MJS: a modified artificial jellyfish search algorithm for continuous optimization problems

Yildizdan, Gülnur

doi:10.1007/s00521-022-07842-w

Cited by 4 publications

(1 citation statement)

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“…These are called exploitation and exploration, respectively. While global search explores all regions in the search space, local search is responsible for obtaining a better result by searching around the global best result [60]. The performance of the algorithm increases by balancing both global search and local search [61].…”

Section: Improved Carnivorous Plant Algorithmmentioning

confidence: 99%

I-CPA: An Improved Carnivorous Plant Algorithm for Solar Photovoltaic Parameter Identification Problem

Beşkirli,

Dağ

2023

Biomimetics

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The carnivorous plant algorithm (CPA), which was recently proposed for solving optimization problems, is a population-based optimization algorithm inspired by plants. In this study, the exploitation phase of the CPA was improved with the teaching factor strategy in order to achieve a balance between the exploration and exploitation capabilities of CPA, minimize getting stuck in local minima, and produce more stable results. The improved CPA is called the I-CPA. To test the performance of the proposed I-CPA, it was applied to CEC2017 functions. In addition, the proposed I-CPA was applied to the problem of identifying the optimum parameter values of various solar photovoltaic modules, which is one of the real-world optimization problems. According to the experimental results, the best value of the root mean square error (RMSE) ratio between the standard data and simulation data was obtained with the I-CPA method. The Friedman mean rank statistical analyses were also performed for both problems. As a result of the analyses, it was observed that the I-CPA produced statistically significant results compared to some classical and modern metaheuristics. Thus, it can be said that the proposed I-CPA achieves successful and competitive results in identifying the parameters of solar photovoltaic modules.

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Section: Improved Carnivorous Plant Algorithmmentioning

confidence: 99%