Multiobjective optimization design of Yagi-Uda antenna

An optimization method, such as the steepest gradient methods, could not easily obtain globally optimum solutions for devising antenna design parameters that allow the antenna to simultaneously improve multiple performances such as gain, sidelobe level, and input impedance. The genetic algorithm (GA) is suitable for empirically solving optimization problems and is effective in designing an antenna. In particular, this method can solve the multiobjective optimization problem using various Pareto-optimal solutions in an extremely efficient manner. In this paper, the Pareto GA, by which various Pareto-optimal solutions for each objective function (performance) can be obtained in a single trial of a numerical simulation and which enables the selection of parameters in accordance with the design requirement, is applied to the multiobjective optimization design of the Yagi-Uda antenna. The effectiveness of the Pareto GA was demonstrated by comparing the performances obtained by the Pareto GA with those of the previously reported values, which were obtained by the conventional GA, and with the values of the design benchmark reference.     

Potential of Particle Swarm Optimization and Genetic Algorithms for FIR Filter Design

This article studies the performance of two metaheuristics, particle swarm optimization (PSO) and genetic algorithms (GA), for FIR filter design. The two approaches aim to find a solution to a given objective function but employ different strategies and computational effort to do so. PSO is a more recent heuristic search method than GA; its dynamics exploit the collaborative behavior of biological populations. Some researchers advocate the superiority of PSO over GA and highlight its capacity to solve complex problems thanks to its ease of implementation. In this paper, different versions of PSOs and GAs including our specific GA scheme are compared for FIR filter design. PSO generally outperforms standard GAs in some performance criteria, but our adaptive genetic algorithm is shown to be better on all criteria except CPU runtime. The study also underlines the importance of introducing intelligence in metaheuristics to make them more efficient by embedding self-tuning strategies. Furthermore, it establishes the potential complementarity of the approaches when solving this optimization problem.     

Virtual network function–forwarding graph embedding: A genetic algorithm approach

Network function virtualization (NFV) provides a simple and effective mean to deploy and manage network and telecommunications' services. A typical service can be expressed in the form of a virtual network function–forwarding graph (VNF‐FG). Allocating a VNF‐FG is equivalent to place VNFs and virtual links onto a given substrate network considering resources and quality‐of‐service (QoS) constraints. The deployment of VNF‐FGs in large‐scale networks, such that QoS measures and deployment cost are optimized, is an emerging challenge. Single‐objective VNF‐FGs allocation has been addressed in existing literature; however, there is still a lack of studies considering multiobjective VNF‐FGs allocation. In addition, it is not trivial to obtain optimal VNF‐FGs allocation due to its high computational complexity even in case of single‐objective VNF‐FGs allocation. Genetic algorithms (GAs) have been proved its ability in coping with multiobjective optimization problems; thus, we propose a GA‐based scheme to solve multiobjective VNF‐FGs allocation problem in this paper. The numerical results confirm that the proposed scheme can provide near Pareto‐optimal solutions within a short execution time.     

Optimal apportionment of reliability and redundancy in seriessystems under multiple objectives

A multiobjective reliability apportionment problem for a series system with time-dependent reliability is presented. The resulting mathematical programming formulation determines the optimal level of component reliability and the number of redundant components at each stage. The problem is a multiobjective, nonlinear, mixed-integer mathematical programming problem, subject to several design constraints. Sequential unconstrained minimization techniques in conjunction with heuristic algorithms are used to find an optimum solution. A generalization of the problem in view of inherent vagueness in the objective and the constraint functions results in an ill-structured reliability apportionment problem. This multiobjective fuzzy optimization problem is solved using nonlinear programming. The computational procedure is illustrated through a numerical example. The fuzzy optimization techniques can be useful during initial stages of the conceptual design of engineering systems where the design goals and design constraints have not been clearly identified or stated, and for decision making problems in ill-structured situations     

Design of Multibeam Dielectric Lens Antennas by Multiobjective Optimization

We propose an effective method for designing multibeam dielectric lens antennas. A genetic algorithm (GA) with multiobjective optimization is adopted to balance gain against sidelobe level. The lens shapes and the position of each feed are associated with chromosomes. The gain and sidelobe level are evaluated by a pareto ranking method. The method yields the distribution of the objective function values and the corresponding antenna structures.      

深亚微米条件下的多层VLSI／MCM层分配的遗传算法

本文提出了一个深亚微米条件下的多层ＶＬＳＭＣＭ有约束分层层分配的遗传算法。该算法分为两步：首先进行超层分配，使各线网满足Ｃｒｏｓｓｔａｌｋ约束，且超层数目最少；然后进行各超层的通孔最少化二分层。与目前的层分配算法相比，该遗传算法具有目标全面，全局优化能力强等特点，是一种可应用于深亚微米条件下的ＩＣ ＣＡＤ的有效分层方法。     

基于实数编码遗传算法的稀布阵列综合

针对有孔径和阵元总数约束的线性阵列,提出了一种基于实数编码遗传算法的稀布阵列综合方法。算法中每条染色体基因主要由阵元间距和激励幅度共同组成,采用双变量组合优化的方式为阵列性能优化提供了更多的自由度。采用十进制实数量化编码的方式,省去了二进制编码过程中的解码运算,使算法程序更为简洁,效率更高。以降低阵列方向图的峰值旁瓣电平为目标函数,运用提出的改进遗传算法针对几种不同的线性阵列进行优化仿真,在同等约束条件下将该算法与其他改进遗传算法进行了优化对比,结果表明该算法表现更为出色。     

Optimization of image coding algorithms and architectures usinggenetic algorithms

This paper addresses the application of genetic algorithm (GA)-based optimization techniques to problems in image and video coding, demonstrating the success of GAs when used to solve real design problems with both performance and implementation constraints. Issues considered include problem representation, problem complexity, and fitness evaluation methods. For offline problems, such as the design of two-dimensional filters and filter banks, GAs are shown to be capable of producing results superior to conventional approaches. In the case of problems with real-time constraints, such as motion estimation, fractal search and vector quantization codebook design, GAs can provide solutions superior to those reported using conventional techniques with comparable implementation complexity. The use of GAs to jointly optimize algorithm performance in the context of a selected implementation strategy is emphasized throughout and several design examples are included     

Multiobjective genetic optimization of diagnostic classifiers withimplications for generating receiver operating characteristiccurves

It is well understood that binary classifiers have two implicit objective functions (sensitivity and specificity) describing their performance. Traditional methods of classifier training attempt to combine these two objective functions (or two analogous class performance measures) into one so that conventional scalar optimization techniques can be utilized. This involves incorporating a priori information into the aggregation method so that the resulting performance of the classifier is satisfactory for the task at hand. We have investigated the use of a niched Pareto multiobjective genetic algorithm (GA) for classifier optimization. With niched Pareto GA's, an objective vector is optimized instead of a scalar function, eliminating the need to aggregate classification objective functions. The niched Pareto GA returns a set of optimal solutions that are equivalent in the absence of any information regarding the preferences of the objectives. The a priori knowledge that was used for aggregating the objective functions in conventional classifier training can instead be applied post-optimization to select from one of the series of solutions returned from the multiobjective genetic optimization. We have applied this technique to train a linear classifier and an artificial neural network (ANN), using simulated datasets. The performances of the solutions returned from the multiobjective genetic optimization represent a series of optimal (sensitivity, specificity) pairs, which can be thought of as operating points on a receiver operating characteristic (ROC) curve. All possible ROC curves for a given dataset and classifier are less than or equal to the ROC curve generated by the niched Pareto genetic optimization.     

Optimization of Cylindrical Pin-Fin Heat Sinks Using Genetic Algorithms

In this paper, genetic algorithms (GAs) are applied for the optimization of pin-fin heat sinks. GAs are usually considered as a computational method to obtain optimal solution in a very large solution space. Entropy generation rate due to heat transfer and pressure drop across pin-fins is minimized by using GAs. Analytical/empirical correlations for heat transfer coefficients and friction factors are used in the optimization model, where the characteristic length is used as the diameter of the pin and reference velocity used in Reynolds number and pressure drop is based on the minimum free area available for the fluid flow. Both inline and staggered arrangements are studied and their relative performance is compared on the basis of equal overall volume of heat sinks. It is demonstrated that geometric parameters, material properties, and flow conditions can be simultaneously optimized using GA.     

非线性几何规划的几种算法

介绍了非线性规划的数学模型(即具有不等式约束条件的求解目标函数最优化解的一类优化问题)以及现今求解这类非线性规划问题时,运用最为广泛的罚函数内点算法,同时介绍了解决几何规划问题的两种算法,内点路经跟踪法和序列二次规划法。通过实例,对比了文中所介绍的内点路径跟踪法和序列二次规划法的运算结果,最终给出结论。     

System reliability design with deteriorative components

In constrained optimum system reliability problems, the reliability of each component is usually assumed to be fixed, and the optimal number of redundancies at each stage is determined. However, in real world the component reliability decreases as component deteriorates; i.e. the component reliability is dependent on its age. This paper presents a system reliability optimization problem with deteriorative components. We formulate this problem as a parametric nonlinear integer programming problem where the objective function has a time parameter t. A solution method is proposed for solving it. We believe that this model can provide very useful information for decision makers and reliability designers.     

基于遗传算法的多目标多路径优化选择算法研究

依据遗传算法GA（Genetic Algorithms）基本原理，文章提出一种多目标多路径选择算法，在给定多个目标约束条件下，能够解出多个近优路径，以满足驾驶员不同偏好的路径选择，并对每一目标设计出了相应适应度函数。实验结果证明能有效解决多目标多路径不重叠路径选择问题，能为驾驶员提供更好路径选择满意度。和目前已有其它方法相比，减少了路径搜索计算时间和复杂度。     

基于遗传-细菌觅食组合算法的非线性模型优化

文中提出一种遗传-细菌觅食组合优化算法以解决非线性模型优化问题。该方法先使用遗传算法进行全局搜索,并缩小最优解的搜索范围;再使用细菌觅食优化算法在该局部范围内执行局部搜索。这种组合搜索策略可以增强算法的收敛性,并能有效地均衡全局搜索和局部搜索。文中利用单峰、多峰和复杂多峰等非线性函数模型验证所提算法的性能。实验结果表明,组合算法的计算精度和效率分别比遗传算法和细菌觅食优化算法提高了30%和50%,表明该组合算法具有更快的收敛速度,更高的求解精度,适用于大规模多极值的非线性问题。     

Pareto Optimal Microwave Filter Design Using Multiobjective Differential Evolution

Microwave filters play an important role in modern wireless communications. A novel method for the design of multilayer dielectric and open loop ring resonator (OLRR) filters under constraints is presented. The proposed design method is based on generalized differential evolution (GDE3), which is a multiobjective extension of differential evolution (DE). GDE3 algorithm can be applied for global optimization to any engineering problem with an arbitrary number of objective and constraint functions. GDE3 is compared against other evolutionary multiobjective algorithms like nondominated sorting genetic algorithm-II (NSGA-II), multiobjective particle swarm optimization (MOPSO) and multiobjective particle swarm optimization with fitness sharing (MOPSO-fs) for a number of microwave filter design cases. In the multilayer dielectric filter design case a predefined database of low loss dielectric materials is used. The results indicate the advantages of this approach and the applicability of this design method.      

Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies

Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggregated to a scalar cost function. This can be mainly attributed to the fact that most conventional learning algorithms can only deal with a scalar cost function. Over the last decade, efforts on solving machine learning problems using the Pareto-based multiobjective optimization methodology have gained increasing impetus, particularly due to the great success of multiobjective optimization using evolutionary algorithms and other population-based stochastic search methods. It has been shown that Pareto-based multiobjective learning approaches are more powerful compared to learning algorithms with a scalar cost function in addressing various topics of machine learning, such as clustering, feature selection, improvement of generalization ability, knowledge extraction, and ensemble generation. One common benefit of the different multiobjective learning approaches is that a deeper insight into the learning problem can be gained by analyzing the Pareto front composed of multiple Pareto-optimal solutions. This paper provides an overview of the existing research on multiobjective machine learning, focusing on supervised learning. In addition, a number of case studies are provided to illustrate the major benefits of the Pareto-based approach to machine learning, e.g., how to identify interpretable models and models that can generalize on unseen data from the obtained Pareto-optimal solutions. Three approaches to Pareto-based multiobjective ensemble generation are compared and discussed in detail. Finally, potentially interesting topics in multiobjective machine learning are suggested.     

Reliability optimization of series-parallel systems using a genetic algorithm

A problem-specific genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine the optimal design configuration when there are multiple component choices available for each of several k-out-of-n:G subsystems. The problem is to select components and redundancy-levels to optimize some objective function, given system-level constraints on reliability, cost, and/or weight. Previous formulations of the problem have implicit restrictions concerning the type of redundancy allowed, the number of available component choices, and whether mixing of components is allowed. GA is a robust evolutionary optimization search technique with very few restrictions concerning the type or size of the design problem. The solution approach was to solve the dual of a nonlinear optimization problem by using a dynamic penalty function. GA performs very well on two types of problems: (1) redundancy allocation originally proposed by Fyffe, Hines, Lee, and (2) randomly generated problem with more complex k-out-of-n:G configurations.     

A new control technique for active power filters using a combinedgenetic algorithm/conventional analysis

In this paper, the computational problems associated with the optimization techniques used to evaluate the switching patterns for controlling variable-characteristics active power filters are presented and critically analyzed. Genetic algorithms (GAs) are introduced in this paper to generate a fast and accurate initial starting point in the highly nonlinear optimization space of mathematical optimization techniques. GAs tend to speed up the initialization process by a factor of 13. A combined GA/conventional technique is also proposed and implemented to reduce the associated computational burden associated with the control and, consequently, increasing the speed of response of this class of active filters. Comparisons of these techniques are discussed and presented in conjunction with simulation and practical results for the filter operation     

Circuit performance optimization and model fitting based on simulated annealing

An efficient new method, based on the coupling between an enhanced simulated annealing algorithm and the SPICE-PAC ‘open’ circuit simulator, is proposed for minimizing objective functions describing circuit performance optimization problems or component model fitting to experimental data. To keep the number of objective function evaluations and CPU times to the lowest possible level, we focus our attention on two features: first, we build an original partitioning technique for splitting large n-dimensional problems; then we carefully study variables discretization, (which is necessary for applying the simulated annealing method to continuous problems). To illustrate the efficiency of our method, we show how to determine the 40 MOS transistor model parameters, through fitting the model to experimental data.     

Multiobjective genetic algorithm applied to aerodynamic design ofcascade airfoils

A multiobjective genetic algorithm (GA) based on Fonseca-Fleming's Pareto-based ranking and fitness-sharing techniques has been applied to aerodynamic shape optimization of cascade airfoil design. Airfoil performance is evaluated by a Navier-Stokes code. Evaluation of GA population is parallelized on the Numerical Wind Tunnel, a parallel vector machine. The present multiobjective design seeks high pressure rise, high flow turning angle, and low total pressure loss at a low Mach number. Pareto solutions that perform better than existing control diffusion airfoils were obtained