Genetic algorithms (GAs) have been widely used in solving multiobjective optimization problems (MOP). The foremost hindrance limiting strength of GA is the large number of nondominated solutions and the computational ...
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Genetic algorithms (GAs) have been widely used in solving multiobjective optimization problems (MOP). The foremost hindrance limiting strength of GA is the large number of nondominated solutions and the computational complexity involved in selecting a preferential candidate among the set of nondominated solutions. In this paper, we analyze the approach of applying aggregation operator in place of density-based indicator mechanism in cases where Pareto dominance method fails to decide the preferential solution. We also propose a new aggregation function () and compare the results obtained with prevailing aggregation functions suggested in the literature. We demonstrate that the proposed method is computationally less expensive with overall complexity of . To show the efficacy and consistency of the proposed method, we applied it on different, two- and three-objective benchmark functions. Results indicate a good convergence rate along with a near-perfect diverse approximation set.
Environmental adaptation method (EAM) is one of the evolutionary algorithms for solving single objective optimization problems. After the first proposal of EAM, other variants have been suggested to speed up the conve...
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Environmental adaptation method (EAM) is one of the evolutionary algorithms for solving single objective optimization problems. After the first proposal of EAM, other variants have been suggested to speed up the convergence and to maintain the population diversity. Among them, IEAM-RP works with real numbers and was able to achieve the desired goal during the optimization process. In this paper, IEAM-RP is used to predict the effort required to develop the software product. The experiments are carried out on NASA software project dataset to check the effectiveness of IEAM-RP. The experimental results demonstrated that the overall performance of IEAM-RP is quite satisfactory in predicting the effort required to develop a software.
Process redesign is an important and valuable phase of the business process management (BPM) lifecycle. However, human creativity and objectiveness regarding continuous redesign initiatives are limited and biased. To ...
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Process redesign is an important and valuable phase of the business process management (BPM) lifecycle. However, human creativity and objectiveness regarding continuous redesign initiatives are limited and biased. To overcome these limitations, we propose computational support based on evolutionary algorithms. Our software tool extends a formerly published proof of concept. Novelties have been introduced by including a new data structure, new mutation and crossover operators as well as an extended evaluation of unambiguous process designs explicitly considering time objectives. Finally, the tool provides new computation process (re-) design support to the BPM community.
In multi-stage hot forging processes, the preform shape is the parameter mainly influencing the final forging result. Nevertheless, the design of multi-stage hot forging processes is still a trial and error process an...
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In multi-stage hot forging processes, the preform shape is the parameter mainly influencing the final forging result. Nevertheless, the design of multi-stage hot forging processes is still a trial and error process and therefore time-consuming. The quality of developed forging sequences strongly depends on the engineer's experience. To overcome these obstacles, this paper presents an algorithm for solving the multi-objective optimization problem when designing preforms. Cross-wedge-rolled (CWR) preforms were chosen as subject of investigation. An evolutionary algorithm is introduced to optimize the preform shape taking into account the mass distribution of the final part, the preform volume, and the shape complexity. The developed algorithm is tested using a connecting rod as a demonstration part. Based on finite element analysis, the implemented fitness function is evaluated, and thus the progressive optimization can be traced.
Numerous studies have investigated the effects of unbalanced service times and inter-station buffer sizes on the efficiency of discrete part, unpaced production lines. There are two main disadvantages of many of these...
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Numerous studies have investigated the effects of unbalanced service times and inter-station buffer sizes on the efficiency of discrete part, unpaced production lines. There are two main disadvantages of many of these studies: (1) only some predetermined degree of imbalance and patterns of imbalance have been evaluated against the perfectly balanced configuration, making it hard to form a general conclusion on these factors;(2) only a single objective has been set as the target, which neglects the fact that different patterns of imbalance may outperform with respect to different performance measures. Therefore, the aim of this study is to introduce a new approach to investigate the performance of unpaced production lines by using multiple-objective optimisation. It has been found by equipping multi-objective optimisation with an efficient, equality constraints handling technique, both the optimal pattern and degree of imbalance, as well as the optimal relationship among these factors and the performance measures of a production system can be sought and analysed with some single optimisation runs. The results have illustrated that some very interesting relationships among the key performance measures studied, including system throughput, work-in-process and average buffer level, could only be observed within a truly multi-objective optimisation context. While these results may not be generalised to apply to any production lines, the genericity of the proposed simulation-based approach is believed to be applicable to study any real-world, complex production lines.
In this study, a new perspective on the application of the clustering approach is proposed. The perspective aims to identify the values of the parameters of clustering, including the choice of the algorithm itself, wh...
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In this study, a new perspective on the application of the clustering approach is proposed. The perspective aims to identify the values of the parameters of clustering, including the choice of the algorithm itself, which lead to a possibly faithful rendering of a partition of data, which is known a priori. Motivation and possible interpretations are discussed which can be associated with such a reverse identification process. The essential motivation is associated, but not limited, to the primary objective of cluster analysis, i.e. gaining insight into the structure of the given data-set or family of data-sets. We propose to use evolutionary strategies for reverse analysis to be carried out in view of the characteristics of the problem considered. The concept and the feasibility of the proposed computational approach are illustrated by the analysis of an exemplary data-set. The preliminary results obtained are promising in both technical and cognitive terms.
Multicast communication transmits packages from a single source to many destinations in a single transmission. Several algorithms have been proposed to obtain the best multicast routes using approaches such as exhaust...
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Practical optimization problems frequently include uncertainty about the quality measure, for example, due to noisy evaluations. Thus, they do not allow for a straightforward application of traditional optimization te...
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Practical optimization problems frequently include uncertainty about the quality measure, for example, due to noisy evaluations. Thus, they do not allow for a straightforward application of traditional optimization techniques. In these settings, randomized search heuristics such as evolutionary algorithms are a popular choice because they are often assumed to exhibit some kind of resistance to noise. Empirical evidence suggests that some algorithms, such as estimation of distribution algorithms (EDAs) are robust against a scaling of the noise intensity, even without resorting to explicit noise-handling techniques such as resampling. In this paper, we want to support such claims with mathematical rigor. We introduce the concept of graceful scaling in which the run time of an algorithm scales polynomially with noise intensity. We study a monotone fitness function over binary strings with additive noise taken from a Gaussian distribution. We show that myopic heuristics cannot efficiently optimize the function under arbitrarily intense noise without any explicit noise-handling. Furthermore, we prove that using a population does not help. Finally, we show that a simple EDA called the compact genetic algorithm can overcome the short-sightedness of mutation-only heuristics to scale gracefully with noise. We conjecture that recombinative genetic algorithms also have this property.
In this paper, we investigate a design approach aiming at simultaneously integrating the energy management and the sizing of a small microgrid with storage. We particularly underline the complexity of the resulting op...
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In this paper, we investigate a design approach aiming at simultaneously integrating the energy management and the sizing of a small microgrid with storage. We particularly underline the complexity of the resulting optimization problem and how it can be solved using suitable optimization methods in compliance with relevant models of the microgrid. We specifically show the reduction of the computational time allowing the microgrid simulation over long time durations in the optimization process in order to take seasonal variations into account. The developed approach allows performing many optimal designs in order to find the appropriate price context that could favor the installation of storage devices.
For each cancer type, only a few genes are informative. Due to the so-called 'curse of dimensionality' problem, the gene selection task remains a challenge. To overcome this problem, we propose a two stage gen...
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For each cancer type, only a few genes are informative. Due to the so-called 'curse of dimensionality' problem, the gene selection task remains a challenge. To overcome this problem, we propose a two stage gene selection method called MRMR-COA-HS. In the first stage, the minimum redundancy and maximum relevance (MRMR) feature selection is used to select a subset of relevant genes. The selected genes are then fed into a wrapper setup that combines a new algorithm, COA-HS, using the support vector machine as a classifier. The method was applied to four microarray datasets, and the performance was assessed by the leave one out cross-validation method. Comparative performance assessment of the proposed method with other evolutionary algorithms suggested that the proposed algorithm significantly outperforms other methods in selecting a fewer number of genes while maintaining the highest classification accuracy. The functions of the selected genes were further investigated, and it was confirmed that the selected genes are biologically relevant to each cancer type. (C) 2017 Published by Elsevier Inc.
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