In recent decades, multi-objective evolutionary algorithms (MOEAs) are developed as powerful tools to solve multi-objective optimization problems. While the diversity of Pareto front (PF) plays an important role in th...
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In recent decades, multi-objective evolutionary algorithms (MOEAs) are developed as powerful tools to solve multi-objective optimization problems. While the diversity of Pareto front (PF) plays an important role in the performance evaluation of MOEAs, various diversity preservation strategies (DPS) have been developed. In this paper, a novel approach that inspired from the crowding distance technique is proposed to maintain the diversity of solutions in multi-objective problems (MOPs) with quite different spans of value range. In order to improve its performance, this approach is applied in a well-know MOEA NSGA II by replacing its original DPS. According to 3 test MOPs, the modified NSGA II shows a better diversity and distribution in the PF compared with the original version. Furthermore, the influence of the spans of value range on the performance of original DPS in NSGA II is discussed and the robustness of the new DPS is illustrated.
Portfolio optimization problem is a multi-objective optimization problem, it is necessary to consider the benefits should also consider the risks and the optimal situation is to achieve the least risk and maximum retu...
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ISBN:
(纸本)9781510835429
Portfolio optimization problem is a multi-objective optimization problem, it is necessary to consider the benefits should also consider the risks and the optimal situation is to achieve the least risk and maximum return. Papers define portfolio optimization problems, analyze the shortcomings of the existing portfolio model and the corresponding solution ideas, introduce multi-objective genetic algorithm and demonstrate the feasibility of multi-objective genetic algorithm in the application portfolio. algorithm design process, detailed process of constructing and building a portfolio investment in line with the actual situation, put forward five objective optimization model from all angles, through a reasonable, optimized simulation test, and then select the algorithm selected from a number of programs better overall performance of the program.
multi-objective evolutionary algorithm (MOEA) is used to solve multi-objective optimization problems (MOOPs). In order to improve the efficiency of MOEA, a fast method of constructing non-dominated set called Arena...
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multi-objective evolutionary algorithm (MOEA) is used to solve multi-objective optimization problems (MOOPs). In order to improve the efficiency of MOEA, a fast method of constructing non-dominated set called Arena' s Principle is suggested. The time complexity of the Arena's Principle is better than that of Deb's and Jensen's on constructing non-dominated set. In the experiments, Arena's Principle is compared with Deb' and Jensen's approach. It is shown that the experimental results satisfy the theoretical analysis.
This paper presents an evolutionaryalgorithm for analyzing the best mix of distributed generations (DG) in a distribution network. The multi-objective optimization aims at minimizing the total cost of real power gene...
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ISBN:
(纸本)9781467325950
This paper presents an evolutionaryalgorithm for analyzing the best mix of distributed generations (DG) in a distribution network. The multi-objective optimization aims at minimizing the total cost of real power generation, line losses and CO_2 emissions, and maximizing the benefits from the DG over a 20 years planning horizon. The method assesses the fault current constraint imposed on the distribution network by the existing and new DG in order not to violate the short circuit capacity of existing switchgear. The analysis utilizes one of the highly regarded evolutionaryalgorithm, the Strength Pareto evolutionaryalgorithm 2 (SPEA2) for multi-objective optimization and MATPOWER for solving the optimal power flow problems.
The MOEA/D-DE (multi-objective evolutionary algorithm based on decomposition combined with differential evolution) is firstly applied to design a high-sensitivity RFID sensor tag with the consideration of its communic...
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ISBN:
(纸本)9781467379601
The MOEA/D-DE (multi-objective evolutionary algorithm based on decomposition combined with differential evolution) is firstly applied to design a high-sensitivity RFID sensor tag with the consideration of its communication performance. For demonstration, an RFID temperature sensor tag is designed and tested. Both simulated and measured results show the designed sensor tag achieves a three times higher sensing sensitivity and a better communication performance than the one in the literature.
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