In this paper we expound, for the first time, the application of the Strength Pareto evolutionary Algorithm to the multi-objective design of isolated hybrid systems. The design is posed as an optimisation problem whos...
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In this paper we expound, for the first time, the application of the Strength Pareto evolutionary Algorithm to the multi-objective design of isolated hybrid systems. The design is posed as an optimisation problem whose solution allows obtaining the configuration of the system as well as the control strategy that simultaneously minimises both the total cost through the useful life of the installation and the pollutant emissions. As an example, we have designed a PV-wind-diesel system for two different load profiles, obtaining a set of possible solutions from which the designer can choose those which he prefers considering the costs and pollutant emissions of each one of them. The reached results demonstrate the practical utility of the design method used. (c) 2005 Elsevier Ltd. All rights reserved.
The Colombian coffee supply network, managed by the Federacion Nacional de Cafeteros de Colombia (Colombian National Coffee-Growers Federation), requires slimming down operational costs while continuing to provide a h...
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The Colombian coffee supply network, managed by the Federacion Nacional de Cafeteros de Colombia (Colombian National Coffee-Growers Federation), requires slimming down operational costs while continuing to provide a high level of service in terms of coverage to its affiliated coffee growers. We model this problem as a biobjective (cost-coverage) uncapacitated facility location problem (BOUFLP). We designed and implemented three different algorithms for the BOUFLP that are able to obtain a good approximation of the Pareto frontier. We designed an algorithm based on the Nondominated Sorting Genetic Algorithm;an algorithm based on the Pareto Archive Evolution Strategy;and an algorithm based on mathematical programming. We developed a random problem generator for testing and comparison using as reference the Colombian coffee supply network with 29 depots and 47 purchasing centers. We compared the algorithms based on the quality of the approximation to the Pareto frontier using a nondominated space metric inspired on Zitzler and Thiele's. We used the mathematical programming-based algorithm to identify unique tradeoff opportunities for the reconfiguration of the Colombian coffee supply network. Finally, we illustrate an extension of the mathematical programming-based algorithm to perform scenario analysis for a set of uncapacitated location problems found in the literature.
In many real-world applications, mobile robots require interacting with objects in their environments by means of performing docking tasks in a precise manner. In the application domain of this work, an Automated Guid...
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ISBN:
(纸本)0780392981
In many real-world applications, mobile robots require interacting with objects in their environments by means of performing docking tasks in a precise manner. In the application domain of this work, an Automated Guided Vehicle (AGV), specifically, a fork-lift truck must often perform docking maneuvers to load pallets in conveyor belts. The main purpose is to improve some features of docking task as its duration, accuracy and stability. We propose a soft computing technique based on a multiobjectiveevolutionary algorithm in order to find multiples fuzzy logic controllers which optimize specific objectives and satisfy imposed constraints for docking task in charge of following up an online generated trajectory.
作者：
Cotik, VZaliz, RRZwir, IWashington Univ
Sch Med Howard Hughes Med Inst Dept Mol Microbiol St Louis MO 63130 USA Univ Buenos Aires
Fac Ciencias Exactas & Nat Dept Computac Buenos Aires DF Argentina Univ Granada
Dept Ciencias Computac & Intelegencia Artificial ETS Ingn Informat Granada Spain
One of the big challenges of the post-genomic era is identifying regulatory systems and integrating them into genetic networks. Gene expression is determined by protein-protein interactions among regulatory proteins a...
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One of the big challenges of the post-genomic era is identifying regulatory systems and integrating them into genetic networks. Gene expression is determined by protein-protein interactions among regulatory proteins and with RNA polymerase(s), and protein-DNA interactions of these trans-acting factors with cis-acting DNA sequences in the promoter regions of those regulated genes. Therefore, identifying these protein-DNA interactions, by means of the DNA motifs that characterize the regulatory factors operating in the transcription of a gene, becomes crucial for determining, which genes participate in a regulation process, how they behave and how they are connected to build genetic networks. In this paper. we propose a hybrid promoter analysis methodology (HPAM) to discover complex promoter motifs that combines: the neural network efficiency and ability of representing imprecise and incomplete patterns;the flexibility and interpretability of fuzzy models;and the multi-objective evolutionary algorithms capability to identify optimal instances of a model by searching according to multiple criteria. We test our methodology by learning and predicting the RNA polymerase motif in prokaryotic genomes. This constitutes a special challenge due to the multiplicity of the RNA polymerase targets and its connectivity with other transcription factors, which sometimes require multiple functional binding sites even in close located regulatory regions;and the uncertainty of its motif, which allows sites with low specificity (i.e., differing from the best alignment or consensus) to still be functional. HPAM is available for public use in http://***. (c) 2004 Elsevier B.V All rights reserved.
In this work we investigate the applicability of a multiobjective formulation of the Ab-Initio Protein Structure Prediction (PSP) to medium size protein sequences (46-70 residues). In particular, we introduce a modifi...
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ISBN:
(纸本)3540253963
In this work we investigate the applicability of a multiobjective formulation of the Ab-Initio Protein Structure Prediction (PSP) to medium size protein sequences (46-70 residues). In particular, we introduce a modified version of Pareto Archived Evolution Strategy (PAES) which makes use of immune inspired computing principles and which we will denote by "I-PAES". Experimental results on the test bed of five proteins from PDB show that PAES, (1+1)-PAES and its modified version I-PAES, are optimal multiobjective optimization algorithms and the introduced mutation operators, mut(1) and mut(2), are effective for the PSP problem. The proposed I-PAES is comparable with other evolutionaryalgorithms proposed in literature, both in terms of best solution found and computational cost.
Scheduling problems are a very common research topic. This is because, for efficiency reasons, our world relies heavily on schedules and deadlines. Aircraft engine maintenance is no exception. The United States Air Fo...
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ISBN:
(纸本)9781450378000
Scheduling problems are a very common research topic. This is because, for efficiency reasons, our world relies heavily on schedules and deadlines. Aircraft engine maintenance is no exception. The United States Air Force has many planes that it must keep up and running. But with the downsizing that has occurred in recent years, the number of planes that are operational has become more critical. This means that every effort needs to be made to ensure that not only are the engines repaired in an efficient manner, but that their component's scheduled maintenance cycles are in sync so that the engine has fewer trips to the logistics maintenance center.
An adaptive Pareto differential evolution algorithm for multi-objective optimization is proposed. Its effectiveness on approximating the Pareto front is compared with that of SPEA[9] and of SPDE[2]. A parallel impleme...
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ISBN:
(纸本)3540219463
An adaptive Pareto differential evolution algorithm for multi-objective optimization is proposed. Its effectiveness on approximating the Pareto front is compared with that of SPEA[9] and of SPDE[2]. A parallel implementation, based on an island model with a random connection topology, is also analyzed. The parallelization efficiency derives from the simple migration strategy. Numerical tests were performed on a cluster of workstations.
The purpose of this paper is to present a flexible genetic-based framework for solving the multi-criteria weighted matching problem (mc-WMP). In the first part of this paper, we design a genetic-based framework for so...
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The purpose of this paper is to present a flexible genetic-based framework for solving the multi-criteria weighted matching problem (mc-WMP). In the first part of this paper, we design a genetic-based framework for solving the ordinary weighted matching problem. We present an extensive analysis of the quality of the results and introduce a methodology for tuning its parameters. In the second part, we develop a modified genetic-based algorithm for solving the mc-WMP. The algorithm generates a significant and representative portion of the Pareto optimal set. To assess the performance of the algorithm, we conduct computational experiments with two and three criteria. The potential of the proposed aligorithm is demonstrated by comparing to a multi-objective simulated annealing algorithm. (C) 2002 Elsevier Science B.V. All rights reserved.
Stochastic optimization by learning and using probabilistic models has received an increasing amount of attention over the last few years. algorithms within this field estimate the probability distribution of a select...
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Stochastic optimization by learning and using probabilistic models has received an increasing amount of attention over the last few years. algorithms within this field estimate the probability distribution of a selection of the available solutions and subsequently draw more samples from the estimated probability distribution. The resulting algorithms have displayed a good performance on a wide variety of single-objective optimization problems, both for binary as well as for real-valued variables. Mixture distributions offer a powerful tool for modeling complicated dependencies between the problem variables. Moreover, they allow for elegant and parallel exploration of a multi-objective front. This parallel exploration aids the important preservation of diversity in multi-objective optimization. In this paper, we propose a new algorithm for evolutionarymulti-objective optimization by learning and using probabilistic mixture distributions. We name this algorithm multi-objective Mixture-based Iterated Density Estimation evolutionary Algorithm (MIDEA). To further improve and maintain the diversity that is obtained by the mixture distribution, we use a specialized diversity preserving selection operator. We verify the effectiveness of our approach in two different problem domains and compare it with two other well-known efficient multi-objective evolutionary algorithms. (C) 2002 Elsevier Science Inc. All rights reserved.
In multi-objective optimization (MOO) problems we need to optimize or at least satisfy many possibly conflicting objectives. For instance, in manufacturing planning we might want to minimize the cost and production ti...
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ISBN:
(纸本)0819445541
In multi-objective optimization (MOO) problems we need to optimize or at least satisfy many possibly conflicting objectives. For instance, in manufacturing planning we might want to minimize the cost and production time while maximizing the product's quality. We propose the use of evolutionaryalgorithms (EAs) to solve these problems. EAs are computer programs that generate solutions by simulating a Darwinian evolution. Solutions are represented as individuals in a population and are assigned scores according to a fitness function that determines their relative quality. Strong solutions are selected for reproduction, and pass their genetic material to the next generation. Weak solutions are removed from the population. The fitness function evaluates each solution and returns a related score.. In MOO problems, this fitness function is vector-valued, i.e. it returns a value for each objective. Therefore, instead of a global optimum, we try to find the Pareto-optimal or non-dominated frontier. We use multi-sexual EAs with as many genders as optimization criteria. We have created new crossover and gender assignment functions, and experimented with various parameters to determine the best setting (yielding the highest number of non-dominated solutions.) These experiments are conducted using a variety of fitness functions, and the algorithms are later evaluated on a flexible manufacturing problem with total cost and time minimization objectives.
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