A Unified Approach for Developing EfficientAlgorithmic Programs

A unified approach called partition-and-recur for developing efficient and correct algorithmic programs is presented. An algorithm (represented by recurrence and initiation) is separated from program, and special attention is paid to algorithm manipulation rather than program calculus. An algorithm is exactly a set of mathematical formulae. It is easier for formal derivation and proof. After getting efficient and correct algorithm, a trivial transformation is used to get a final program. The approach covers several known algorithm design techniques, e.g. dynamic programming, greedy, divide-and-conquer and enumeration, etc. The techniques of partition and recurrence are not new. Partition is a general approach for dealing with complicated objects and is typically used in divide-and-conquer approach. Recurrence is used in algorithm analysis, in developing loop invariants and dynamic programming approach. The main contribution is combining two techniques used in typical algorithm development into a unified and systematic approach to develop general efficient algorithmic programs and presenting a new representation of algorithm that is easier for understanding and demonstrating the correctness and ingenuity of algorithmic programs.

AN INFEASIBLE-INTERIOR-POINT PREDICTOR-CORRECTOR ALGORITHM FOR THE SECOND-ORDER CONE PROGRAM

A globally convergent infeasible-interior-point predictor-corrector algorithm is presented for the second-order cone programming （SOCP） by using the Alizadeh- Haeberly-Overton （AHO） search direction. This algorithm does not require the feasibility of the initial points and iteration points. Under suitable assumptions, it is shown that the algorithm can find an -approximate solution of an SOCP in at most O（√n ln（ε0/ε）） iterations. The iteration-complexity bound of our algorithm is almost the same as the best known bound of feasible interior point algorithms for the SOCP.

MDA-TOEPGA:A novel method to identify miRNA-disease association based on two-objective evolutionary programming genetic algorithm

The association between miRNA and disease has attracted more and more attention.Until now,existing methods for identifying miRNA related disease mainly rely on top-ranked association model,which may not provide a full landscape of association between miRNA and disease.Hence there is strong need of new computational method to identify the associations from miRNA group view.In this paper,we proposed a framework,MDA-TOEPGA,to identify miRNAdisease association based on two-objective evolutionary programming genetic algorithm,which identifies latent miRNAdisease associations from the view of functional module.To understand the miRNA functional module in diseases,the case study is presented.We have been compared MDA-TOEPGA with several state-of-the-art functional module algorithm.Experimental results showed that our method cannot only outperform classical algorithms,such as K-means,IK-means,MCODE,HC-PIN,and ClusterONE,but can also achieve an ideal overall performance in terms of a composite score consisting of f1,Sensitivity,and Accuracy.Altogether,our study showed that MDA-TOEPGA is a promising method to investigate miRNA-disease association from the landscapes of functional module.

A POTENTIAL REDUCTION ALGORITHM FOR LINEARLY CONSTRAINED CONVEX PROGRAMMING

A potential reduction algorithm is proposed for optimization of a convex function subject to linear constraints.At each step of the algorithm,a system of linear equations is solved to get a search direction and the Armijo's rule is used to determine a stepsize.It is proved that the algorithm is globally convergent.Computational results are reported.

Application of Dynamic Programming Algorithm Based on Model Predictive Control in Hybrid Electric Vehicle Control Strategy

A good hybrid vehicle control strategy cannot only meet the power requirements of the vehicle,but also effectively save fuel and reduce emissions.In this paper,the construction of model predictive control in hybrid electric vehicle is proposed.The solving process and the use of reference trajectory are discussed for the application of MPC based on dynamic programming algorithm.The simulation of hybrid electric vehicle is carried out under a specific working condition.The simulation results show that the control strategy can effectively reduce fuel consumption when the torque of engine and motor is reasonably distributed,and the effectiveness of the control strategy is verified.

Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability programming.Inspired by evolutionary algorithms,which can iteratively find the nearoptimal solution,different Evolutionary Algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic Algorithm(GA),Evolution Strategy Algorithm(ES),Differential Evolution Algorithm(DE),and Evolutionary Programming Algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language.With the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms.It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT.

Supply-based optimal scheduling of oil product pipelines

Oil product pipelines have features such as transporting multiple materials, ever-changing operating conditions, and synchronism between the oil input plan and the oil offloading plan. In this paper, an optimal model was established for a single-source multi-distribution oil pro- duct pipeline, and scheduling plans were made based on supply. In the model, time node constraints, oil offloading plan constraints, and migration of batch constraints were taken into consideration. The minimum deviation between the demanded oil volumes and the actual offloading volumes was chosen as the objective function, and a linear programming model was established on the basis of known time nodes＇ sequence. The ant colony optimization algo- rithm and simplex method were used to solve the model. The model was applied to a real pipeline and it performed well.

Two-Machine Hybrid Flow-Shop Problems in Shared Manufacturing

In the“shared manufacturing”environment,based on fairness,shared manufacturing platforms often require manufacturing service enterprises to arrange production according to the principle of“order first,finish first”which leads to a series of scheduling problems with fixed processing sequences.In this paper,two two-machine hybrid flow-shop problems with fixed processing sequences are studied.Each job has two tasks.The first task is flexible,which can be processed on either of the two machines,and the second task must be processed on the second machine after the first task is completed.We consider two objective functions:to minimize the makespan and tominimize the total weighted completion time.First,we show the problem for any one of the two objectives is ordinary NP-hard by polynomial-time Turing Reduction.Then,using the Continuous ProcessingModule(CPM),we design a dynamic programming algorithm for each case and calculate the time complexity of each algorithm.Finally,numerical experiments are used to analyze the effect of dynamic programming algorithms in practical operations.Comparative experiments show that these dynamic programming algorithms have comprehensive advantages over the branch and bound algorithm(a classical exact algorithm)and the discrete harmony search algorithm(a high-performance heuristic algorithm).

Optimization of Urban Rail Transportation in Emerging Countries Using Operational Research Techniques

Rail systems are gradually becoming the most desirable form of transit infrastructure around the world, partly because they are becoming more environmentally friendly compared with airplanes and automobiles. This paper examines the place of emerging countries in this move of implementing modern rail system that will eventually enhance the realization of a low-carbon society. Network model, transportation model and linear programming algorithms are used to model the present urban rail transport system in Nigeria, as an emerging country, in order to optimize it. Operational research methods, including simplex method and MODI, with the aids of computer software (excel solver and LIP solver) were adopted to solve the resulting models. The results showed that optimization of rail transport system will not only reduce carbon emission but also bring about economic development which is required for the eradication of prevalent poverty in these emerging countries.

A Multi-Customer Supply Chain Scheduling with Subcontracting Option on a Single Machine

This paper studies the cost problem caused by the activity of the work-piece in the supply chain. The objective function is to find an optimal ordering that minimizes the total cost of production, transportation and subcontracting. This paper presents a dynamic programming algorithm for the corresponding sorting problem, and finally demonstrates the feasibility of the algorithm through an example.

The Effects of Nanoparticle Additives on Thermophysical Properties of Concrete Mixtures

In recent years, energy-retrofitting is becoming an imperative aim for existing buildings worldwide and increased interest has focused on the development of nanoparticle blended concretes with adequate mechanical properties and durability performance, through the optimization of concrete permeability and the incorporation of the proper nanoparticle type in the concrete matrix. In order to investigate the potential use of nanocomposites as dense barriers against the permeation of liquids into the concrete, three types of nanoparticles including Zinc Oxide (ZnO), Magnesium Oxide (MgO), and composite nanoparticles were used in the present study as partial replacement of cement. Besides, the effect of adding these nanoparticles on both pore structure and mechanical strengths of the concrete at different ages was determined, and scanning electron microscopy (SEM) images were then used to illustrate the uniformity dispersion of nanoparticles in cement paste. It was demonstrated that the addition of a small number of nanoparticles effectively enhances the mechanical properties of concrete and consequently reduces the extent of the water permeation front. Finally, the behavioral models using Genetic Algorithm (GA) programming were developed to describe the time-dependent behavioral characteristics of nanoparticle blended concrete samples in various compressive and tensile stress states at different ages.

Goal Programming for Stable Mode Transition in Tandem Turbo-ramjet Engines

This article, in order to guarantee the stable mode transition in tandem turbo-ramjet engines, investigates the multi-objective and multi-variable goal programming algorithm. First, it introduces the structural features of the variable cycle turbo-ramjet engines, the principles of selecting the mode transition operation point and the design parameters, and the characteristics of the turbofan mode and the ramjet mode. Second, a component-based variable cycle turbo-ramjet engine model is developed to simulate the mode transition process. Third, the Newton-Raphson algorithm is used to solve the multi-variable and multi-objective optimization problem. The results show that with the maximum residua of only 0.06%, this algorithm has an acceptable convergence that meets the predetermined goals. Finally, the simulation shows that the stable turbo-ramjet mode transition could be realized with the mode transition control law developed by the algorithm.

Automatic Algorithm Programming Model Based on the Improved Morgan's Refinement Calculus

The automatic algorithm programming model can increase the dependability and efficiency of algorithm program development,including specification generation,program refinement,and formal verification.However,the existing model has two flaws:incompleteness of program refinement and inadequate automation of formal verification.This paper proposes an automatic algorithm programming model based on the improved Morgan’s refinement calculus.It extends the Morgan’s refinement calculus rules and designs the C++generation system for realizing the complete process of refinement.Meanwhile,the automation tools VCG(Verification Condition Generator)and Isabelle are used to improve the automation of formal verification.An example of a stock’s maximum income demonstrates the effectiveness of the proposed model.Furthermore,the proposed model has some relevance for automatic software generation.

Optimization of Numerical Control Program and Machining Simulation Based on VERICUT

In the machining process of large-scale complex curved surface,workers will encounter problems such as empty stroke of tool,collision interference,and overcut or undercut of the workpieces.This paper presents a method for generating the optimized tool path,compiling and checking the numerical control(NC)program.Taking the bogie frame as an example,the tool paths of all machining surface are optimized by the dynamic programming algorithm,Creo software is utilized to compile the optimized computerized numerical control(CNC)machining program,and VERICUT software is employed to simulate the machining process,optimize the amount of cutting and inspect the machining quality.The method saves the machining time,guarantees the correctness of NC program,and the overall machining efficiency is improved.The method lays a good theoretical and practical foundation for integration of the similar platform.

NEW ALGORITHMS FOR LINEAR PROGRAMMING

The problem of solving a linear programming is converted into that of solving an uncon-strained maximization problem in which the objective function is concave. Two algorithms areproposed. These two algorithms have very simple structure and can be implemented easily. Forany given precision, the algorithms will terminate in a finite number of steps.

A NEW SUCCESSIVE QUADRATIC PROGRAMMING ALGORITHM

The main difficulties encountered in the successive quadratic programming methods are.the choice of penalty parameter, the choice of steplenth, and the Maratos effect. An algorithmwithout penalty parameters is presented in this paper. The choice of steplength parameters isbased on the method of trust region. Global convergence and local superlinear convergence areproved under suitable assumption.

AN OVERALL STUDY OF CONVERGENCE CONDITIONS FOR ALGORITHMS IN NONLINEAR PROGRAMMING

Since the point-to-set maps were introduced by Zangwill in the study of conceptual algorithms, various sufficient conditions for the algorithms to be of global convergence have been established.In this paper, the relations among all these conditions are illustrated by a unified approach;still more, unlike the sufficient conditions previously given in the literature,a new necessary condition is put forward at the end of the paper, so that it implies more applications.

PREDICTOR-CORRECTOR ALGORITHM FOR CONVEX QUADRATIC PROGRAMMING WITH UPPER BOUNDS

Predictor-corrector algorithm for linear programming, proposed by Mizuno et al.([1]), becomes the best well known in the interior point methods. The purpose of this paper is to extend these results in two directions. First, we modify the algorithm in order to solve convex quadratic programming with upper bounds. Second, we replace the corrector step with an iteration of Monteiro and Adler's algorithm([2]). With these modifications, the duality gap is reduced by a constant factor after each corrector step for convex quadratic programming. It is shown that the new algorithm has a O(root nL)-iteration complexity.

APPROXIMATE LAGRANGE MULTIPLIER ALGORITHM FOR STOCHASTIC PROGRAMS WITH COMPLETE RECOURSE:NONLINEAR DETERMINISTIC CONSTRAINTS

In this paper we design an approximation method for solving stochastic programs with com-plete recourse and nonlinear deterministic constraints. This method is obtained by combiningapproximation method and Lagrange multiplier algorithm of Bertsekas type. Thus this methodhas the advantages of both the two.

Multi-Parameter and Multi-Objective Optimization of Occupant Restraint System in Frontal Collision

To solve the constraints of multi-objective optimization of the driver system and high nonlinear problems, according to the relevant dimensions of a car, we build a simulation model with Hybrid Ⅲ 50th dummy driver constraint system. The comparison of the driver mechanics index of the experimental data with the simulation data in the frontal crash shows that the accuracy of simulation model meets the requirements. The optimal Latin test design is adopted, and the global sensitivity analysis of the design parameters is carried out based on the Kriging model. The four most sensitive parameters are selected, and the parameters are solved by a multi-island genetic algorithm.And then the nonlinear programming quadratic line(NLPQL) algorithm is used to search for accurate optimization. The optimal parameters of the occupant restraint system are determined: the limiting force value of force limiter 2 985.603 N, belt extension 12.684%, airbag point explosion time 27.585 ms, and airbag vent diameter 27.338 mm, with the weighted injury criterion(WIC) decreased by 12.97%, the head injury decreased by 22.60%, and the chest compression decreased by 7.29%. The results show that the system integration of passive safety devices such as seat belts and airbags can effectively protect the driver.