Teaching–learning-based optimization with learning experience of other learners and its application

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao

doi:10.1016/j.asoc.2015.08.047

Cited by 96 publications

(32 citation statements)

References 46 publications

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“…In this work, both the SDM and DDM are used to model all of these four PV models. In addition, ten state-of-the-art algorithms including comprehensive learning particle swarm optimizer (CLPSO) [50], hybrid differential evolution with biogeography-based optimization (DE/BBO) [51], generalized oppositional teaching-learningbased optimization (GOTLBO) [22], improved JAYA optimization algorithm (IJAYA) [23], improved whale optimization algorithm (IWOA) [25], teaching-learning-based optimization with learning experience of other learners (LETLBO) [52], modified artificial bee colony algorithm (MABC) [53], opposition-based differential evolution (ODE) [54], teaching-learning-based artificial bee colony (TLABC) [55], and self-adaptive teaching-learning-based optimization (SATLBO) [56] are employed to verify SDO. e parameter settings for these ten compared algorithms are kept the same as those in their original literature and summarized in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.

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Section: Resultsmentioning

confidence: 99%

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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“…The performance of MCSWOA was further verified by some advanced non−WOA variants. Thirteen algorithms consisting of BLPSO [43], CLPSO [44], CSO [45], DBBO [46], DE/BBO [47], GOTLBO [14], IJAYA [17], LETLBO [48], MABC [49], ODE [50], SATLBO [15], SLPSO [51], and TLABC [24] were employed for comparison in this subsection. The result of Wilcoxon's rank sum test tabulated in Table 12 shows that MCSWOA performed very competitively and outperformed all of the other 13 algorithms on 9 cases except Case 4, on which MCSWOA was surpassed by ODE and DBBO, and tied by TLABC.…”

Section: Comparison With Advanced Non−woa Variantsmentioning

confidence: 99%

Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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Extracting accurate values for involved unknown parameters of solar photovoltaic (PV) models is very important for modeling PV systems. In recent years, the use of metaheuristic algorithms for this problem tends to be more popular and vibrant due to their efficacy in solving highly nonlinear multimodal optimization problems. The whale optimization algorithm (WOA) is a relatively new and competitive metaheuristic algorithm. In this paper, an improved variant of WOA referred to as MCSWOA, is proposed to the parameter extraction of PV models. In MCSWOA, three improved components are integrated together: (i) Two modified search strategies named WOA/rand/1 and WOA/current-to-best/1 inspired by differential evolution are designed to balance the exploration and exploitation; (ii) a crossover operator based on the above modified search strategies is introduced to meet the search-oriented requirements of different dimensions; and (iii) a selection operator instead of the “generate-and-go” operator used in the original WOA is employed to prevent the population quality getting worse and thus to guarantee the consistency of evolutionary direction. The proposed MCSWOA is applied to five PV types. Both single diode and double diode models are used to model these five PV types. The good performance of MCSWOA is verified by various algorithms.

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“…It is seen that the proposed algorithm outperforms than traditional TLBO algorithm. For improving the global performance of traditional TLBO algorithm, Zou et al, have developed an improved variant of TLBO algorithm based on learning experience [28]. Further, a copy operator is also integrated in TLBO algorithm and called it LETLBO.…”

Section: Related Workmentioning

confidence: 99%

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

Kumar

Dahiya²,

Malik³

et al. 2019

IJCAI

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This paper presents a new variant of teaching learning based optimization (TLBO) algorithm for solving global optimization problems. The performance of the TLBO algorithm depends on coordination of teacher phase and learner phase. It is noticed that sometimes performance of TLBO algorithm is affected due to lack of diversity in teacher and learner phases. In this work, a new variant of TLBO algorithm is proposed based on genetic crossover and mutation strategies. These strategies are inculcated in TLBO algorithm for improving its search mechanism and convergence rate. Genetic mutation strategy is applied in teacher phase of TLBO algorithm for improving the mean knowledge of leaners. While, Crossover strategy is applied in learner phase of TLBO algorithm to find the good learner. The effectiveness of the proposed algorithm is tested on several bench mark test functions of CEC'14. From simulation results, it is stated that the proposed algorithm provides more optimized results in comparison to same class of algorithms.Povzetek: V prispevku je opisan nov globalni optimizacijski algoritem na osnovi učenja optimizacije (TLBO).

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Teaching–learning-based optimization with learning experience of other learners and its application

Cited by 96 publications

References 46 publications

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

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