Recently, decomposition-based multi-objective evolutionary algorithm (MOEA/D) has received increasing attentions due to its simplicity and decent optimization performance. In the presence of the deceptive optimum, the...
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ISBN:
(纸本)9783642412783;9783642412776
Recently, decomposition-based multi-objective evolutionary algorithm (MOEA/D) has received increasing attentions due to its simplicity and decent optimization performance. In the presence of the deceptive optimum, the weight vector approach used in MOEA/D may not be able to prevent the population traps into local optimum. In this paper, we propose a new algorithm, namely Diversity Preservation multi-objective evolutionary algorithm based on Decomposition (DivPre-MOEA/D), which uses novel diversity maintenance scheme to enhance the performance of MOEA/D. The proposed algorithm relaxes the dependency of the weight vector approach on approximated ideal vector to maintain diversity of the population. The proposed algorithm is evaluated on CEC-09 test suite and compared the optimization performance with MOEA/D. The experiment results show that DivPre-MOEA/D can provide better solutions spread along the Pareto front.
Robustness, the ability to maintain performance in the face of perturbations and uncertainty, is believed to be a necessary property of biological systems. In this paper, we address the issue of robustness in an impor...
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Robustness, the ability to maintain performance in the face of perturbations and uncertainty, is believed to be a necessary property of biological systems. In this paper, we address the issue of robustness in an important signal transduction network-epidermal growth factor receptor (EGFR) network. First, we analyze the robustness in the EGFR signaling network using all rate constants against the Gauss variation which was described as "the reference parameter set" in the previous study [Kholodenko, B.N., Demin, O.V., Mochren, G., Hoek, J.B., 1999. Quantification of short term signaling by the epidermal growth factor receptor. ***. Chem. 274, 30169-30181]. The simulation results show that signal time, signal duration and signal amplitude of the EGRR signaling network are relatively not robust against the simultaneous variation of the reference parameter set. Second, robustness is quantified using some statistical quantities. Finally, a multi-objective evolutionary algorithm (MOEA) is presented to search reaction rate constants which optimize the robustness of network and compared with the NSGA-II, which is a representation of a class of modem multi-objective evolutionary algorithms. Our simulation results demonstrate that signal time, signal duration and signal amplitude of the four key components - the most downstream variable in each of the pathways: R-Sh-G-S, R-PLP, R-G-S and the phosphorylated receptor RP in EGRR signaling network for the optimized parameter sets have better robustness than those for the reference parameter set and the NSGA-II. These results can provide valuable insight into experimental designs and the dynamics of the signal-response relationship between the dimerized and activated EGFR and the activation of downstream proteins. (C) 2007 Elsevier Ireland Ltd. All rights reserved.
A multi-objective design optimization of a rotating two-pass cooling channel with airfoil-shaped guide vanes in the turning region has been performed to enhance heat transfer and reduce pressure drop in the channel. A...
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A multi-objective design optimization of a rotating two-pass cooling channel with airfoil-shaped guide vanes in the turning region has been performed to enhance heat transfer and reduce pressure drop in the channel. An optimization procedure using three-dimensional Reynolds-averaged Navier Stokes analysis, a surrogate method, and a multi-objective evolutionary algorithm is presented. Two objective functions related to heat transfer and friction loss are considered to estimate the cooling performance of the rotating two-pass channel. For the optimization, four design variables, the angles and radii of the airfoil-shaped guide vanes, are selected. A comparison of the friction factors between the experimental data and numerical results for a smooth channel is made to validate the numerical results. The optimized airfoil-shaped guide vanes in the turning region reduce pressure loss and enhance heat transfer throughout the channel.
The energy consumption of the Internet accounts for approximately 1% of the world's total electricity usage, which may become the main constraint for its future growth of the Internet. In response, we propose a no...
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ISBN:
(纸本)9783642405174;9783642405167
The energy consumption of the Internet accounts for approximately 1% of the world's total electricity usage, which may become the main constraint for its future growth of the Internet. In response, we propose a novel dynamic energy management framework that reduces the overall energy consumption without degrading network performance. The main concept is to combine infrastructure sleeping with virtual router migration. During off-peak hours, the virtual routers are moved onto fewer physical platforms and the unused physical platforms are placed in a sleep mode to save energy. The sleeping physical platforms are awakened again during busy periods. In our energy management framework, an important question that is considered is where to move the virtual routers to. To this end, we develop an evolutionaryalgorithm to solve the destination physical platform selection problem.
Data structures used for an algorithm can have a great impact on its performance, particularly for the solution of large and complex problems, such as multi-objective optimization problems (MOPs). multi-objective ev...
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Data structures used for an algorithm can have a great impact on its performance, particularly for the solution of large and complex problems, such as multi-objective optimization problems (MOPs). multi-objective evolutionary algorithms (MOEAs) are considered an attractive approach for solving MOPs~ since they are able to explore several parts of the Pareto front simultaneously. The data structures for storing and updating populations and non-dominated solutions (archives) may affect the efficiency of the search process. This article describes data structures used in MOEAs for realizing populations and archives in a comparative way, emphasizing their computational requirements and general applicability reported in the original work.
Development in composite materials technology has led to wide applications of such promising materials. The detection and characterisation of the wide range of defects requires a number of specialised non-destructive ...
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Development in composite materials technology has led to wide applications of such promising materials. The detection and characterisation of the wide range of defects requires a number of specialised non-destructive methods. In this article, lock-in thermography was used to identify defects in carbon-fibre-reinforced polymer specimens. The effects of modulation frequency and incident angle were discussed. The amplitude and the phase images were compared. A multi-objective evolutionary algorithm was used to optimise the experimental frequency, time and other parameters of infrared NDT. The temperature difference between the non-destructive and the defect regions was used as evaluation function. The results show this method can provide theoretical parameters for engineering inspection.
Bi-objective graph coloring problem (BOGCP) is a generalized version in which the number of colors used to color the vertices of a graph and the corresponding penalty which incurs due to coloring the endpoints of an e...
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Bi-objective graph coloring problem (BOGCP) is a generalized version in which the number of colors used to color the vertices of a graph and the corresponding penalty which incurs due to coloring the endpoints of an edge with the same color are simultaneously minimized. In this paper, we have analyzed the graph density, the interconnection between high degree nodes of a graph, the rank exponent of the standard benchmark input graph instances and observed that the characterization of graph instances affects on the behavioral quality of the solution sets generated by existing heuristics across the entire range of the obtained Pareto fronts. We have used multi-objective evolutionary algorithm (MOEA) to obtain improved quality solution sets with the problem specific knowledge as well as with the embedded heuristics knowledge. To establish this fact for BOGCP, hybridization approach is used to construct recombination operators and mutation operators and it is observed from empirical results that the embedded problem specific knowledge in evolutionary operators helps to improve the quality of solution sets across the entire Pareto front;the nature of problem specific knowledge differentiates the quality of solution sets. (C) 2012 Elsevier B.V. All rights reserved.
This study addresses robust scheduling for a flexible job-shop scheduling problem with random machine breakdowns. Two objectives - makespan and robustness - are simultaneously considered. Robustness is indicated by th...
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This study addresses robust scheduling for a flexible job-shop scheduling problem with random machine breakdowns. Two objectives - makespan and robustness - are simultaneously considered. Robustness is indicated by the expected value of the relative difference between the deterministic and actual makespan. Utilizing the available information about machine breakdowns, two surrogate measures for robustness are developed. Specifically, the first suggested surrogate measure considers the probability of machine breakdowns, while the second surrogate measure considers the location of float times and machine breakdowns. To address this problem, a multi-objective evolutionary algorithm is presented in this paper. The experimental results indicate that, compared with several other existing surrogate measures, the first suggested surrogate measure performs better for small cases, while the second surrogate measure performs better for both small and relatively large cases. (C) 2012 Elsevier B.V. All rights reserved.
This paper presents a new possibilistic model for the portfolio selection problem. The uncertainty of future returns on a given portfolio is modeled using LR-fuzzy numbers. Some possibilistic moments are considered to...
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This paper presents a new possibilistic model for the portfolio selection problem. The uncertainty of future returns on a given portfolio is modeled using LR-fuzzy numbers. Some possibilistic moments are considered to measure the risk of and return on the investment. Since the joint possibility distribution of the returns on the assets is unknown, we consider the returns on a given portfolio as the historical dataset instead of considering the individual returns on the assets as the dataset. We introduce a coefficient of possibilistic skewness in order to incorporate a measurement of the asymmetry of the fuzzy return on a given portfolio. We solve the multi-objective optimization problems that are associated with the possibilistic mean-downside risk-skewness model by using an evolutionary procedure to generate efficient portfolios. The procedure provides different patterns of investment, whose portfolios meet the explicit restrictions imposed by the investor. Thus, from among the points in the efficient frontier, the investor may select a portfolio that optimizes an economically meaningful objective function. The performance of this approach is tested using a dataset of assets from the Spanish stock market.
This study presents a new multi-objective optimization method, the niched Pareto tabu search (NPTS), for optimal design of groundwater remediation systems. The proposed NPTS is then coupled with the commonly used flow...
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This study presents a new multi-objective optimization method, the niched Pareto tabu search (NPTS), for optimal design of groundwater remediation systems. The proposed NPTS is then coupled with the commonly used flow and transport code, MODFLOW and MT3DMS, to search for the near Pareto-optimal tradeoffs of groundwater remediation strategies. The difference between the proposed NPTS and the existing multiple objective tabu search (MOTS) lies in the use of the niche selection strategy and fitness archiving to maintain the diversity of the optimal solutions along the Pareto front and avoid repetitive calculations of the objective functions associated with the flow and transport model. Sensitivity analysis of the NPTS parameters is evaluated through a synthetic pump-and-treat remediation application involving two conflicting objectives, minimizations of both remediation cost and contaminant mass remaining in the aquifer. Moreover, the proposed NPTS is applied to a large-scale pump-and-treat groundwater remediation system of the field site at the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts, involving minimizations of both total pumping rates and contaminant mass remaining in the aquifer. Additional comparison of the results based on the NPTS with those obtained from other two methods, namely the single objective tabu search (SOTS) and the nondominated sorting genetic algorithm II (NSGA-II), further indicates that the proposed NPTS has desirable computation efficiency, stability, and robustness and is a promising tool for optimizing the multi-objective design of groundwater remediation systems. (C) 2013 Elsevier B.V. All rights reserved.
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