Improving monarch butterfly optimization through simulated annealing strategy

Yang, Dan; Wang, Xitong; Tian, Xin; Zhang, Yonggang

doi:10.1007/s12652-020-01702-y

Cited by 14 publications

(8 citation statements)

References 29 publications

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“…Yang et al [98] presented a new version of MBO with simulated annealing (SA) strategy called SAMBO. The SA strategy was put in the migration operator and butterfly adjusting operator.…”

Section: Kelotra and Pandeymentioning

confidence: 99%

Clustering-Based Monarch Butterfly Optimization for Constrained Optimization

Huang¹,

Cui²,

Wei³

et al. 2020

IJCIS

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Monarch butterfly optimization (MBO) algorithm is a newly-developed metaheuristic approach that has shown striking performance on several benchmark problems. In order to enhance the performance of MBO, many scholars proposed various strategies for benchmark evaluation and practical applications. As an application of artificial intelligence (AI), machine learning (ML) developed fast and succeeded in dealing with so many complicated problems. However, up to now, ML did not use to improve the performance of MBO algorithm. In this paper, one of ML techniques, clustering, is introduced into the basic MBO algorithm, so an improved clustering-based MBO namely CBMBO is proposed. In CBMBO algorithm, the whole population is divided into two subpopulations according to k-means clustering. Also, only the individuals having better fitness can be passed to the next generation instead of accepting all the updated individuals used in the basic MBO algorithm. In order to improve the diversity of the population, few individuals having worse fitness are accepted as new individuals. In order to verify the performance of our proposed CBMBO algorithm, CBMBO is compared with six basic and four improved metaheuristic algorithms on twenty-eight CEC 2017 constrained problems with dimension of 30, 50, and 100, respectively. The experimental results suggest a significant addition to the portfolio of computational intelligence techniques.

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“…Yang et al [98] presented a new version of MBO with simulated annealing (SA) strategy called SAMBO. The SA strategy was put in the migration operator and butterfly adjusting operator.…”

Section: Kelotra and Pandeymentioning

confidence: 99%

Clustering-Based Monarch Butterfly Optimization for Constrained Optimization

Huang¹,

Cui²,

Wei³

et al. 2020

IJCIS

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“…Nowadays, ithere ihas ibeen ia ilot iof iinterest ito ide velop imetaheuristic ioptimization ialgorithms (Dhiman, Guo, & Kaur, 2018;Dhi-man & Kumar, 2019b;Dhiman & Kaur, 2019b;Dhiman & Kumar, 2019a;Dhiman, Singh, Kaur, & Maini, 2019;Dhiman, 2019b;Singh et al, 2019;Dhiman, 2019aDhiman, , 2019cDehghani, Montazeri, Malik, Dhiman, & Kumar, 2019;Maini & Dhiman, 2018;Pallavi & Dhiman, 2018;Garg & Dhiman, 2020;S. Kaur, Awasthi, Sangal, & Dhiman, 2020) iwhich iare icomputationally iine xpensive, ifle xible, iand igradient ifree (Ragmani, Elomri, Abghour, Moussaid, & Rida, 2019;D. Yang, Wang, Tian, & Zhang, 2020;Balasubramanian & Marichamy, 2020).…”

Section: Introductionmentioning

confidence: 99%

A novel algorithm for global optimization: Rat Swarm Optimizer

Dhiman¹,

Garg²,

Nagar

et al. 2020

J Ambient Intell Human Comput

327

126

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“…The proposed paper mainly focused on improvement of power quality in all aspects of irregular load conditions using best optimal location and control with butterfly optimizer (BO) [1], monarch butterfly optimization (MBO) [2] and gray wolf optimizer (GWO) [3] techniques. The power quality (PQ) works with voltage variations, recurrence variety, drifters, and other nonlinear load-related issues [4].…”

Section: Introductionmentioning

confidence: 99%

A novel optimization-based power quality enhancement using dynamic voltage restorer and distribution static compensator

Chiranjivi

Swarnasri

2022

IJEECS

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The power assurance <span lang="EN-US">for distinct purchaser in the nation must be guaranteed through electric power distribution (EPD) system, subsequently EPD must be reliable, and well facilitating with power quality (PQ) in distribution framework. It will be much significant to keep up with PQ in power distribution (PD) framework for service continuity. The PQ issue basically emerge in distribution framework are voltage swell, sag, harmonics, and disparities in power. In this manuscript, the PQ development in photovoltaic (PV) PD framework for IEEE 33 and 57 bus frameworks is deliberated as the aim. The improvement is satisfied with FACTS pay gadgets of dynamic voltage restorer (DVR) and distributed static compensator (DSTATCOM). The butterfly optimizer (BO) and gray wolf optimizer (GWO) framework is deliberated for suggested work that implies a switching gadget and it compares the yield signal of DSTATCOM and DVR. In this proposed system DVR is compensated with an advanced GWO and DSTATCOM with BO for optimal location of FACTS devices in IEEE 33 and 57 bus systems are discussed like in the conventional paper as 13th bus for IEEE 33 system & 30th bus for IEEE 57 system.</span>

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Improving monarch butterfly optimization through simulated annealing strategy

Cited by 14 publications

References 29 publications

Clustering-Based Monarch Butterfly Optimization for Constrained Optimization

Clustering-Based Monarch Butterfly Optimization for Constrained Optimization

A novel algorithm for global optimization: Rat Swarm Optimizer

A novel optimization-based power quality enhancement using dynamic voltage restorer and distribution static compensator

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