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The n -dimensional HSDC with the logic graph H n is one of the most attractive server-centric data center networks for high incremental scalability. The routing design in network topology is very important for information transmission. In particular, the application of disjoint path covers can solve various problems such as program code optimization and mapping parallel programs to parallel structures. H n consists of 2 n cliques and each two vertices in a clique are adjacent. For any vertex sets { x , u } and { y , v } in W and B , respectively, and any two disjoint vertex sets A 1 , A 2 in some clique, where (W , B) is a bipartition of H n , in this paper, we prove that, when | A i | ≠ 1 and A i ∩ { x , y , u , v } = ∅ for i = 1 , 2 , if | A 1 ∪ A 2 | ≤ n − 3 , then there exists an (x , y) -path P 1 and a (u , v) -path P 2 in H n passing through A 1 and A 2 , respectively, such that P 1 and P 2 have no common vertices and P 1 ∪ P 2 contains all vertices of H n. Then P 1 ∪ P 2 is called the 2-disjoint path cover in H n and we propose the efficient algorithms for the path design. Furthermore, we prove that our main result is optimal. • In the problem of data harvest and information transmission, it is very important to design the disjoint path cover in the logic graph of Data Center Network. • We consider the construction of 2-disjoint path cover such that each path should contain all prescribe vertices which have low probability of failure. • The efficient algorithms have been given and we prove that the number of prescribed vertices is not more than n − 3 and explain that this upper bound is sharp. [ABSTRACT FROM AUTHOR]

In this paper, we consider the eigenvalue bounds for real symmetric matrices whose entries are in a given interval. Based on some matrix computation theories, a new algorithm and some theorems are presented which provide ways for solving the left problems in former papers and obtain satisfying results. Furthermore, the ideas are spread to general symmetric interval matrices. Finally, numerical examples are presented which show the validity of the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, a class of differential variational inequalities are studied, and a new approach is introduced to relax the convexity condition. Firstly, an existence theorem of Carathéodory weak solution for the differential variational inequalities is established. Secondly, an algorithm for solving the problem is developed and the convergence analysis for the algorithm is given. Finally, a numerical example is reported to illustrate the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a new modified method for solving multi-level multi objective linear fractional programming problems (ML-MOLFPPs) based on fuzzy goal programming (FGP) approach with some modifications in the algorithm suggested by Baky (2010) [18] which dealt with multi-level multi objective linear programming problem (ML-MOLPP). In proposed modified approach, numerator and denominator function of each objective at each level are individually transformed into fuzzy goals and their aspiration levels are determined using individual best solutions. Different linear membership functions are defined for numerator and denominator function of each objective function. Then highest degree of each of these membership goals is achieved by minimising the sum of negative deviational variables. The proposed algorithm simplifies the ML-MOLFPP by eliminating solution preferences by the decision makers at each level, thereby avoiding difficulties associate with multi-level programming problems and decision deadlock situations. The aim of this paper is to present simple and efficient technique to obtain compromise optimal solution of ML-MOLFP problems. Numerical examples are illustrated in order to support the proposed modified FGP technique. [ABSTRACT FROM AUTHOR]

The hybridization of Differential Evolution (DE) and Particle Swarm Optimization (PSO) have been well preferred over their individual effort in solving optimization problems. The way of applying DE and PSO in the hybridization process is a big deal to achieve promising solutions. Recently, they have been used simultaneously (i.e. in parallel) on different sub-populations of the same population, instead of applying them alternatively in series over the generation. An attempt is made in this paper to hybrid DE and PSO in parallel, under a ‘tri-population’ environment. Initially, the whole population (in increasing order of fitness) is divided into three groups – inferior group, mid group and superior group. Based on their inherent ability, DE is employed in the inferior and superior groups whereas PSO is used in the mid-group. This proposed method is named as DPD as it uses DE–PSO–DE on the sub-populations of the same population. Two more strategies namely Elitism (to retain the best obtained values so far) and Non Redundant Search (to improve the solution quality) have been incorporated in DPD cycle. The paper is designed with three major aims: (i) investigation of suitable DE-mutation strategies to support DPD, (ii) performance comparison of DPD over state-of-the-art algorithms through a set of benchmark functions and (iii) application of DPD to real life problems. Numerical, statistical and graphical analysis in this paper finally concludes the robustness of the proposed DPD. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates single-machine scheduling problems with piece-rate machine maintenance and interval constrained actual processing time. The actual processing time of a job is a general function of the normal job processing time and the position in job sequence, and it is required to restrict in given interval otherwise earliness or tardiness penalty should be paid. The maintenance duration studied in the paper is a time-dependent linear function. The objective is to find jointly the optimal frequency to perform maintenances and the optimal job sequence to minimize the total cost, which is a linear function of the makespan, total earliness and total tardiness. There is shown that the problem can be optimally solved in O(n 4) time. There is also shown that two special cases of the problem can be optimally solved by lower order algorithms. [Copyright &y& Elsevier]

Abstract: In this paper, we extend technique for order preference by similarity ideal solution (TOPSIS) for solving large scale multiple objective programming problems involving fuzzy parameters. These fuzzy parameters are characterized as fuzzy numbers. For such problems, the α-Pareto optimality is introduced by extending the ordinary Pareto optimality on the basis of the α-level sets of fuzzy numbers. An interactive fuzzy decision making algorithm for generating α-Pareto optimal solution through TOPSIS approach is provided where the decision maker (DM) is asked to specify the degree α and the relative importance of objectives. Finally, a numerical example is given to clarify the main results developed in the paper. [Copyright &y& Elsevier]

Abstract: In this paper, from among multi-criteria models in making complex decisions and multiple attribute models for the most preferable choice, technique for order preference by similarity ideal solution (TOPSIS) approach has been dealt with. In some cases, determining precisely the exact value of the attributes is difficult and that, as a result of this, their values are considered as intervals. Therefore, the aim of this paper is to extend the TOPSIS method for decision-making problems with interval data. By extension of TOPSIS method, an algorithm to determine the most preferable choice among all possible choices, when data is interval, is presented. Finally, an example is shown to highlight the procedure of the proposed algorithm at the end of this paper. [Copyright &y& Elsevier]

In this paper, we present an accelerated gradient based algorithm by minimizing certain criterion quadratic function for solving the generalized Sylvester-transpose matrix equation A X B + C X T D = F . The idea is from (Ding and Chen, 2005; Niu et al., 2011; Wang et al., 2012) in which some efficient algorithms were developed for solving the Sylvester matrix equation and the Lyapunov matrix equation. On the basis of the information generated in the previous half-step, we further introduce a relaxation factor to obtain the solution of the generalized Sylvester-transpose matrix equation. We show that the iterative solution converges to the exact solution for any initial value provided that some appropriate assumptions. Finally, some numerical examples are given to illustrate that the introduced iterative algorithm is efficient. [ABSTRACT FROM AUTHOR]

Application of evolutionary computation techniques is relatively novel for machine learning. Motivated by different types of evolutionary computation techniques, different types of automatic programming were proposed. Biogeography-Based Optimization (BBO) is a new evolutionary algorithm that is inspired by the science of biogeography and has been shown to be competitive to other population-based algorithms. Inspired by biogeography theory and previous results, in this paper Biogeography-Based Programming (BBP) is proposed as a new type of automatic programming for creating polynomial regression models. In order to show the effectiveness of the proposed BBP, a number of experiments were carried out on a suite set of benchmark functions and the results were also compared with several existing automatic programming algorithms. Furthermore, sensitivity analysis was performed for the parameter settings of the proposed BBP. The results indicate that the proposed model is promising in terms of success rate and accuracy and it performs better than other algorithms investigated in this consideration. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new algorithm for finding a common element of the set of solutions of an equilibrium problem and the set of common fixed points of two finite families of demicontractive mappings. The strong convergence theorem of the proposed algorithm is established under some suitable control conditions in a real Hilbert space. Our result generalizes several recent results in the current literature. [ABSTRACT FROM AUTHOR]

In this paper, we present a new regularized quasi-Newton algorithm for unconstrained optimization. In this algorithm, an adaptive quadratic term is employed to regularize the quasi-Newton model. At each iteration, the trial step is obtained by solving an unconstrained quadratic subproblem. The global convergence and superlinear convergence of the new algorithm are established under reasonable assumptions. The numerical results show that new algorithm is effective. [ABSTRACT FROM AUTHOR]

Provided with the real spectrum, this paper presents a numerical procedure based on the induction principle to solve two types of inverse eigenvalue problems, one for nonnegative matrices and another for symmetric nonnegative matrices. As an immediate application, our approach can offer not only the sufficient condition to solve inverse eigenvalue problems for nonnegative or symmetric nonnegative matrices, but also a numerical way to solve inverse eigenvalue problems for stochastic matrices. Numerical examples are presented to show the capacity of our method. [ABSTRACT FROM AUTHOR]

This article attempts to present an algorithm to find Nash equilibrium point in large two-person static games. In static games, each player chooses his/her strategy simultaneously with other players. Equilibrium can be easily recognized in static games with a limited number of strategies. This paper focuses on two-person games in which each player deals with many strategies. After reviewing the literature, this article considers concepts such as game, equilibrium, and rationalism in order to identify the problem and the required methodology to solve it; eventually, a heuristic-meta heuristic algorithm will be presented to solve such problems which have been validated using a sample problem. [ABSTRACT FROM AUTHOR]

In this paper, we establish a class of accelerated Uzawa (AU) algorithms for solving the large sparse nonsingular saddle point problems by making use of the extrapolation technique. This extrapolation technique is based on the eigenvalues of the iterative matrix. These AU algorithms involve two iteration parameters whose special choices can cover the known classical Uzawa method, as well as yield new ones. Firstly, the accelerated model for the Uzawa algorithm is established and the detail algorithm description of AU method is presented. Then the convergence analyse of the AU method is given. Moreover, theoretical analyses show that the AU algorithm converges faster than some Uzawa-type methods (the Uzawa method is also included in) when the eigenvalues of the iterative matrix and the parameter τ satisfy some conditions. Numerical experiments on a few model problems are presented to illustrate the theoretical results and examine the numerical effectiveness of the AU method. [ABSTRACT FROM AUTHOR]

The assignment problem (AP) is to optimally solve the task of assigning n jobs to n individuals, such that minimum cost or maximum profit can be obtained. It is a vitally important NP-complete problem in operation management and applied mathematics, having a wide range of applications. In this paper, we present a new parallel DNA algorithm to solve the assignment problem using DNA molecular operations. Meanwhile, we design flexible length DNA strands to represent different jobs and individuals, take appropriate steps and get the solutions of the AP in proper length range and with O ( n 2 ) time complexity. We extend the application of DNA molecular operations and simultaneity reduce the complexity of the computation. [ABSTRACT FROM AUTHOR]

Fast algorithms for solving symmetric penta-diagonal Toeplitz linear systems have been proposed in McNally (2010) [7], Nemani (2010) [8] and Xu (2010) [11]. In this paper, we discuss the general nonsymmetric problem and propose an algorithm for solving nonsymmetric penta-diagonal Toeplitz linear systems. Numerical examples are given to illustrate the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a numerical method to calculate the basic reproductive number, commonly denoted as R0, for a class of compartmental epidemiological models in nonhomogeneous environments. We focus our attention on time-periodic models that incorporate seasonal variation and heterogeneity into disease dynamics. Our numerical algorithm is based on a procedure that efficiently transforms the operator eigenvalue problem into a matrix eigenvalue problem. In addition to time-periodic ODE models, the proposed computational approach can also be applied to spatially heterogeneous epidemic models represented by reaction-diffusion PDE systems. We present several examples to demonstrate the validity and application of this algorithm, and we compare the method with existing approaches for the calculation of R0. [ABSTRACT FROM AUTHOR]

Abstract: Recently, three computational algorithms for solving comrade linear systems have been proposed by Karawia (2013, 2007) [6,5] and by Sogabe (2008) [10]. In this paper, we derive a novel elementary algorithm for solving comrade linear systems based on matrix reordering and the Sherman–Morrison–Woodbury formula. An efficient method of evaluating the determinant of a comrade matrix is also discussed. [Copyright &y& Elsevier]

Abstract: The main weak points in using AI optimization techniques are the possibility of being trapped at local minima, being confined to the population space, difficulty to solve heavily nonlinear problems and to make full use of the historical information beside the lack of prediction about the search space. This paper is concerned with a hybrid optimization technique; namely cultural-based genetic algorithm MCBGA for solving multidimensional and nonlinear applications. The feature of proposed MCBGA technique is enhanced using biased initialization and dynamic parameters setting. Also elitism is carried out. The proposed approach has been carried out on pressure vessel design, fed-batch fermentor and continuous Stirred Tank Reactor CSTR test systems as well as Rastrigin, a well known standard workbench. The proposed MCBGA in terms of the diversity of the optimal solutions are obtained and compared to real coded genetic algorithm as well as other optimization techniques reported in the literature such as binary Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and its variants. The results obtained using the proposed algorithm are more accurate and the fast convergence is obvious. [Copyright &y& Elsevier]

Abstract: In this paper, we study the real eigenvalue bounds of standard and generalized real interval eigenvalue problems. Based on some known sufficient conditions for the regularity of interval matrices, a new algorithm for computing real eigenvalue bounds for real interval matrices is proposed. It can easily be extended for applying to generalized real interval eigenvalue problems. One advantage of the method is that the computation procedure takes less time than former methods we have proposed. Therefore it can be applied for solving larger interval eigenvalue problems. Finally, some numerical examples are presented which show the effectiveness of the method. [Copyright &y& Elsevier]

Abstract: In this paper, the generalized complementarity problem is studied. Based on a new smoothing function, the generalized complementarity problem is solved by a smoothing Newton-type algorithm. Under suitable conditions, we prove that the iteration sequence generated by the proposed smoothing method is bounded and the proposed algorithm is globally convergent. Some numerical results are reported. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a subgradient extragradient method for solving multi-valued variational inequality. It is showed that the method converges globally to a solution of multi-valued variational inequality, provided the multi-valued mapping is pseudomonotone with nonempty compact convex values. Convergence rate is analyzed. Preliminary computational experience is also reported. [Copyright &y& Elsevier]

Abstract: In this paper, a new block preconditioner is proposed for the block partitioned matrices. This preconditioner is based on the block C-orthogonalization, where C is a symmetric positive definite matrix. The block preconditioned least squares (BPLS) and block preconditioned global least squares (BPGLS) algorithms are presented to solve the linear system of equations with block partioned coefficient matrix and multiple linear system of equations, respectively. The BPLS algorithm is applied to solve the complex linear system of equations and also BPLS and classical preconditioned least squares (PLS) algorithms are compared. Finally, some numerical experiments are given to show the efficiency of the new block preconditioner. [Copyright &y& Elsevier]

Abstract: This paper focuses on implementation of a general canonical primal–dual algorithm for solving a class of fourth-order polynomial minimization problems. A critical issue in the canonical duality theory has been addressed, i.e., in the case that the canonical dual problem has no interior critical point in its feasible space , a quadratic perturbation method is introduced to recover the global solution through a primal–dual iterative approach, and a gradient-based method is further used to refine the solution. A series of test problems, including the benchmark polynomials and several instances of the sensor network localization problems, have been used to testify the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, the least squares solution of the matrix equation for symmetric arrowhead matrices with the least norm is discussed. By using Moore–Penrose inverse and the Kronecker product, the general expression of the solution to this problem is derived. A corresponding numerical algorithm and an example are also given. [Copyright &y& Elsevier]

Abstract: In this paper, nonlinear minimax problems with inequality constraints are discussed. Combined the norm-relaxed SQP method with the idea of strongly sub-feasible directions method, a new method of quasi-strongly sub-feasible directions (MQSSFD) with arbitrary initial point for the discussed problems is presented. At each iteration of the proposed algorithm, an improved search direction is obtained by solving a quadratic program (QP) which always has a solution, and a high-order correction direction is yielded via a system of linear equations (SLE) to avoid the Maratos effect. After finite iterations, the iteration point always get into the feasible set by introducing a new non-monotone curve search. Under some mild conditions including the weak Mangasarian–Fromovitz constraint qualification (MFCQ), the proposed algorithm possesses global convergence, and the superlinear convergence is obtained without the strict complementarity. Finally, some elementary numerical experiments are implemented and reported. [Copyright &y& Elsevier]

Abstract: The alternating direction method of multipliers (ADMM) is known to be a classic and efficient method for constrained optimization problem with two blocks of variables, and its empirical efficiency has been well illustrated in various fields. Specially, for improving its speed performance, Ye and Yuan suggested to do an additional extension with an optimal step size on the variables after each iteration of the primary ADMM. Indeed, the numerical experiments indicate that this modified ADMM improves the speed performance of the ADMM by around 40% without changing the algorithmic framework much. Recently, the convergence rate of the primary ADMM is established. Inspired by its idea, in this paper, we show that this improved ADMM also has convergence rate. The reason that larger γ yields better speed performance is also investigated and explained. [Copyright &y& Elsevier]

Abstract: In this paper, an efficient optimization technique is proposed for constrained optimization problems with interval valued objective function. At first, the significance of interval-valued objective function and the meaning of the interval-valued solution of the proposed problem have been explained with graphical interpretation. Generally, this type of problems has infinitely many compromise solutions. The aim of this approach is to obtain one of such solutions with higher accuracy and lower computational cost. The proposed technique is mainly based on the splitting criterion of the accepted/prescribed search region, calculation of the interval inclusion functions and the selection of subregion depending on the modified interval order relations in the context of the decision makers’ point of view. Novel interval oriented constraint satisfaction rules are used for non-interval equality and inequality constraints. Clearly, the proposed technique is nothing but an imitation of well known interval analysis based branch and bound (B &B) optimization approach. The modified multi-section division criterion with some new interval oriented constraint satisfaction rules for non-interval-valued equality and inequality constraints and some novel interval order relations in the context of the decision makers’ point of view have been applied to increase the efficiency of the proposed algorithm. Finally, the technique is applied for solving some test problems and the results are compared with the same obtained from the existing methods. [Copyright &y& Elsevier]

Abstract: In this paper, we construct an iterative method to solve the general coupled matrix equations where is a reflexive matrix with a specified central principal submatrix. The algorithm produces suitable such that within finite iteration steps in the absence of roundoff errors. We show that the algorithm is stable any case. The algorithm requires little storage capacity. Given numerical examples show that the algorithm is efficient. [Copyright &y& Elsevier]

Abstract: In this paper, the steepest descent algorithm without line search is proposed for p-Laplacian. Its search direction is the weighted preconditioned steepest descent one, and step length is estimated by a formula except the first iteration. Continuation method is applied for solving the p-Laplacian with very large p. Lots of numerical experiments are carried out on these algorithms. All numerical results show the algorithm without line search can cut down some computational time. Fast convergence of these new algorithms is displayed by their step length figures. These figures show that if search direction is the steepest descent one, exact step lengths can be substituted properly with step lengths obtained by the formula. [Copyright &y& Elsevier]

• In order to solve the problem of unbalanced sample, a new Tradaboost algorithm is designed. • An industrial control anomaly detection system based on transfer learning is constructed. • The identification performance of both source and target domains is improved. • The experimental results show that the improved algorithm has higher performance. In industrial control systems, industrial infrastructure is often attacked by hackers. Due to the serious sample imbalance in industrial control data, the traditional machine learning method has poor performance in anomaly detection. In this paper, TrAdaboost algorithm is applied to industrial control anomaly detection. The samples that are easy to classify are taken as the source domain data, and the samples with poor classification effect are taken as the target domain. The source domain data is used to guide the target domain data training. Then, we improve the traditional TrAdaboost algorithm from two aspects of initial weight and final classifier, and apply it to industrial control anomaly detection. Finally, the performance of the algorithm on two different industrial control data sets is verified. And the improved algorithm is compared with other traditional algorithms. The experimental results show that the improved TrAdaboost algorithm has a significant advantage in predicting categories with a small sample size. This algorithm can accurately identify a few abnormal samples. Moreover, the F1 value, recall and precision value of the improved TrAdaboost algorithm on the two data sets have been significantly improved. This indicates that the improved TrAdaboost algorithm greatly improves the overall prediction accuracy of the model. [ABSTRACT FROM AUTHOR]

Abstract: The fast algorithms work in the Sobolev–Hilbert spaces on a finite interval and arise from the normal spline collocation method introduced in [4] by Gorbunov and developed within the last 7years by Chinese mathematicians as a reproducing kernel Hilbert space method. For a natural -class of mth order linear problems, , two extended variants of the method (applied to an operator ) yield 4th order uniform approximations of any normal solution v together with its derivatives up to order , i.e. , for . Here is the diameter of the partition of by , for . One of the extended methods is a Hermitian modification of NSCM, while the other (more effective) variant assumes uniform partitions of the domain and requires two additional phantom boundary conditions: for . The above fast methods are then applied to four example problems. The resulting numerical solutions in through are compared with each other and with those given by standard NSC–RKHS algorithms. The paper is a continuation of earlier results of the author, concerning 2nd order approximation of the normal solution – for problems of class . [Copyright &y& Elsevier]

Abstract: The vehicle routing problem (VRP) is one of the most explored combinatorial problems in operations research. A very fast and simple algorithm that solves the VRP is the well known Clarke–Wright savings algorithm. In this paper we introduce a new way of merging routes and the corresponding formula for calculating savings. We also apply the enhanced merging to develop a new heuristic – Extended Savings Algorithm (ESA) that dynamically recalculates savings during iterations. Computational results show that, on average ESA gives better solutions than the original savings algorithm. Implementing randomization of some steps of our heuristic we obtained even better results which competes with more complex and well known heuristics. The ESA is further used to generate good routes as part of a set-covering-based algorithm for the Capacitated VRP (CVRP). The numerical results of our experiments are reported. [Copyright &y& Elsevier]

Abstract: The 0-1 knapsack problem is an open issue in discrete optimization problems, which plays an important role in real applications. In this paper, a new bio-inspired model is proposed to solve this problem. The proposed method has three main steps. First, the 0-1 knapsack problem is converted into a directed graph by the network converting algorithm. Then, for the purpose of using the amoeboid organism model, the longest path problem is transformed into the shortest path problem. Finally, the shortest path problem can be well handled by the amoeboid organism algorithm. Numerical examples are given to illustrate the efficiency of the proposed model. [Copyright &y& Elsevier]

Abstract: In this paper, a sixth-order fast algorithm is presented for solving the electromagnetic scattering problem from a rectangular open cavity embedded in an infinite ground plane. Firstly, a sixth-order compact finite difference scheme is employed to discretize the scattering problem described by the Helmholtz equation with a nonlocal boundary condition. Then, exploiting the discrete Fourier transformation in the horizontal and a Gaussian elimination in the vertical direction presented by [2], the discrete system reduces to a much smaller interface system. An effective preconditioner is developed for the Krylov subspace iterative solver to solve this interface system. Numerical results show the remarkable efficiency and accuracy of the new algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, we have given the proof of an algorithm which transforms the Hamilton–Jacobi–Bellman equation HJBE in its implicit form, to a set of explicit ODE’s for the value function V. Once V is known, the optimal control may be computed in feedback form from the HJBE. [Copyright &y& Elsevier]

Abstract: In this paper we propose an adaptive nonmonotone algorithm for minimax problem. Unlike traditional nonmonotone method, the nonmonotone technique applied to our method is based on the nonmonotone technique proposed by Zhang and Hager [H.C. Zhang, W.W. Hager, A nonmonotone line search technique and its application to unconstrained optimization, SIAM J. Optim. 14(4)(2004) 1043–1056] instead of that presented by Grippo et al. [L. Grippo, F. Lampariello, S. Lucidi, A nonmonotone line search technique for Newton’s method, SIAM J. Numer. Anal. 23(4)(1986) 707–716]. Meanwhile, by using adaptive technique, it can adaptively perform the nonmonotone trust-region step or nonmonotone curvilinear search step when the solution of subproblems is unacceptable. Global and superlinear convergences of the method are obtained under suitable conditions. Preliminary numerical results are reported to show the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, a lattice Boltzmann model for Burgers’ equation is proposed through selecting equilibrium distribution function properly. The model has been verified by three test examples. The numerical results are found in good agreement with analytical solutions and numerical solutions reported in previous studies. The advantages of the resulting method are algorithmic simplicity, parallel computation, easy handling of complicated boundary conditions, so it is very easy to implement and deal with more complex problems. [Copyright &y& Elsevier]

Abstract: In this paper, a trust-region sequential quadratic programming algorithm with a modified filter acceptance mechanism is proposed for nonlinear equality constrained optimization. The most important advantage of the proposed algorithm is its avoidance of any feasibility restoration phase, a necessity in traditional filter methods. We solve quadratic programming subproblems based on the well-known Byrd–Omojokun trust-region method. Inexact solutions to these subproblems are allowed. Under some standard assumptions, global convergence of the proposed algorithm is established. Numerical results show our approach is potentially useful. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new partial splitting augmented Lagrangian method for solving the separable constrained convex programming problem where the objective function is the sum of three separable convex functions and the constraint set is also separable into three parts. The proposed algorithm combines the alternating direction method (ADM) and parallel splitting augmented Lagrangian method (PSALM), where two operators are handled by a parallel method, while the third operator and the former two are dealt with by an alternating manner. Under mild conditions, we prove the global convergence of the new method. We also report some preliminary numerical results on constrained matrix optimization problem, illustrating the advantage of the new algorithm over the most recently PADALM of Peng and Wu (2010) [12]. [Copyright &y& Elsevier]

Abstract: The quadratic assignment problem (QAP) is one of the most studied combinatorial optimization problems with various practical applications. In this paper, we present breakout local search (BLS) for solving QAP. BLS explores the search space by a joint use of local search and adaptive perturbation strategies. Experimental evaluations on the set of QAPLIB benchmark instances show that the proposed approach is able to attain current best-known results for all but two instances with an average computing time of less than 4.5hours. Comparisons are also provided to show the competitiveness of the proposed approach with respect to the best-performing QAP algorithms from the literature. [Copyright &y& Elsevier]

Abstract: We have studied on quantum algorithm several years, and introduced a mathematical model of it in order to discuss the computational complexity. Our model of quantum algorithm, called a generalized quantum Turing machine (GQTM) contains not only unitary computation process but also quantum measurement and dissipative process. Moreover, we discovered that a chaos dynamics has very important role in quantum algorithm, that is useful to solve NP complete problem in polynomial time. In this paper, we introduce the GQTM and some applications. [Copyright &y& Elsevier]

Abstract: In this paper we study the permutation flow shop scheduling problem with order acceptance and weighted tardiness (PFSS-OAWT) faced by firms that have a number of candidate orders to be selected and scheduled on a flow shop production line. The objective is to maximize the total net profit with weighted tardiness penalties. We formulate the PFSS-OAWT problem as an integer programming (IP) model. A heuristic algorithm named Simulated Annealing Based on Partial Optimization (SABPO) is developed for solving the IP model and obtaining near-optimal solutions. Computational studies are carried out on solving 160 problem instances with different scales (small, medium, large, and very large). The experimental results show that the SABPO algorithm exhibits good optimality for small-sized problems and robustness for medium/large-sized problems compared with benchmarks. [Copyright &y& Elsevier]

Abstract: This paper studies a single machine scheduling problem simultaneously with deteriorating jobs and learning effects. The objectives are to minimize the makespan and the number of tardy jobs, respectively. Two polynomial time algorithms are proposed to solve these problems optimally. [Copyright &y& Elsevier]

Abstract: Highly reliable systems can reduce loss of money and time in practice. System reliability can be enhanced by: (i) increasing component reliabilities and/or (ii) providing redundancy at the component level. A trade-off between these two options is necessary for nonlinear-constrained reliability optimization. The problem of maximizing system reliability through component reliability choices and component redundancy is called as reliability–redundancy allocation problem, and it is a difficult but realistic nonlinear mixed-integer optimization problem. In this paper, under nonlinear constraints of weight, cost, and volume, we propose a new immune based two-phase approach to solve the reliability–redundancy allocation problem. In the first phase, an immune based algorithm (IA) is developed to solve the allocation problem, and in the second phase we present a new procedure to improve the solutions by IA. Numerical results of four benchmark problems are reported and compared. As shown, the solutions by the new proposed approach are all superior to those best solutions by typical approaches in the literature. [Copyright &y& Elsevier]

Abstract: Chaotic catfish particle swarm optimization (C-CatfishPSO) is a novel optimization algorithm proposed in this paper. C-CatfishPSO introduces chaotic maps into catfish particle swarm optimization (CatfishPSO), which increase the search capability of CatfishPSO via the chaos approach. Simple CatfishPSO relies on the incorporation of catfish particles into particle swarm optimization (PSO). The introduced catfish particles improve the performance of PSO considerably. Unlike other ordinary particles, the catfish particles initialize a new search from extreme points of the search space when the gbest fitness value (global optimum at each iteration) has not changed for a certain number of consecutive iterations. This results in further opportunities of finding better solutions for the swarm by guiding the entire swarm to promising new regions of the search space and accelerating the search. The introduced chaotic maps strengthen the solution quality of PSO and CatfishPSO significantly. The resulting improved PSO and CatfishPSO are called chaotic PSO (C-PSO) and chaotic CatfishPSO (C-CatfishPSO), respectively. PSO, C-PSO, CatfishPSO, C-CatfishPSO, as well as other advanced PSO procedures from the literature were extensively compared on several benchmark test functions. Statistical analysis of the experimental results indicate that the performance of C-CatfishPSO is better than the performance of PSO, C-PSO, CatfishPSO and that C-CatfishPSO is also superior to advanced PSO methods from the literature. [Copyright &y& Elsevier]

Abstract: Minesweeper is a popular single player game. It has been shown that the Minesweeper consistency problem is NP-complete and the Minesweeper counting problem is #P-complete. In this paper, we present efficient algorithms for solving these problems for Minesweeper graphs with bounded treewidth. Our algorithms turn out to be much better than those based directly on dynamic programming. The algorithms mostly use of algebraic operations on multivariate polynomials, so that one may use existing software to implement them easily. [Copyright &y& Elsevier]

Abstract: Many real life problems can be stated as a minimax optimization problem, such as the problems in economics, finance, management, engineering and other fields. In this paper, we present an algorithm with nonmonotone strategy and second-order correction technique for minimax optimization problems. Using this scheme, the new algorithm can overcome the difficulties of the Maratos effect occurred in the nonsmooth optimization, and the global and superlinear convergence of the algorithm can be achieved accordingly. Numerical experiments indicate some advantages of this scheme. [Copyright &y& Elsevier]

Abstract: Ranking technology suppliers is an important area to which insufficient attention has been paid by researchers. Present models of technology suppliers ranking are all based on both buyer’s perspective as well as on complete discretionary of decision making factors. The objective of this paper is to propose an innovative algorithm for ranking technology suppliers in the presence of nondiscretionary factors from supplier’s perspective. A numerical example demonstrates the application of the proposed method. [Copyright &y& Elsevier]