Single-objective Optimization Techniques Research Articles

A Systematic Approach to Jointly Optimize Rate and Power Consumption for OFDM Systems

In this paper, we adopt a multiobjective optimization for the bit and power allocation problem in order to meet the requirements of emerging wireless systems, i.e., achieving higher throughput without considerably increasing the transmit power. More specifically, we propose to simultaneously maximize the throughput and minimize the transmit power of an OFDM system subject to average bit error rate (BER), power budget, and maximum allocated number of bits per subcarrier constraints. The formulated optimization problem is not convex and we use an evolutionary algorithm, i.e., genetic algorithm, in order to obtain the solution. We study the structure of the problem and the obtained solution and notice that the constraint on the average BER can be replaced by a BER per subcarrier constraint. As such, we propose an approximate non-convex optimization problem. We further exploit the structure of the approximate optimization problem and notice that the BER constraint per subcarrier (i.e., the source of the non-convexity) must be satisfied with an equality sign and can be substituted; this leads to an equivalent convex optimization problem where the global optimality of the Pareto solutions is guaranteed. Closed-form expressions are obtained for the bit and power allocations with reduced complexity. Simulation results show that the proposed multiobjective optimization approach provides significant performance improvements over single objective optimization techniques presented in the literature, without incurring additional complexity.

Superdefect Photonic Crystal Filter Optimization Using Grey Wolf Optimizer

This letter proposes a new method for designing photonic crystal (PhC) filters. The method is proposed while designing novel superdefect PhC filters with three and five ellipse-shaped rods. The main idea of the method is to formulate the optical filter design problem as an optimization problem and to solve it by single-objective optimization techniques. This method bypasses discovering the complex relationship between the output spectral transmission performance and the radii of rods. The results demonstrate the merits of the method in facilitating the design process of the PhC filters and finding high-performance designs.

ADAPTIVE ZONING DESIGN BY SUPERVISED LEARNING USING MULTI-OBJECTIVE OPTIMIZATION

Zoning is a widespread feature extraction technique for handwritten digit recognition, since it is able to handle handwritten pattern variability. Static techniques for zoning design have recently been superseded by adaptive techniques, in which zoning design is considered as the result of an optimization procedure. This paper presents a new learning strategy to optimal zoning design using multi-objective genetic algorithm. More precisely, the nondominant sorting genetic algorithm II (NSGA II) has been applied to define, in a single process, both the optimal number of zones and the optimal zones for the Voronoi-based zoning method. The experimental tests, carried out in the field of handwritten digit recognition, show the effectiveness of this new approach with respect to traditional dynamic approaches for zoning design, based on single-objective optimization techniques.

Modeling and multiple performance optimization of ultrasonic micro-hole machining of PCD using fuzzy logic and taguchi quality loss function

Polycrystalline diamond is an ideal material for parts with micro-holes and has been widely used as dies and cutting tools in automotive, aerospace and woodworking industries due to its superior wear and corrosion resistance. In this research paper, the modeling and simultaneous optimization of multiple performance characteristics such as material removal rate and surface roughness of polycrystalline diamond (PCD) with ultrasonic machining process has been presented. The fuzzy logic and taguchi's quality loss function has been used. In recent years, fuzzy logic has been used in manufacturing engineering for modeling and monitoring. Also the effect of controllable machining parameters like type of abrasive slurry, their size and concentration, nature of tool material and the power rating of the machine has been determined by applying the single objective and multi-objective optimization techniques. The analysis of results has been done using the MATLAB 7.5 software and results obtained are validated by conducting the confirmation experiments. The results show the considerable improvement in S/N ratio as compared to initial cutting conditions. The surface roughness of machined surface has been measured by using the Perthometer (M4Pi, Mahr Germany).

Multiobjective Optimization of Circumferential Casing Grooves for a Transonic Axial Compressor

R ECENTLY, casing treatments using grooves or slots have been applied to axial compressors to prevent surge and enhance stall margin (SM). However, this is accompanied by a loss near the blade tip region, which leads to a decrease in efficiency of the axial compressor. Therefore, a systematic design of the casing treatment using multiobjective optimization techniques in conjunction with a flow analysis using three-dimensional Reynolds-averaged Navier– Stokes equations (RANS) was used to investigate a compromise between the SM and the efficiency of the compressor. Various types of grooves have been suggested by many researchers, and it has been reported that using grooves increases SM by delaying stall. Huang et al. [1] studied the effects of the configuration, width, and depth of circumferential groove casing treatment on SMby numerical analysis, and suggested that the stall mechanism was substantially influenced by tip clearance.Muller et al. [2] studied the effects of the number and depth of the grooves on SM by threedimensional numerical analysis. Houghton and Day [3] examined the effect of grooves on rotor outflow blockage, and the near casing flowfield was studied using experimental and computational methods. Many engineering designs involve multiple disciplines and simultaneous optimization of multiple objectives related to each discipline. These design problems, usually known as multiobjective problems, require simultaneous consideration of all objective functions to optimize the system. The nondominated sorting genetic algorithm (NSGA-2) by Deb [4] generates a Pareto-optimal solution using an evolutionary algorithm. Shape optimization of a hydraulic turbine diffuser was performed with multiple objectives by Marjavaara et al. [5]. Kim et al. [6] reported a multiobjective optimization of a centrifugal compressor impeller with four design variables that defined the impeller hub and shroud contours in meridian terms. Samad and Kim [7] reviewed the performance of the surrogate models applied to turbomachinery design optimization, and introducedmulti and single-objective optimization techniques in conjunction with three-dimensional RANS analysis. In this study, based on previous work [8] for a single-objective optimization, a hybridmultiobjective evolutionary algorithm (hybrid MOEA) [9] coupledwith the Kriging (KRG)model [10] was applied to obtain a global Pareto-optimal front for the design of circumferential casing grooves in an axial compressor with NASARotor 37 [11]. To the best of the authors’ knowledge, no previous study has reported on the optimization of the design of circumferential casing grooves. The present multiobjective optimization was motivated to provide an efficient tool for the design of an axial compressor with circumferential casing groove treatment, and it is expected for designers to meet their design requirements with regard to the SM and adiabatic efficiency from the Pareto-optimal designs (PODs) obtained in this work. Numerical solutions at the selected design points were obtained by three-dimensional RANS analysis. The shape of the grooves was optimized by considering width and depth as designvariables in order to improve the SM and the peak adiabatic efficiency. The tradeoff between these two competing objective functions is explored and discussed with respect to the design variables.

Overview of NSGA-II for Optimizing Machining Process Parameters

This paper presents an overview on NSGA-II optimization techniques of machining process parameters. There are many multi objective optimization (MoGA) techniques involved in machining process parameters optimization including multi-objective genetic algorithm (MOGA), strength Pareto evolutionary algorithm (SPEA), micro genetic algorithm (Micro-GA), Pareto-archived evolution strategy (PAES), etc. This paper reviews the application of non dominated sorting genetic algorithm II (NSGA-II), classified as one of MoGA techniques, for optimizing process parameters in various machining operations. NSGA-II is a well known, fast sorting and elite multi objective genetic algorithm. Process parameters such as cutting speed, feed rate, rotational speed etc. are the considerable conditions in order to optimize the machining operations in minimizing or maximizing the machining performances. Unlike the single objective optimization technique, NSGA-II simultaneously optimizes each objective without being dominated by any other solution.

Multiobjective optimization of synthesis gas production using non-dominated sorting genetic algorithm

Synthesis gas finds wide applications in various chemical industries. Various routes for manufacture of synthesis gas have been reported such as steam reforming of methane, carbon dioxide reforming of methane, partial oxidation of methane and combination of both carbon dioxide reforming and partial oxidation of methane. However, very few studies have been reported on optimization of process parameters for synthesis gas production. These processes have multiple objective functions that are conflicting in nature and hence use of single objective optimization technique is not suitable. In this study therefore real parameter non-dominated sorting genetic algorithm has been used to obtain a Pareto optimal set of process parameters for production of synthesis gas from combined carbon dioxide reforming and partial oxidation of natural gas over a Pt/γ-Al 2O 3 catalyst. The objectives are to maximize the conversion of methane, maximize the selectivity of carbon monoxide and maintain the hydrogen to carbon monoxide mole ratio at approximately 1. The variables that have been taken are temperature, gas hourly space velocity and the oxygen to methane mole ratio. The results have been compared with that reported by other authors.

Multiobjective Optimization for Pavement Maintenance Programming

Pavement maintenance planning and programming requires optimization analysis involving multiobjective considerations. Traditionally single-objective optimization techniques have been employed by pavement researchers and practitioners because of the complexity involved in multiobjective analysis. This paper develops a genetic-algorithm-based procedure for solving multiobjective network level pavement maintenance programming problems. The concepts of Pareto optimal solution set and rank-based fitness evaluation, and two methods of selecting an optimal solution, were adopted. It was found that the robust search characteristics and multiple-solution handling capability of genetic-algorithms were well suited for multiobjective optimization analysis. Formulation and development of the solution algorithm were described and demonstrated with a numerical example problem in which a hypothetical network level pavement maintenance programming analysis was performed for two- and three-objective optimization, respectively. A comparison between the two- and three-objective solutions was made to highlight some practical considerations in applying multiobjective optimization to pavement maintenance management.

Interactive Sequential Goal Programming

This paper introduces a new solution method based on Goal Programming for Multiple Objective Decision Making (MODM) problems. The method, called Interactive Sequential Goal Programming (ISGP), combines and extends the attractive features of both Goal Programming and interactive solution approaches for MODM problems. ISGP is applicable to both linear and non-linear problems. It uses existing single objective optimization techniques and, hence, available computer codes utilizing these techniques can be adapted for use in ISGP. The non-dominance of the best-compromise solution is assured. The information required from the decision maker in each iteration is simple. The proposed method is illustrated by solving a nutrition problem.