The Moth-Flame Optimization Algorithm for Flow Shop Scheduling Problem with Travel Time

This article examined the flow shop scheduling problem by considering the travel time between machines. The objective function of this problem was to provide a makespan. The Moth Flame Optimization (MFO) algorithm was proposed to solve the flow shop problem. The MFO experiment was carried out with a combination of iteration parameters and the population of the MFO algorithm to solve the flow shop scheduling problem. The computational results showed that MFO could produce a better solution than the actual scheduling method. Furthermore, the MFO Proposal Algorithm was able to reduce the makespan by up to 3%.

Deep learning-aided high-precision data detection for massive MU-MIMO systems

The data detector for future wireless system needs to achieve high throughput and low bit error rate (BER) with low computational complexity. In this paper, we propose a deep neural networks (DNNs) learning aided iterative detection algorithm. We first propose a convex optimization-based method for calculating the efficient detection of iterative soft output data, and then propose a method for adjusting the iteration parameters using the powerful data driven by DNNs, which achieves fast convergence and strong robustness. The results show that the proposed method can achieve the same performance as the known algorithm at a lower computation complexity cost.

Permeability Calculation of Sand Conglomerate Reservoirs Based on Nuclear Magnetic Resonance (NMR)

The concept of an intermingled fractal unit (IFU) model was first proposed by Atzeni and Pia in 2008, and their model has since been successfully applied to predict thermal conductivity, electrical conductivity, and the mechanical properties of porous media materials. This paper, based on the Pia IFU model, fits the pore size distribution spectrum to quantitatively characterize the Triassic Karamay Formation conglomerate reservoirs in the Mahu region, in the Junggar Basin of Northwest China, and makes permeability predictions using the free fluid T 2 spectrum according to the nuclear magnetic resonance (NMR) experimental data. The results show that the accuracy of the IFU model is significantly higher than that of the classic Coates and SDR models for conglomerate reservoirs with complex pore structures, indicating that this is an effective method to calculate permeability based on NMR. In addition, preliminary discussions are entered into regarding the intermingled fractal expression of the Kozeny-Carman equation and the relative permeability, in order to widen the application of the IFU model in reservoir physics. The derived expressions appear complicated in form but are straightforward to calculate and apply using computer programming since their iteration parameters are definite. The findings set out in this paper provide a valuable reference for further research of the IFU model in reservoir physics.

Integrated Procurement-Production Inventory Model with Two-Stage Production

The inventory-production system concerns the effective management of the goods flows from raw materials to finished products. The Integrated Procurement-Production (IPP) system consists of many elements that must be managed effectively. The problem will be more complex if it involves deciding on the number of delivery frequencies at the retailer level. In this case, the Integrated Procurement-Production's objective function depends on the frequency of raw material shipments, the frequency of delivery of finished products, and the time of the production cycle. This study aims to develop an IPP system to maximize total profit. The decision variables used are the frequency of raw material delivery, the frequency of delivery of finished products, and the production cycle time. This study proposes the Dragonfly Algorithm (DA) as an algorithm for problem-solving. Dragonfly Algorithm is used to find the best inventory decision variables. This study conducted experiments with various iteration parameters and DA population. The results showed that the greater the iteration and the population used, the greater the profit. A sensitivity analysis of decision variables is also presented in this investigation.

A note on the optimal parameters of USSOR method for solving linear least squares problems

For solving rank deficient linear least squares problems, unsymmetric successive overrelaxation (USSOR) type methods are investigated by some researchers recently. In this note, we continue to study the USSOR method for solving rank deficient linear least squares problems and obtain the optimal iteration parameters and the corresponding optimal convergence factors. Numerical experiments are given to examine the feasibility and effectiveness of the USSOR method with optimal parameters.

Generalized Accelerated Hermitian and Skew-Hermitian Splitting Methods for Saddle-Point Problems

We generalize the accelerated Hermitian and skew-Hermitian splitting (AHSS) iteration methods for large sparse saddle-point problems. These methods involve four iteration parameters whose special choices can recover the preconditioned HSS and accelerated HSS iteration methods. Also a new efficient case is introduced and we theoretically prove that this new method converges to the unique solution of the saddle-point problem. Numerical experiments are used to further examine the effectiveness and robustness of iterations.

A New GSOR Method for Generalised Saddle Point Problems

A novel generalised successive overrelaxation (GSOR) method for solving generalised saddle point problems is proposed, based on splitting the coefficient matrix. The proposed method is shown to converge under suitable restrictions on the iteration parameters, and we present some illustrative numerical results.

Generalized ASOR and Modified ASOR Methods for Saddle Point Problems

Recently, the accelerated successive overrelaxation- (SOR-) like (ASOR) method was proposed for saddle point problems. In this paper, we establish a generalized accelerated SOR-like (GASOR) method and a modified accelerated SOR-like (MASOR) method, which are extension of the ASOR method, for solving both nonsingular and singular saddle point problems. The sufficient conditions of the convergence (semiconvergence) for solving nonsingular (singular) saddle point problems are derived. Finally, numerical examples are carried out, which show that the GASOR and MASOR methods have faster convergence rates than the SOR-like, generalized SOR (GSOR), modified SOR-like (MSOR-like), modified symmetric SOR (MSSOR), generalized symmetric SOR (GSSOR), generalized modified symmetric SOR (GMSSOR), and ASOR methods with optimal or experimentally found optimal parameters when the iteration parameters are suitably chosen.

Convergence Theorems for Fixed Points of Multivalued Mappings in Hilbert Spaces

Let H be a real Hilbert space and K a nonempty closed convex subset of H. Suppose T:K→CB(K) is a multivalued Lipschitz pseudocontractive mapping such that F(T)≠∅. An Ishikawa-type iterative algorithm is constructed and it is shown that, for the corresponding sequence {xn}, under appropriate conditions on the iteration parameters, lim infn→∞⁡ d (xn,Txn)=0 holds. Finally, convergence theorems are proved under approximate additional conditions. Our theorems are significant improvement on important recent results of Panyanak (2007) and Sastry and Babu (2005).

Self-similar hierarchical honeycombs

Hierarchical structures are observed in nature, and can be shown to offer superior efficiency. However, the potential advantages of structural hierarchy are not well understood. We extensively explored a bending-dominated model material (i.e. transversely loaded hexagonal honeycomb) which is susceptible to improvement by simple iterative refinement that replaces each three-edge structural node with a smaller hexagon. Using a blend of analytical and numerical techniques, both elastic and plastic properties were explored over a range of loadings and iteration parameters. A wide variety of specific stiffness and specific strengths (up to fourfold increase) were achieved. The results offer insights into the potential value of iterative structural refinement for creating low-density materials with desired properties and function.