Yin-Yang-pair Optimization: A novel lightweight optimization algorithm

Punnathanam, Varun; Kotecha, Prakash

doi:10.1016/j.engappai.2016.04.004

Cited by 105 publications

(25 citation statements)

References 37 publications

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“…This optimizer employs two random points (p l and p 2 ) to search the problem hyperspace. The points p l and p 2 are generated in the initial step of YYPO algorithm inside the domain of (0,1) and their fitness are assessed [2] [3]. The point with better fitness is denoted as p 1 and the other one is denoted as p 2 .…”

Section: Power Quality Controlstrategymentioning

confidence: 99%

YYPO-PSO Algorithm Based Power Quality Control in a Grid-Connected Hybrid System

2021

IJATCSE

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Micro-grid is a potential solution for the integration and management of renewable energy generation. Power generation through renewable energy sources has become more preferential and cost-effective, by articulating small scale distributed energy resources, micro-grids are becoming an alternative method in electrical power generation at the distribution voltage level. The increment of nonlinear loads and power electronic interfaced distribution generation system (DG) in the grid creates power quality issues like harmonic distortions in the distributed power system [6] [18]. The microgrid proposed in this paper consists of a photovoltaic array which represents the main generation unit and a three-phase induction generator that supplements the variable power generated by the photovoltaic array; a battery bank is included in the micro-grid to reduce the burden of the power generated by the micro-grid during the peak period [19] [20]. The power electronic interface maintains the necessary adaptability, security and reliability of operation between renewable sources and the distribution system [18]. In this paper, a comprehensive survey on micro-grid to improve the power quality parameters like THD is taken as the main objective with the help of suitable hybrid optimization algorithm, harmonic filters, controllers and battery storage.

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Section: Power Quality Controlstrategymentioning

confidence: 99%

YYPO-PSO Algorithm Based Power Quality Control in a Grid-Connected Hybrid System

2021

IJATCSE

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“…To further verify the effectiveness of ILxBBO, ILxBBO is compared with other IOAs on the 30-dimensional functions. ese algorithms include YYPO [43], DPCABC [44], DFnABC [45], FMPSO [46], GLPSO [47], HFPSO [48], and MEGWO [49], which are the latest and most famous IOAs and are competitive and representative. DPCABC and DFnABC are ABC variants, and MEGWO is a GWO variant.…”

Section: Comparison With Other Ioasmentioning

confidence: 99%

Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

et al. 2020

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Laplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO’s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-global-best guiding operator is created for guiding the worst habitat mainly to enhance the exploitation of LxBBO. Second, a dynamic two-differential perturbing operator is proposed for the first two best habitats’ updating to improve the global search ability in the early search phase and the local one in the late search one, respectively. Third, an improved Laplace migration operator is formulated for other habitats’ updating to improve the search ability and the operability. Finally, some measures such as example learning, mutation operation removing, and greedy selection are adopted mostly to reduce the computation complexity of LxBBO. A lot of experimental results on the complex functions from the CEC-2013 test set show ILxBBO obtains better performance than LxBBO and quite a few state-of-the-art algorithms do. Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA).

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“…Hence, a comprehensive study on parameter settings of NRO algorithm could be discussed in the future. [69], WDE [94], DE [12], LSA [51], CSA [96] and YYPO [95] are employed to compare with NRO. The population size of all compared algorithms is set to 10 D , and MaxFEs are set to 10000 D…”

Section: Parameter Analysis Of Nromentioning

confidence: 99%

Nuclear Reaction Optimization: A Novel and Powerful Physics-Based Algorithm for Global Optimization

et al. 2019

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Meta-heuristic algorithms have gained substantial popularity in recent decades and have focused on applications in a wide spectrum of fields. In this paper, a new and powerful physics-based algorithm named nuclear reaction optimization (NRO) is presented. Meanwhile, NRO imitates the nuclear reaction process and consists of two phases, namely, a nuclear fission (NFi) phase and a nuclear fusion (NFu) phase. The Gaussian walk and differential evolution operators between nucleus and neutron are employed for exploitation and appropriate exploration in the (NFi) phase, respectively. Meanwhile, the variants of differential evolution operator are utilized for exploration in the NFu phase, which consists of the ionization and fusion stages. Additionally, variants of Levy flight are used for random searching to escape from the local optima in each stage of NFu phase. The exploration and exploitation abilities of NRO can be balanced due to a combination of the two phases. Both constrained and unconstrained benchmark functions are employed for testing the performance of NRO. To make comparisons between NRO and the state-of-the-art algorithms, twenty-three classic benchmark functions and twenty-night modern benchmark functions are performed. Moreover, three engineering design optimization problems are solved as constrained benchmark functions by using NRO and the compared algorithms. The results illustrate that the proposed nuclear reaction optimization algorithm is a potential and powerful approach for global optimization.

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Yin-Yang-pair Optimization: A novel lightweight optimization algorithm

Cited by 105 publications

References 37 publications

YYPO-PSO Algorithm Based Power Quality Control in a Grid-Connected Hybrid System

YYPO-PSO Algorithm Based Power Quality Control in a Grid-Connected Hybrid System

Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

Nuclear Reaction Optimization: A Novel and Powerful Physics-Based Algorithm for Global Optimization

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