM). While learning parameters of the FMM the proposed algorithm minimizes the mutual information among components of the FMM provided that the reduction in the likelihood of the FMM to fit the input data is minimized. The performance of the proposed algorithm is compared with the performances of other algorithms in the literature. Results show the superiority of the proposed algorithm over the other algorithms especially with data sets that are sparsely distributed or generated from overlapped clusters. [Copyright &y& Elsevier]

Abstract: In this paper, a new optimization approach combining primer vector theory and evolutionary algorithms for fuel-optimal non-linear impulsive rendezvous is proposed. The optimization approach is designed to seek the optimal number of impulses as well as the optimal impulse vectors. In this optimization approach, adding a midcourse impulse is determined by an interactive method, i.e. observing the primer-magnitude time history. An improved version of simulated annealing is employed to optimize the rendezvous trajectory with the fixed-number of impulses. This interactive approach is evaluated by three test cases: coplanar circle-to-circle rendezvous, same-circle rendezvous and non-coplanar rendezvous. The results show that the interactive approach is effective and efficient in fuel-optimal non-linear rendezvous design. It can guarantee solutions, which satisfy the Lawden''s necessary optimality conditions. [Copyright &y& Elsevier]

Abstract: This paper considers a new class of network flows, called dynamic generative network flows in which, the flow commodity is dynamically generated at a source node and dynamically consumed at a sink node and the arc-flow bounds are time dependent. Then the maximum dynamic flow problem in such networks for a pre-specified time horizon T is defined and mathematically formulated in both arc flow and path flow presentations. By exploiting the special structure of the problem, an efficient algorithm is developed to solve the general form of the dynamic problem as a minimum cost static flow problem. [Copyright &y& Elsevier]

Abstract: Borodin, Nielsen and Rackoff  introduced the class of priority algorithms as a framework for modeling deterministic greedy-like algorithms. In this paper we address the effect of randomization in greedy-like algorithms. More specifically, we consider approximation ratios within the context of randomized priority algorithms. As case studies, we prove inapproximation results for two well-studied optimization problems, namely facility location and makespan scheduling. [Copyright &y& Elsevier]

Abstract: Many real problems can be modeled to the problems with a hierarchical structure, and bilevel programming is a useful tool to solve the hierarchical optimization problems. So the bilevel programming is widely applied, and numerous methods have been proposed to solve this programming. In this paper, we propose an approximate programming algorithm to solve bilevel nonlinear programming problem. Finally, the example illustrates the feasibility of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: We consider a class of global optimization problems, in which the objective function is Lebesgue integrable. In this paper, we present a sufficient and necessary condition for computing the essential infimum. This optimality condition can be used to design algorithms for solving the global optimization problem. [Copyright &y& Elsevier]

Abstract: We consider in this paper the scheduling of families of jobs in which both processing and delivery are coordinated together. Only one vehicle is available to deliver the jobs to specified customers. The jobs can be processed together to form processing batches on the machine and setups of batches are required when the machine is changing from one family to another. Jobs from different families cannot be transported together by the vehicle. The objective is to minimize the time when the vehicle finishes delivering the last delivery batch to its customer and returns to the machine. We propose an -time optimal algorithm for the scheduling problem under the group technology assumption. For the scheduling problem without the group technology assumption, we show that the problem is NP-hard and give an -time dynamic programming algorithm, where is the number of jobs, and is the number of families; we also provide a heuristic algorithm with a performance ratio of 3/2. [Copyright &y& Elsevier]

Abstract: This paper develops an optimal solution procedure for the multi-period online fulfillment assignment problem to determine how many and which of a retailer/e-tailer’s capacitated regional warehouse locations should be set up to handle online sales over a finite planning horizon. To reduce the number of candidate solutions in each period, dominance rules from the facility location literature are extended to handle the nonlinear holding and backorder cost implications of our problem. Computational results indicate that multi-period considerations can play a major role in determining the optimal set of online fulfillment locations. In 92% of our test problems, the multi-period solution incorporated fewer openings and closings than myopic single period solutions. To illustrate the use of the model under changing demands, the multi-period solution yielded different supply chain configurations than the myopic single period solution in over 37% of the periods. [Copyright &y& Elsevier]

Abstract: This paper compares the performances of two different optimisation techniques for solving inverse problems; the first one deals with the Hierarchical Asynchronous Parallel Evolutionary Algorithms software (HAPEA) and the second is implemented with a game strategy named Nash-EA. The HAPEA software is based on a hierarchical topology and asynchronous parallel computation. The Nash-EA methodology is introduced as a distributed virtual game and consists of splitting the wing design variables–aerofoil sections–supervised by players optimising their own strategy. The HAPEA and Nash-EA software methodologies are applied to a single objective aerodynamic ONERA M6 wing reconstruction. Numerical results from the two approaches are compared in terms of the quality of model and computational expense and demonstrate the superiority of the distributed Nash-EA methodology in a parallel environment for a similar design quality. [Copyright &y& Elsevier]

Abstract: In this paper, by means of a new efficient identification technique of active constraints and the method of strongly sub-feasible direction, we propose a new sequential system of linear equations (SSLE) algorithm for solving inequality constrained optimization problems, in which the initial point is arbitrary. At each iteration, we first yield the working set by a pivoting operation and a generalized projection; then, three or four reduced linear equations with a same coefficient are solved to obtain the search direction. After a finite number of iterations, the algorithm can produced a feasible iteration point, and it becomes the method of feasible directions. Moreover, after finitely many iterations, the working set becomes independent of the iterates and is essentially the same as the active set of the KKT point. Under some mild conditions, the proposed algorithm is proved to be globally, strongly and superlinearly convergent. Finally, some preliminary numerical experiments are reported to show that the algorithm is practicable and effective. [Copyright &y& Elsevier]

Abstract: In this paper, a new filled function which has better properties is proposed for identifying a global minimum point for a general class of nonlinear programming problems within a closed bounded domain. An algorithm for unconstrained global optimization is developed from the new filled function. Theoretical and numerical properties of the proposed filled function are investigated. The implementation of the algorithm on seven test problems is reported with satisfactory numerical results. [Copyright &y& Elsevier]

Abstract: This paper considers two semi-online scheduling problems, one with known optimal value and the other with known total sum, on two uniform machines with a machine speed ratio of . For the first problem, we provide an optimal algorithm for , and improved algorithms or/and lower bounds for , over which the optimal algorithm is unknown. As a result, the largest gap between the competitive ratio and the lower bound decreases to 0.02192. For the second problem, we also present algorithms and lower bounds for . The largest gap between the competitive ratio and the lower bound is 0.01762, and the length of the interval over which the optimal algorithm is unknown is 0.47382. Our algorithms and lower bounds for these two problems provide insights into their differences, which are unusual from the viewpoint of the known results on these two semi-online scheduling problems in the literature. [Copyright &y& Elsevier]

Abstract: In this paper, we present a nonmonotone conic trust region method based on line search technique for unconstrained optimization. The new algorithm can be regarded as a combination of nonmonotone technique, line search technique and conic trust region method. When a trial step is not accepted, the method does not resolve the trust region subproblem but generates an iterative point whose steplength satisfies some line search condition. The function value can only be allowed to increase when trial steps are not accepted in close succession of iterations. The local and global convergence properties are proved under reasonable assumptions. Numerical experiments are conducted to compare this method with the existing methods. [Copyright &y& Elsevier]

Abstract: The optimization of parallel applications is difficult to achieve by classical optimization techniques because of their diversity and the variety of actual parallel and distributed platforms and/or environments. Adaptive algorithmic schemes, capable of dynamically changing the allocation of jobs during the execution to optimize global system behavior, are the best alternatives for solving this problem. In this paper, we focus on non-clairvoyant scheduling of parallel jobs with known resource requirements but unknown running times, with emphasis on the regulation of idle periods in the context of general list policies. We consider a new family of scheduling strategies based on two phases which successively combine sequential and parallel execution of jobs. We generalize known worst-case performance bounds by considering two extra parameters, in addition to the number of processors and maximum processor requirements considered in the literature, namely, job parallelization penalty and idle regulation factor. Furthermore, we prove that under certain conditions of idle regulation, the performance guarantee of parallel job scheduling in space-sharing mode can be improved. [Copyright &y& Elsevier]

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

Personalisation of web information systems (WISs) means customisation of the presented data content to the needs of users, restricting the available functionality to the goals and preferences of users, and tailoring the web presentation according to used devices and style options. This paper primarily concentrates on the customisation of functionality by making all those operations available to a user that are needed to achieve a specified goal, and by organising them in an action scheme called plot that is in accordance with the behavioural preferences of the user. Plots are formalised by algebraic expressions in Kleene algebras with tests (KATs). Then personalisation can be formalised as an optimisation problem with equational preference rules, for which a term rewriting approach is proposed. In a second step the approach is extended to conditional term rewriting thereby dispensing with the particular need to associate preference rules with user profiles. Finally, the approach is refined by taking content specifications via extended views and abstract programs on these views into account. This leads us to reformulating the personalisation problem in higher-order dynamic logic. [Copyright &y& Elsevier]

Abstract: In this paper we study the higher secant varieties of Grassmann varieties in relation to the functional Waring’s problem for alternating tensors and to the Alexander–Hirschowitz theorem. We show how to identify defective higher secant varieties of Grassmannians using a probabilistic method involving Terracini’s lemma, and we describe an algorithm which can compute, by numerical methods, dim (S s G(k, n)) for n ⩽14. Our main result is that, except for Grassmannians of lines, if n ⩽14 and (if n =14 we have studied the case k ⩽5) there are only the four known defective cases: S 2 G(2,6), S 2 G(3,7), S 3 G(3,7) and S 3 G(2,8). [Copyright &y& Elsevier]

Abstract: This paper presents several algorithms that compute border bases of a zero-dimensional ideal. The first relates to the FGLM algorithm as it uses a linear basis transformation. In particular, it is able to compute border bases that do not contain a reduced Gröbner basis. The second algorithm is based on a generic algorithm by Bernard Mourrain originally designed for computing an ideal basis that need not be a border basis. Our fully detailed algorithm computes a border basis of a zero-dimensional ideal from a given set of generators. To obtain concrete instructions we appeal to a degree-compatible term ordering and hence compute a border basis that contains the reduced -Gröbner basis. We show an example in which this computation actually has advantages over Buchberger''s algorithm. Moreover, we formulate and prove two optimizations of the Border Basis Algorithm which reduce the dimensions of the linear algebra subproblems. [Copyright &y& Elsevier]