Application of Modified NSGA-II Algorithm to Reactive Power Optimization

Ramesh, S.; Kannan, S.; Baskar, S.

doi:10.1007/978-3-642-24043-0_35

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…Preliminary one of the biologic-based approaches are evolutionary algorithms. For example, non-dominated sorting genetic algorithms (Nsgai and Nsgaii) by Srinivas and Deb (1995) and Deb et al (2002), strength Pareto evolutionary algorithm (sPEa and sPEaii) by Zitzler and Thiele (1999), Zitzler et al (2001), modified Nsgaii by Ramesh et al (2011), sub-population genetic algorithm II (SPGAII) by Chang and Chen (2009). While some of the biologic-based approaches mimics directly an artificial immune system (an evolutionary immune approach by Tan et al (2008)), the immune system is also utilized to hybridize with a genetic algorithm approach (Park et al 2009).…”

Section: Description Of a Multi-objective Optimization Problem And Brmentioning

confidence: 99%

Optimization of Geometrically Nonlinear Lattice Girders. Part I: Considering Member Strengths

Talaslioglu

2015

Journal of Civil Engineering and Management

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In this study, the entire weight, joint displacements and load-carrying capacity of tubular lattice girders are simultaneously optimized by a multi-objective optimization algorithm, named Non-dominated Sorting Genetic Algorithm II (NSGAII). Thus, the structural responses of tubular lattice girders are obtained by use of arc-length method as a geometrically nonlinear analysis approach and utilized to check their member strengths at each load step according to the provisions of the American Petroleum Institute specification (API RP2A-LRFD 1993). In order to improve the computing capacity of proposed optimization approach, while the optimization algorithm is hybridized with a radial basis neural network approach, an automatic lattice girder generator is included into the design stage. The improved optimization algorithm, called ImpNSGAII, is applied to both a benchmark lattice girder with 17 members and a lattice girder with varying span lengths and loading conditions. Consequently, it is demonstrated: 1) the optimal lattice girder configuration generated has a higher load-carrying capacity ensuring lower weight and joint displacement values; 2) the use of a multi-objective optimization approach increases the correctness degree in evaluation of optimality quality due to the possibility of performing a trade-off analysis for optimal designations; 3) the computing performance of ImpNSGAII is higher than NSGAII’s.

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Section: Description Of a Multi-objective Optimization Problem And Brmentioning

confidence: 99%

Optimization of Geometrically Nonlinear Lattice Girders. Part I: Considering Member Strengths

Talaslioglu

2015

Journal of Civil Engineering and Management

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“…In the past 30 years, evolutionary multiobjective optimization (EMO) has been popular in research and application [1][2][3][4][5][6][7][8][9][10], and a lot of multiobjective evolutionary algorithms (MOEAs) have been presented [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. The nondominated sorting genetic algorithm (NSGA) being one of the first MOEA was introduced in [12].…”

Section: Introductionmentioning

confidence: 99%

An Improved Multiobjective Algorithm: DNSGA2-PSA

Ding

Wang

2018

Journal of Robotics

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In general, the proximities to a certain diversity along the front and the Pareto front have the equal importance for solving multiobjective optimization problems (MOPs). However, most of the existing evolutionary algorithms give priority to the proximity over the diversity. To improve the diversity and decrease execution time of the nondominated sorting genetic algorithm II (NSGA-II), an improved algorithm is presented in this paper, which adopts a new vector ranking scheme to decrease the whole runtime and utilize Part and Select Algorithm (PSA) to maintain the diversity. In this algorithm, a more efficient implementation of nondominated sorting, namely, dominance degree approach for nondominated sorting (DDA-NS), is presented. Moreover, an improved diversity preservation mechanism is proposed to select a well-diversified set out of an arbitrary given set. By embedding PSA and DDA-NS into NSGA-II, denoted as DNSGA2-PSA, the whole runtime of the algorithm is decreased significantly and the exploitation of diversity is enhanced. The computational experiments show that the combination of both (DDA-NS, PSA) to NSGA-II is better than the isolated use cases, and DNSGA2-PSA still performs well in the high-dimensional cases.

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