Deteriorated water distribution networks require significant investments to maximize their functionality. The problem is that limited financial resources are allocated for rehabilitation strategies. This deficiency hi...
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Deteriorated water distribution networks require significant investments to maximize their functionality. The problem is that limited financial resources are allocated for rehabilitation strategies. This deficiency highlights the importance of developing a tool that helps decision makers develop maintenance and replacement management plans. The optimization tool is employed using two evolutionary algorithms: genetic algorithms and particle swarm optimization. The efficacy of the developed model is demonstrated through its application in a case study of Shaker Al-Bahery, Egypt. Furthermore, evaluation metrics are considered to compare the performance of the aforementioned algorithms. The results reveal that the particle swarm optimization exhibited superior results when compared with the genetic algorithms. Moreover, the following two multicriteria decision-making techniques are used to provide a ranking for the near-optimum solutions: multiobjectiveoptimization on the basis of ratio analysis and technique for order preference by similarity to ideal solution. Finally, the Spearman correlation coefficient is utilized to assess the correlation between rankings obtained from different decision-making methods. The results indicate a very strong relation among the aforementioned techniques.
NSGA-II has shown good performance in solving multi-objectiveoptimization problems, However, the tournament selection strategy in NSGA-II always generates many duplicate individuals. This phenomenon not only affects ...
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ISBN:
(纸本)9783319959573;9783319959566
NSGA-II has shown good performance in solving multi-objectiveoptimization problems, However, the tournament selection strategy in NSGA-II always generates many duplicate individuals. This phenomenon not only affects the crossover, mutation and updating operations and finally deteriorates the performance significantly. To overcome this problem, this paper introduces a new strategy, namely selection strategy without replacement, which can produces different individuals to increase the diversity. Simulation results show the proposed tournament selection without replacement achieve better performance.
Very complex micro-architectures, like complex superscalar/SMT or multicore systems, have lots of configurations. Exploring this huge design space and trying to optimize multiple objectives, like performance, power co...
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ISBN:
(纸本)9781479914920
Very complex micro-architectures, like complex superscalar/SMT or multicore systems, have lots of configurations. Exploring this huge design space and trying to optimize multiple objectives, like performance, power consumption and hardware complexity is a real challenge. In this paper, using the multi-objective design space exploration tool FADSE, we tried to optimize the hardware parameters of the complex superscalar Grid ALU Processor. We compared how different heuristic algorithms handle the DSE optimization. Three of these algorithms are taken from the jMetal library (NSGAII, SPEA2 and SMPSO) while the other two, CNSGAII and MOHC were implemented by us. We show that in this huge design space the differences between the best found individuals by every algorithm are very small, only the time in which they got to these solutions differs. In order to accelerate the DSE process we also did a feature selection through machine learning techniques and ran all DSE algorithms again with a smaller number of input parameters.
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