Improved NSGA-II Multi-objective Genetic Algorithm Based on Hybridization-encouraged Mechanism

Sun, Yijie; Gongzhang, Shen

doi:10.1016/s1000-9361(08)60172-7

Cited by 35 publications

(10 citation statements)

References 11 publications

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“…MGA is a variant of a non-dominated sorting genetic algorithm II (NSGA-II) proposed by (Deb, 2001). NSGA-II favors individuals with an elitist strategy and individuals that can help increase the diversity of the population (Yijie and Gongzhang, 2008). The output of the MGA is a set of solutions that is also known as Pareto front optimized solutions, among which we can select the most preferable solution.…”

Section: Cell Neighborhood Calibrationmentioning

confidence: 99%

Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm

Mustafà

Heppenstall

Omrani

et al. 2018

Computers, Environment and Urban Systems

156

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Section: Cell Neighborhood Calibrationmentioning

confidence: 99%

Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm

Mustafà

Heppenstall

Omrani

et al. 2018

Computers, Environment and Urban Systems

156

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“…In our second approach, we develop a novel Genetic algorithm based on non-dominated sort called N-Genetic algorithm or NGA. Non-dominated sort Genetic algorithms [16] are a class of Genetic algorithms that are capable of tackling problems of a multi-objective nature. They are also able to perform non-dominated sort operation in addition to standard operations such as crossover and mutation.…”

Section: Network-aware Genetic Algorithmmentioning

confidence: 99%

Network Aware Composition for Internet of Thing Services

Shehu

Safdar

Epiphaniou

2015

TNC

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To enhance the adoption of Internet of Things (IoT) philosophy for the internet, research into IoT service composition has gathered momentum. In a distributed IoT environment, identifying IoT service among a set of similar service offerings that meets both functional and performance requirements of an IoT application has become important. However, the performance of a service cannot be guaranteed. Therefore service's QoS and network characteristics are required to aggregate IoT services. Most existing composition approaches only consider non-network related QoS properties at the application tier. However they do not consider the network parameters such as network latency at the application level in selection and composition of services. Therefore we propose two evolutionary algorithms for IoT service composition that consider not only QoS but also network latency at the IoT application layer. The algorithms are discussed and results of evaluation are presented. The results indicate that our algorithms are efficient in finding QoS optimal and low latency solutions.

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“…Cheng et al [7] combined the non-revisiting mechanism based on binary space partitioning with NSGA-II to realize non-repetitive search and save computing resources. In reference [8], the ''Neighboring-Max'' mode, which not only takes advantage of hybridization but also improves the distribution of the population near the Pareto optimal front, was chosen and used in NSGA-II on the basis of a hybridization-encouraged mechanism. Deb and Himanshu [14], [15] proposed the non dominated sorting genetic algorithm III (NSGA-III) based on reference-point.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization Design of a Notch Filter Based on Improved NSGA-II for Conducted Emissions

et al. 2020

IEEE Access

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This paper develops an improved non dominated sorting genetic algorithm II (NSGA-II) based on objective importance vector γ , abbreviated as γ-NSGA-II. Different importance levels for the multiple objectives are incorporated in the objective importance vector, which is applied to determine the individual selection of sorting individuals in the critical layer. And such an individual selection strategy is developed to the NSGA-II algorithm in order to obtain the optimized solution for a task which has multiple objectives with different importance. The differences between the γ-NSGA-II algorithm and the traditional NSGA-II algorithm are discussed in detail. A notch filter is designed for the conducted emission suppression of a transformer rectifier unit (TRU) used in C919 flight testing, and then the parameters optimization design of a notch filter is discussed and conducted based on the γ-NSGA-II algorithm. The non-linear relationship between the filter's parameters and the suppression effect of the conducted emission is also discussed with the help of an electromagnetic compatibility (EMC) evaluation model based on a back propagation (BP) neural network. The experimental results show that the optimized design of the notch filter is effective and the improved γ-NSGA-II algorithm be more specific. INDEX TERMS Suppression conducted emission, multi-objective optimization, NSGA-II, BP neural network, notch filter, autotransformer rectifier.

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Improved NSGA-II Multi-objective Genetic Algorithm Based on Hybridization-encouraged Mechanism

Cited by 35 publications

References 11 publications

Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm

Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm

Network Aware Composition for Internet of Thing Services

Multi-Objective Optimization Design of a Notch Filter Based on Improved NSGA-II for Conducted Emissions

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