An important issue in multiobjective optimization is the study of the convergence speed of algorithms. An optimization problem must be defined as simple as possible to minimize the computational cost required to solve...
详细信息
An important issue in multiobjective optimization is the study of the convergence speed of algorithms. An optimization problem must be defined as simple as possible to minimize the computational cost required to solve it. In this work, we study the convergence speed of seven multiobjective evolutionary algorithms: DEPT, MO-VNS, MOABC, MO-GSA, MO-FA, NSGA-II, and SPEA2;when solving an important biological problem: the motif discovery problem. We have used twelve instances of four different organisms as benchmark, analyzing the number of fitness function evaluations required by each algorithm to achieve reasonable quality solutions. We have used the hypervolume indicator to evaluate the solutions discovered by each algorithm, measuring its quality every 100 evaluations. This methodology also allows us to study the hit rates of the algorithms over 30 independent runs. Moreover, we have made a deeper study in the more complex instance of each organism. In this study, we observe the increase of the archive (number of non-dominated solutions) and the spread of the Pareto fronts obtained by the algorithm in the median execution. As we will see, our study reveals that DEPT, MOABC, and MO-FA provide the best convergence speeds and the highest hit rates.
Chaotic maps play an important role in improving evolutionary algorithms (EAs) for avoiding the local optima and speeding up the convergence. However, different chaotic maps in different phases have different effects ...
详细信息
Chaotic maps play an important role in improving evolutionary algorithms (EAs) for avoiding the local optima and speeding up the convergence. However, different chaotic maps in different phases have different effects on EAs. This paper focuses on exploring the effects of chaotic maps and giving comprehensive guidance for improving multiobjective evolutionary algorithms (MOEAs) by series of experiments. NSGA-II algorithm, a representative of MOEAs using the nondominated sorting and elitist strategy, is taken as the framework to study the effect of chaotic maps. Ten chaotic maps are applied in MOEAs in three phases, that is, initial population, crossover, and mutation operator. Multiobjective problems (MOPs) adopted are ZDT series problems to show the generality. Since the scale of some sequences generated by chaotic maps is changed to fit for MOPs, the correctness of scaling transformation of chaotic sequences is proved by measuring the largest Lyapunov exponent. The convergence metric.. and diversity metric. are chosen to evaluate the performance of new algorithms with chaos. The results of experiments demonstrate that chaotic maps can improve the performance of MOEAs, especially in solving problems with convex and piecewise Pareto front. In addition, cat map has the best performance in solving problems with local optima.
This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs). Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization) alg...
详细信息
This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs). Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization) algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization systemin the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.
A feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, technique...
详细信息
A feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the use of random feature models. However, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this article, we propose to model the problem of finding computationally hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm for optimized feature models (ETHOM). Given a tool and an analysis operation, ETHOM generates input models of a predefined size maximizing aspects such as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the performance of tools in pessimistic cases providing a better idea of their real power and revealing performance bugs. Experiments using ETHOM on a number of analyses and tools have successfully identified models producing much longer executions times and higher memory consumption than those obtained with random models of identical or even larger size. (C) 2013 Elsevier Ltd. All rights reserved.
In this paper, we define spiking neural P circuits (SN P circuits) as an acyclic variant of spiking neural P systems. We then study how well genetic algorithms (GA) are able to find an SN P circuit that computes a giv...
详细信息
In this paper, we define spiking neural P circuits (SN P circuits) as an acyclic variant of spiking neural P systems. We then study how well genetic algorithms (GA) are able to find an SN P circuit that computes a given Boolean function, possibly partially defined. The proposed technique can be used to find SN P circuits that solve binary classification problems. We performed several computer experiments, testing different mutation operators and several combinations of hyperparameter values. The preliminary results obtained show that the probability of success of GA strongly depends upon the structure (in particular, the algebraic degree and the number of input/output variables) of the Boolean function to be computed.
The problem of finding a cohesive subgraph, notably the s-club model, which is a subgraph with diameter at most s, is a widely applied topic in social network analysis and group of objects modeling. In particular, the...
详细信息
The problem of finding a cohesive subgraph, notably the s-club model, which is a subgraph with diameter at most s, is a widely applied topic in social network analysis and group of objects modeling. In particular, the minimum s-club cover problem (min s-club cover) is a recently introduced variant in the literature which asks to cover the vertices of a graph with a minimum number of s-clubs. The existence of common connections among these highly connected components encourages the application of multitasking optimization to leverage the shared meaningful knowledge in the discovery of multiple s-clubs at the same time. Therefore, this study proposes a multitasking evolutionary algorithm to solve the minimum s-club cover problem. Our proposal is designed with an effective solution representation method and evolutionary operators for the variation in the number of clubs. Each solution is represented by two components, where the gene number of a component can be different for each individual and can change during performing evolutionary operations. We also propose a solution generation method based on a random greedy algorithm that helps to ensure individual quality and population diversity in the initial population. The proposed algorithm is evaluated on two datasets in the DIMACS library. This study then analyzed the influence of different factors of the input data on the proposed algorithm results. Based on statistical analysis of the performance results, it is clear that our proposal's solution is superior to an existing algorithm on two-thirds of the experimental data set.
This paper tackles the optimization of a stand-alone hybrid photovoltaic-batteries-hydrogen (PV-hydrogen) system, using an evolutionary algorithm. Specifically, a stand alone power system for feeding a remote telecomm...
详细信息
This paper tackles the optimization of a stand-alone hybrid photovoltaic-batteries-hydrogen (PV-hydrogen) system, using an evolutionary algorithm. Specifically, a stand alone power system for feeding a remote telecommunications facility is studied. The considered system is specifically designed to cover the power necessities of remote, isolated telecommunications facilities, so it must be able to work in an unattended way during a long time period. On the other hand, if maintenance visits are scheduled, it is intuitive that the cost of the stand alone system could be reduced. Thus, two different optimization problems have been considered in this work. The first one consists in the obtention of the optimal number, distribution (two different arrays of batteries must be fed) and disposition (slope and azimuth) of the PV panels in the facility, for the case of autonomous operation of the telecommunication system during at least two years. The second problem considered consists of scheduling a maintenance visit per year, where a technician is able to reconfigure the system. In this case, the problem consists of obtaining the optimal number, distribution, disposition of the PV panels, and also the time of the year where the maintenance visit should take place. An evolutionary algorithm, able to tackle both problems with very few changes, is described in this paper. The proposed evolutionary algorithm has been analyzed in a simulation of a real PV-hydrogen system sited at National Spanish Institute for Aerospace Technology (INTA), at Torrejon de Ardoz, Madrid, Spain. The well-known software TRNSYS has been used in order to simulate the behavior of this PV-hydrogen system. Several simulations of the system recreating different weather conditions of three Spanish cities (Madrid, Barcelona and La Coruna) have been carried out, and a comparative analysis of the results obtained by the evolutionary algorithm has been done. The results obtained in the first problem tackled show
The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches of computer sc...
详细信息
The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches of computer science due to its financial, ecological, political, and technical consequences. One of the answers is given by scheduling combined with dynamic voltage scaling technique to optimize the energy consumption. The way of reasoning is based on the link between current semiconductor technologies and energy state management of processors, where sacrificing the performance can save energy. This paper is devoted to investigate and solve the multi-objective precedence constrained application scheduling problem on a distributed computing system, and it has two main aims: the creation of general algorithms to solve the problem and the examination of the problem by means of the thorough analysis of the results returned by the algorithms. The first aim was achieved in two steps: adaptation of state-of-the-art multi-objective evolutionary algorithms by designing new operators and their validation in terms of performance and energy. The second aim was accomplished by performing an extensive number of algorithms executions on a large and diverse benchmark and the further analysis of performance among the proposed algorithms. Finally, the study proves the validity of the proposed method, points out the best-compared multi-objective algorithm schema, and the most important factors for the algorithms performance. (C) 2014 Elsevier B.V. All rights reserved.
In this article, a new fitness assignment scheme to evaluate the Pareto-optimal solutions for multi-objective evolutionary algorithms is proposed. The proposed DOmination Power of an individual Genetic Algorithm (DOPG...
详细信息
In this article, a new fitness assignment scheme to evaluate the Pareto-optimal solutions for multi-objective evolutionary algorithms is proposed. The proposed DOmination Power of an individual Genetic Algorithm (DOPGA) method can order the individuals in a form in which each individual (the so-called solution) could have a unique rank. With this new method, a multi-objective problem can be treated as if it were a single-objective problem without drastically deviating from the Pareto definition. In DOPGA, relative position of a solution is embedded into the fitness assignment procedures. We compare the performance of the algorithm with two benchmark evolutionary algorithms (Strength Pareto evolutionary Algorithm (SPEA) and Strength Pareto evolutionary Algorithm 2 (SPEA2)) on 12 unconstrained bi-objective and one tri-objective test problems. DOPGA significantly outperforms SPEA on all test problems. DOPGA performs better than SPEA2 in terms of convergence metric on all test problems. Also, Pareto-optimal solutions found by DOPGA spread better than SPEA2 on eight of 13 test problems.
Markerless Human Motion Capture is the problem of determining the joints' angles of a three-dimensional articulated body model that best matches current and past observations acquired by video cameras. The problem...
详细信息
Markerless Human Motion Capture is the problem of determining the joints' angles of a three-dimensional articulated body model that best matches current and past observations acquired by video cameras. The problem of Markerless Human Motion Capture is high-dimensional and requires the use of models witha considerable number of degrees of freedom to appropriately adapt to the human anatomy. Particle filters have become the most popular approach for Markerless Human Motion Capture, despite their difficulty to cope with high-dimensional problems. Although several solutions have been proposed to improve their performance, they still suffer from the curse of dimensionality. As a consequence, it is normally required to impose mobility limitations in the body models employed, or to exploit the hierarchical nature of the human skeleton by partitioning the problem into smaller ones. evolutionary algorithms, though, are powerful methods for solving continuous optimization problems, specially the high-dimensional ones. Yet, few works have tackled Markerless Human Motion Capture using them. This paper evaluates the performance of three of the most competitive algorithms in continuous optimization - Covariance Matrix Adaptation evolutionary Strategy, Differential Evolution and Particle Swarm Optimization - with two of the most relevant particle filters proposed in the literature, namely the Annealed Particle Filter and the Partitioned Sampling Annealed Particle Filter. The algorithms have been experimentally compared in the public dataset HumanEva-I by employing two body models with different complexities. Our work also analyzes the performance of the algorithms in hierarchical and holistic approaches, i.e., with and without partitioning the search space. Non-parametric tests run on the results have shown that: (i) the evolutionary algorithms employed outperform their particle filter counterparts in all the cases tested;(ii) they can deal with high-dimensional models thus leadin
暂无评论