进化高维多目标优化算法研究综述

首先针对常规多目标优化算法求解高维多目标优化时面临的选择压力衰减问题进行论述;然后针对该问题,按照选择机制的不同详细介绍基于Pareto支配、基于分解策略和基于性能评价指标的典型高维多目标优化算法,并分析各自的优缺点;接着立足于一种全新的性能评价指标-----R2指标,给出R2指标的具体定义,介绍基于R2指标的高维多目标优化算法,分析此类算法的本质,并按照R2指标的4个关键组成部分进行综述;最后,发掘其存在的潜在问题以及未来发展空间.     

Comparison of many-objective evolutionary algorithms using performance metrics ensemble

In this study, we have thoroughly researched on performance of six state-of-the-art Multiobjective Evolutionary Algorithms (MOEAs) under a number of carefully crafted many-objective optimization benchmark problems. Each MOEA apply different method to handle the difficulty of increasing objectives. Performance metrics ensemble exploits a number of performance metrics using double elimination tournament selection and provides a comprehensive measure revealing insights pertaining to specific problem characteristics that each MOEA could perform the best. Experimental results give detailed information for performance of each MOEA to solve many-objective optimization problems. More importantly, it shows that this performance depends on two distinct aspects: the ability of MOEA to address the specific characteristics of the problem and the ability of MOEA to handle high-dimensional objective space.     

区间参数高维多目标集合进化优化方法

区间参数高维多目标优化问题是现实生活中常见的一类优化问题,但其有效的求解方法并不是很多.对此,利用集合的概念,提出一种求解此类问题的新方法.首先,利用衡量解集收敛性、分布性、多样性的3种性能指标将原优化问题降为3目标优化问题;其次,采用集合Pareto占优关系和不确定测度来区分转化后优化问题解的优劣;再次,设计自适应变化的交叉、变异概率以提高种群的全局和局部搜索能力;最后,利用4种基准函数优化问题,对所提出方法和对比方法进行测试.测试结果显示,除了收敛性,所提出方法得到的Pareto解集的不确定性、多样性、分布性均优于对比方法.     

A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery

When solving a wide range of complex scenarios of a given optimization problem, it is very difficult, if not impossible, to develop a single technique or algorithm that is able to solve all of them adequately. In this case, it is necessary to combine several algorithms by applying the most appropriate one in each case. Parallel computing can be used to improve the quality of the solutions obtained in a cooperative algorithms model. Exchanging information between parallel cooperative algorithms will alter their behavior in terms of solution searching, and it may be more effective than a sequential metaheuristic. For demonstrating this, a parallel cooperative team of four multiobjective evolutionary algorithms based on OpenMP is proposed for solving different scenarios of the Motif Discovery Problem (MDP), which is an important real-world problem in the biological domain. As we will see, the results show that the application of a properly configured parallel cooperative team achieves high quality solutions when solving the addressed problem, improving those achieved by the algorithms executed independently for a much longer time.     

A radial space division based evolutionary algorithm for many-objective optimization

In evolutionary many-objective optimization, diversity maintenance plays an important role in pushing the population towards the Pareto optimal front. Existing many-objective evolutionary algorithms mainly focus on convergence enhancement, but pay less attention to diversity enhancement, which may fail to obtain uniformly distributed solutions or fall into local optima. This paper proposes a radial space division based evolutionary algorithm for many-objective optimization, where the solutions in high-dimensional objective space are projected into the grid divided 2-dimensional radial space for diversity maintenance and convergence enhancement. Specifically, the diversity of the population is emphasized by selecting solutions from different grids, where an adaptive penalty based approach is proposed to select a better converged solution from the grid with multiple solutions for convergence enhancement. The proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms on a variety of benchmark test problems. Experimental results demonstrate the competitiveness of the proposed algorithm in terms of both convergence enhancement and diversity maintenance.     

A generic model for software architectures

In other industries, the idea of build corporate culture by establishing a common level of “best practice” is widely known and used. The architecture concept directly supports this goal for our industry and can help us improve problem areas dominated by organizational and social issues, such as health care organizations, educational systems, and so on. Our proposed reference model for architecture specification and development is organized around a set of aspects that structure concepts and rules; these, in turn, specify a conceptual architecture. We have added principles and guidelines to the concepts and rules to give a more complete picture of the architecture and to provide a place to store and communicate successfully applied design patterns and other knowledge related to the architecture. Adding architectural elements is a step toward a more constructive type of architecture representation. Our current research is focused on further refining these concepts and developing a formal specification of the architecture reference model. We are continuing to test our ideas in case studies, such as applying our model to the OSCA architecture and the application machine concept. We are also developing a prototype architecture editor, and we are testing different tools to learn more about integrating them into a real infrastructure and to learn what typical services an infrastructure must provide     

A comparative study of hardware architectures for lightweight block ciphers

A hardware-based performance comparison of lightweight block ciphers is conducted in this paper. The DESL, DESXL, CURUPIRA-1, CURUPIRA-2, HIGHT, PUFFIN, PRESENT and XTEA block ciphers have been employed in this comparison. Our objective is to survey what ciphers are suitable for security in Radio Frequency Identification (RFID) and other security applications with demanding area restrictions. A general architecture option has been followed for the implementation of all ciphers. Specifically, a loop architecture has been used, where one basic round is used iteratively. The basic performance metrics are the area, power consumption and hardware resource cost associated with the implementation resulting throughput of each cipher. The most compact cipher is the 80-bit PRESENT block cipher with a count of 1704 GEs and 206.4 Kbps, while the largest in area cipher is the CURUPIRA-1. The CURUPIRA-1 cipher consumes the highest power of 118.1 μW, while the PRESENT cipher consumes the lowest power of 20 μW. All measurements have been taken at a 100 kHz clock frequency.     

A coordinated many-objective evolutionary algorithm using random adaptive parameters

Applied Intelligence - Selection strategy is an essential evolutionary component for many-objective evolutionary algorithms, including mating selection and environmental selection. However, there...     

A hybrid greedy indicator- and Pareto-based many-objective evolutionary algorithm

Applied Intelligence - As most of Multi-Objective Evolutionary Algorithms (MOEAs) scale quite poorly when the number of objective functions increases, new strategies have been proposed to face this...     

Stability assessment of aspect-oriented software architectures: A quantitative study

Design of stable software architectures has increasingly been a deep challenge to software developers due to the high volatility of their concerns and respective design decisions. Architecture stability is the ability of the high-level design units to sustain their modularity properties and not succumb to modifications. Architectural aspects are new modularity units aimed at improving design stability through the modularization of otherwise crosscutting concerns. However, there is no empirical knowledge about the positive and negative influences of aspectual decompositions on architecture stability. This paper presents an exploratory analysis of the influence exerted by aspect-oriented composition mechanisms in the stability of architectural modules addressing typical crosscutting concerns, such as error handling and security. Our investigation encompassed a comparative analysis of aspectual and non-aspectual decompositions based on different architectural styles applied to an evolving multi-agent software architecture. In particular, we assessed various facets of components’ and compositions’ stability through such alternative designs of the same multi-agent system using conventional quantitative indicators. We have also investigated the key characteristics of aspectual decompositions that led to (in)stabilities being observed in the target architectural options. The evaluation focused upon a number of architecturally-relevant changes that are typically performed through real-life maintenance tasks.     

An improved evolutionary algorithm for handling many-objective optimization problems

It has been shown that the multi-objective evolutionary algorithms (MOEAs) act poorly in solving many-objective optimization problems which include more than three objectives. The research emphasis, in recent years, has been put into improving the MOEAs to enable them to solve many-objective optimization problems efficiently. In this paper, we propose a new composite fitness evaluation function, in a novel way, to select quality solutions from the objective space of a many-objective optimization problem. Using this composite function, we develop a new algorithm on a well-known NSGA-II and call it FR-NSGA-II, a fast reference point based NSGA-II. The algorithm is evaluated for producing quality solutions measured in terms of proximity, diversity and computational time. The working logic of the algorithm is explained using a bi-objective linear programming problem. Then we test the algorithm using experiments with benchmark problems from DTLZ family. We also compare FR-NSGA-II with four competitive algorithms from the extant literature to show that FR-NSGA-II will produce quality solutions even if the number of objectives is as high as 20.     

A many-objective evolutionary algorithm based on a projection-assisted intra-family election

In recent years, many researchers have put emphasis on the study of how to keep a good balance between convergence and diversity in many-objective optimization. This paper proposes a new many-objective evolutionary algorithm based on a projection-assisted intra-family election. In the proposed algorithm, basic evolution directions are adaptively generated according to the current population and potential evolution directions are excavated in each individual's family. Based on these evolution directions, a strategy of intra-family election is performed in every family and elite individuals are elected as representatives of the specific family to join the next stage, which can enhance the convergence of the algorithm. Moreover, a selection procedure based on angles is used to maintain the diversity. The performance of the proposed algorithm is verified and compared with several state-of-the-art many-objective evolutionary algorithms on a variety of well-known benchmark problems ranging from 5 to 20 objectives. Empirical results demonstrate that the proposed algorithm outperforms other peer algorithms in terms of both the diversity and the convergence of the final solutions set on most of the test instances. In particular, our proposed algorithm shows obvious superiority when handling the problems with larger number of objectives.     

A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization

For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition based evolutionary algorithm with uniform designs is proposed to achieve the goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and the size of the weight vectors neither increases nonlinearly with the number of objectives nor considers a formulaic setting. A crossover operator based on the uniform design method is constructed to enhance the search capacity of the proposed algorithm. Moreover, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on six benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence.     

A comparative study of the multi-objective optimization algorithms for coal-fired boilers

Combustion optimization has been proved to be an effective way to reduce the NOx emissions and unburned carbon in fly ash by carefully setting the operational parameters of boilers. However, there is a trade-off relationship between NOx emissions and the boiler economy, which could be expressed by Pareto solutions. The aim of this work is to achieve multi-objective optimization of the coal-fired boiler to obtain well distributed Pareto solutions. In this study, support vector regression (SVR) was employed to build NOx emissions and carbon burnout models. Thereafter, the improved Strength Pareto Evolutionary Algorithm (SPEA2), the new Multi-Objective Particle Swarm Optimizer (OMOPSO), the Archive-Based hYbrid Scatter Search method (AbYSS), and the cellular genetic algorithm for multi-objective optimization (MOCell) were used for this purpose. The results show that the hybrid algorithms by combining SVR can obtain well distributed Pareto solutions for multi-objective optimization of the boiler. Comparison of various algorithms shows MOCell overwhelms the others in terms of the quality of solutions and convergence rate.     

A comparative study of staff removal algorithms

This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization based approach. We define three different error metrics, compare the algorithms with respect to these metrics and measure their robustness with respect to certain image defects. Our test images are computer-generated scores on which we apply various image deformations typically found in real-world data. In addition to modern western music notation our test set also includes historic music notation such as mensural notation and lute tablature. Our general approach and evaluation methodology is not specific to staff removal, but applicable to other segmentation problems as well.     

A comparative study of image processing algorithms

We compared the performances of the well-known image processing algorithms run on a set of reference images. For this, the algorithm-distorted images are compared against “ground truth” images.     

A Two-Engine interaction driven many-objective evolutionary algorithm with feasibility-aware adaptation

The infeasible parts of the objective space in many-objective optimization problems make evolutionary algorithms face difficulties in obtaining proximity and maintaining diversity simultaneously. This paper proposes a Two-Engine interaction driven many-objective Evolutionary Algorithm with feasibility-aware adaptation (TEEA) that adapts the reference vectors and evolves the population towards the true Pareto Front (PF). The two interacting engines make reference vectors always approximately evenly distributed within the current PF for providing appropriate guidance for selection. The current PF is tracked by maintaining an Individual Archive (IA) of undominated individuals, and the adaptation of reference vectors is conducted with the help of a Reference Archive (RA) that contains layers of reference vectors corresponding to different density. On CEC’2018 benchmark functions with competition standards, the experimental results of the proposed TEEA have demonstrated the expected characteristics and competitive performance.     

A tool for multiobjective evolutionary algorithms

This paper introduces a software tool based on illustrative applications for the development, analysis and application of multiobjective evolutionary algorithms. The multiobjective evolutionary algorithms tool (MOEAT) written in C# using a variety of multiobjective evolutionary algorithms (MOEAs) offers a powerful environment for various kinds of optimization tasks. It has many useful features such as visualizing of the progress and the results of optimization in a dynamic or static mode, and decision variable settings. The performance measurements of well-known multiobjective evolutionary algorithms in MOEAT are done using benchmark problems. In addition, two case studies from engineering domain are presented.     

A comparative study for estimating software development effort intervals

Software cost/effort estimation is still an open challenge. Many researchers have proposed various methods that usually focus on point estimates. Until today, software cost estimation has been treated as a regression problem. However, in order to prevent overestimates and underestimates, it is more practical to predict the interval of estimations instead of the exact values. In this paper, we propose an approach that converts cost estimation into a classification problem and that classifies new software projects in one of the effort classes, each of which corresponds to an effort interval. Our approach integrates cluster analysis with classification methods. Cluster analysis is used to determine effort intervals while different classification algorithms are used to find corresponding effort classes. The proposed approach is applied to seven public datasets. Our experimental results show that the hit rate obtained for effort estimation are around 90–100%, which is much higher than that obtained by related studies. Furthermore, in terms of point estimation, our results are comparable to those in the literature although a simple mean/median is used for estimation. Finally, the dynamic generation of effort intervals is the most distinctive part of our study, and it results in time and effort gain for project managers through the removal of human intervention.