Automation of analog integrated circuit (IC) design process is very important because of the optimization contradictions. In this study, benefits of multi-objective evolutionary algorithms are presented on two stage o...
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ISBN:
(纸本)9781479984992
Automation of analog integrated circuit (IC) design process is very important because of the optimization contradictions. In this study, benefits of multi-objective evolutionary algorithms are presented on two stage operational amplifier design using Harmony Search algorithm (HSA) and non-dominated sorting genetic algorithm (NSGA-II). HSA is a new kind of multi-objective evolutionary algorithm which was inspired from the musicians those are looking for the best combination of musical sounds of different instruments that produces most pleasing sound. NSGA-II is an advanced version of geneticalgorithm. It combines both current parents and their child population to select new parents. These kinds of design automation tools are required for analog circuit design because there are several contradictions in the design. In this work, transistor sizes which effects all constraints indirectly were automatically synthesized by HSA an NSGA-II.
For the generation maintenance scheduling (GMS) problem, a producer hopes to maximize its profit while ISO is to guarantee the system reliability. Thus, the GMS is a multi-objective optimization problem. In the GMS, t...
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ISBN:
(纸本)9781479964161
For the generation maintenance scheduling (GMS) problem, a producer hopes to maximize its profit while ISO is to guarantee the system reliability. Thus, the GMS is a multi-objective optimization problem. In the GMS, there are large numbers of both continuous and integer variables, which complicates the resolving of the GMS. This paper proposes a new GMS model, which is suitable to be solved by the non-dominated sorting genetic algorithm-II (NSGA-II). In the GMS model, the maintenance status of a generator is encoded into an integer variable and both the online status and the start-up status are represented by the generation variables. The GMS model on the IEEE reliability test system is solved by NSGA-II with a set of Pareto-optimal solutions obtained. The simulation results show that the GMS can be efficiently solved by NSGAII. The simulation results also show that one producer's profit conflicts with another one's, and that the reliability objective is independent of the other objectives.
From simulation experiments of the multi-objective optimization control of wastewater treatment process(WWTP),it can be found that the number of obtained Pareto solutions is less using the normal non-dominatedsorting...
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From simulation experiments of the multi-objective optimization control of wastewater treatment process(WWTP),it can be found that the number of obtained Pareto solutions is less using the normal non-dominated sorting genetic algorithm-II(NSGA-II) *** achieve a satisfactory optimal performance,an improved NSGA-II algorithm based on differential evolution mechanism is proposed in this *** simulation results show that the diversity of the solutions is enhanced and a better homogeneity of non-inferior solutions is kept using the proposed method.
Earth observation satellites(EOS) equipped with high-resolution cameras and infrared sensors are used to observe various types of objects on the Earth's surface. When there are numerous ground targets, the availab...
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Earth observation satellites(EOS) equipped with high-resolution cameras and infrared sensors are used to observe various types of objects on the Earth's surface. When there are numerous ground targets, the available time windows for multiple EOS to observe the given task should be utilized effectively. This problem is known as the multi-satellite observation scheduling problem(MSOSP). To address the scheduling problem of dense tasks with multiple satellites, this paper establishes a bi-objective mixed integer programming model with minimizing the maximum completion time and the total weighted completion time simultaneously. Then, an improved NSGA-II algorithm(INSGA-II), which incorporates a density-based clustering algorithm called DBSCAN to enhance the diversity of solutions during the population evolution process, is proposed to solve the model. Finally, two observation scenarios are designed, and computational experiments are conducted. The results show that INSGA-II outperforms the NSGA-II algorithm in terms of C-metric and hypervolume metric.
The flexible job shop scheduling problem (FJSSP) is a complex optimization challenge that plays a crucial role in enhancing productivity and efficiency in modern manufacturing systems, aimed at optimizing the allocati...
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Medical analytics in genetic process mining have developed models with multiple and often conflicting criteria. Some studies have reduced the complexities inherent in multiple and conflicting criteria by eliminating s...
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While the number of deliveries has been increasing rapidly, infrastructure such as parking and building configurations has changed less quickly, given limited space and funds. This may lead to an imbalance between sup...
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While the number of deliveries has been increasing rapidly, infrastructure such as parking and building configurations has changed less quickly, given limited space and funds. This may lead to an imbalance between supply and demand, preventing the current resources from meeting the future needs of urban freight activities. The aim of this study was to discover the future delivery rates that would overflow the current delivery systems and find the optimal numbers of resources. To achieve this objective, we introduced a multi-objective, simulation-based optimization model to define the complex freight delivery cost relationships among delivery workers, building managers, and city planners, based on the real-world observations of the final 50 ft of urban freight activities at an office building in downtown Seattle, Washington, U.S.A. Our discrete-event simulation model with increasing delivery arrival rates showed an inverse relationship in costs between delivery workers and building managers, while the cost of city planners decreased up to ten deliveries/h and then increased until 18 deliveries/h, at which point costs increased for all three parties and overflew the current building and parking resources. The optimal numbers of resources that would minimize the costs for all three parties were then explored by a non-dominated sorting genetic algorithm (NSGA-2) and a multi-objective, evolutionary algorithm based on decomposition (MOEA/D). Our study sheds new light on a data-driven approach for determining the best combination of resources that would help the three entities work as a team to better prepare for the future demand for urban goods deliveries.
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