A New Bonobo optimizer (BO) for Real-Parameter optimization

Das, Amit Kumar; Pratihar, Dilip Kumar

doi:10.1109/tensymp46218.2019.8971108

Cited by 39 publications

(42 citation statements)

References 17 publications

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“…Bonobo optimizer is a new optimization algorithm that was proposed in [22]. In BO, the social reproductive behavior of the bonobo is modeled based on four mating strategies: promiscuous, restrictive, consortship, and extra-group mating.…”

Section: Bonobo Optimizermentioning

confidence: 99%

“…Consortship and Extra-Group Mating If r is greater than ρ ph , consortship and extra-group mating can occur. However, a new random number r 2 between [0, 1] is used with a probability of extra-group mating ρ xg to represent the occurrence of extra-group mating when r 2 is less than or equal to ρ xg as follows [22,29]:…”

Section: Creation Of New Bonobomentioning

confidence: 99%

“…In this paper, a new smart algorithm named Bonobo Optimizer [22] was employed and improved using a quasi-oppositional method, and the modified Quasi Oppositional BO (QOBO) was utilized for optimal economic designing of a stand-alone microgrid hybrid system in Aswan, Egypt, where the hybrid system consists of RES (PV panels, WT and BESS) with diesel generators. Then, the results were compared between the traditional and improved BO.…”

Section: Introductionmentioning

confidence: 99%

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Development and Implementation of a Novel Optimization Algorithm for Reliable and Economic Grid-Independent Hybrid Power System

et al. 2020

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Recently, fast uptake of renewable energy sources (RES) in the world has introduced new difficulties and challenges; one of the most important challenges is providing economic energy with high efficiency and good quality. To reach this goal, many traditional and smart algorithms have been proposed and demonstrated their feasibility in obtaining the optimal solution. Therefore, this paper introduces an improved version of Bonobo Optimizer (BO) based on a quasi-oppositional method to solve the problem of designing a hybrid microgrid system including RES (photovoltaic (PV) panels, wind turbines (WT), and batteries) with diesel generators. A comparison between traditional BO, the Quasi-Oppositional BO (QOBO), and other optimization techniques called Harris Hawks Optimization (HHO), Artificial Electric Field Algorithm (AEFA) and Invasive Weed Optimization (IWO) is carried out to check the efficiency of the proposed QOBO. The QOBO is applied to a stand-alone hybrid microgrid system located in Aswan, Egypt. The results show the effectiveness of the QOBO algorithm to solve the optimal economic design problem for hybrid microgrid power systems.

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Section: Bonobo Optimizermentioning

confidence: 99%

Section: Creation Of New Bonobomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development and Implementation of a Novel Optimization Algorithm for Reliable and Economic Grid-Independent Hybrid Power System

et al. 2020

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“…Bonobo optimizer (BO) is an algorithm inspired by the reproductive strategies and social behavior of bonobos. Several improvements have been made to solve BO control problems and standards to be consistent with nature [24]. In the literature, BO has been used in several recent applications such as finding the best preventive maintenance interval with the lowest overall maintenance cost [25], and the best prediction accuracy was seen with the adaptive neuro-fuzzy inference method (ANFIS) tuned by BO [26].…”

Section: Introductionmentioning

confidence: 99%

Development of an Improved Bonobo Optimizer and Its Application for Solar Cell Parameter Estimation

et al. 2021

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Recently, photovoltaic (PV) energy has been considered one of the most exciting new technologies in the energy sector. PV power plants receive considerable attention because of their wide applications. Consequently, it is important to study the parameters of the solar cell model to control and determine the characteristics of the PV systems. In this study, an improved bonobo optimizer (IBO) was proposed to improve the performance of the conventional bonobo optimizer (BO). Both the IBO and the BO were utilized to obtain the accurate values of the unknown parameters of different mathematical models of solar cells. The proposed IBO improved the performance of the conventional BO by enhancing the exploitation (local search) and exploration (global search) phases to find the best optimal solution, where the search space was reduced using Levy flights and the sine–cosine function. Levy flights enhance the explorative phase, whereas the sine–cosine function improves the exploitation phase. Both the proposed IBO and the conventional BO were applied on single, double, and triple diode models of solar cells. To check the effectiveness of the proposed algorithm, statistical analysis based on the results of 20 runs of the optimization program was performed. The results obtained by the proposed IBO were compared with other algorithms, and all results of the proposed algorithm showed their durability and exceeded other algorithms.

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“…Çözümü çok zor olan yada çok fazla zaman gerektiren problemler için global optimumdan feragat edilerek yaklaşık çözümü bulmaya yarayan meta-sezgisel algoritmalar geliştirilmiştir. Doğada çeşitli ilke ve mekanizmalara bağlı olarak gerçekleşen bir çok olay vardır ve bu olayların matematiksel modellenmesiyle optimizasyon problemleri oldukça verimli şekillerde çözülebilmektedir [1].…”

Section: Introductionunclassified

Global Optimizasyon Problemleri için Kaotik Bonobo Algoritması

Bazna¹,

Akyol²

2021

European Journal of Science and Technology

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ÖzOptimizasyon algoritmaları, global optimumdan feragat edilerek yaklaşık en iyi çözümü bulmayı amaçlayan algoritmalardır. Bu çalışmada incelenen Bonobo Optimizasyon (BO) Algoritması ise sürü zekasına dayanan bir algoritma olup, bonoboların sosyal davranışlarının ve üreme stratejilerinin matematiksel modellenmesine dayanmaktadır. Bonobolar, yaşadıkları topluluk içinde çeşitli büyüklük ve özelliklerde gruplar oluşturarak, farklı amaçlar için birbirlerinden ayrılıp bir süre sonra yeniden bir araya gelmektedirler. Bonoboların üreme stratejisi incelendiğinde rasgele çiftleşme, kısıtlayıcı çiftleşme, konsorsiyum ve grup dışı çiftleşme gibi dört farklı stratejiyi benimsedikleri görülmektedir. Bonoboların bu doğal davranışları çeşitli optimizasyon problemlerini çözmek için kullanılmıştır. BO'yu diğer sürü zekasına dayalı algoritmalardan ayıran en önemli özelliği ise arama ajanlarının güncelleme mekanizmaları ve bunlarla ilişkili parametreler ve çiftleşme ortaklarının seçim yöntemidir. Bu çalışmada BO incelenip, algoritmada kullanılan parametreler Chebyshev, Circle, Gauss, Iterative, Logistic ve Tent kaotik haritaları kullanılarak yeniden üretilmiştir. Performansları karşılaştırmak için sekiz adet kalite testi fonksiyonu kullanılmıştır. Buna göre kaotik haritalar kullanılarak oluşturulan yeni algoritmalardan elde edilen sonuçların, klasik BO'ya göre daha verimli olduğu görülmüştür.

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A New Bonobo optimizer (BO) for Real-Parameter optimization

Cited by 39 publications

References 17 publications

Development and Implementation of a Novel Optimization Algorithm for Reliable and Economic Grid-Independent Hybrid Power System

Development and Implementation of a Novel Optimization Algorithm for Reliable and Economic Grid-Independent Hybrid Power System

Development of an Improved Bonobo Optimizer and Its Application for Solar Cell Parameter Estimation

Global Optimizasyon Problemleri için Kaotik Bonobo Algoritması

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