Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation

Moosavi, Seyyed Hamid Samareh; Bardsiri, Vahid Khatibi

doi:10.1016/j.engappai.2017.01.006

Cited by 229 publications

(50 citation statements)

References 56 publications

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“…Such an assumption reveals the importance of new optimizers in different fields because the capability of an optimizer to solve a series of problems does not assure its effectiveness in other problems [23]. Some of the recently developed optimizers, such as water cycle algorithm (WCA) [24], salp swarm algorithm (SSA) [25], satin bowerbird optimization (SBO) [26], and grey wolf optimizer (GWO), have not been employed in the process of optimal placement of WTs within a WF. This prompted the authors of the present paper to use them to determine the optimal distribution of WTs.…”

Section: Year Optimizer Remarksmentioning

confidence: 99%

“…Throughout the autumn and winter seasons, bowerbirds disuse their own locations and stir to open woodlands to devour fruits and pests. However, during the breeding period, bowerbirds drove together in little groups, inhabiting territories that they clearly inhabit year after year [26,30]. According to the SBO technique, adults build up a trellis with various materials on the ground during the mating period.…”

Section: Satin Bowerbird Optimizationmentioning

confidence: 99%

“…According to the SBO technique, adults build up a trellis with various materials on the ground during the mating period. Based on the moralities of a bowerbird's life, the SBO approach is applied to update the new position as follows [26,30]:…”

Section: Satin Bowerbird Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

The Application of Water Cycle Optimization Algorithm for Optimal Placement of Wind Turbines in Wind Farms

et al. 2019

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Wind farms (WFs) include an enormous number of wind turbines (WTs) in order to achieve high capacity. The interaction among WTs reduces the extracted amount of wind energy because wind speed decreases in the wake region. The optimal placement of WTs within a WF is therefore vital for achieving high performance. This permits as many WTs as possible to be installed inside a narrow region. In this work, the water cycle algorithm (WCA), a recently developed optimizer, was employed to identify the optimal distribution of WTs. Minimization of the total cost per kilowatt was the objective of the optimization process. Two different cases were considered: the first assumed constant wind speed with variable wind direction, while the second applied variable wind speed with variable wind direction. The results obtained through the WCA optimizer were compared with other algorithms, namely, salp swarm algorithm (SSA), satin bowerbird optimization (SBO), grey wolf optimizer (GWO), and differential evolution (DE), as well as other reported works. WCA gave the best solution compared to other reported and programmed algorithms, thus confirming the reliability and validity of WCA in optimally configuring turbines in a wind farm for both the studied cases.

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Section: Year Optimizer Remarksmentioning

confidence: 99%

Section: Satin Bowerbird Optimizationmentioning

confidence: 99%

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The Application of Water Cycle Optimization Algorithm for Optimal Placement of Wind Turbines in Wind Farms

et al. 2019

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“…This optimization algorithm is new in terms of application toa power system but, in [19], its unimodal and multimodal standard test functions were tested. It showed better performance compared with other optimization techniques such as the Antlion algorithm (ALO), Firefly algorithm (FA), Artificial bee colony algorithm (ABC), Particle swarm optimization algorithm (PSO) and Genetic algorithm (GA).…”

Section: Satin Bowerbird Optimization (Sbo)mentioning

confidence: 99%

Real-Power Rescheduling of Generators for Congestion Management Using a Novel Satin Bowerbird Optimization Algorithm

Chintam¹,

Daniel²

2018

Energies

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Abstract:In this paper, an efficient meta-heuristic satin bowerbird optimization (SBO) algorithm is presented for congestion management (CM) in the deregulated power system. The main objective of CM is to relieve congestion in the transmission lines using a generation rescheduling-based approach, while satisfying all the constraints with minimum congestion cost. The SBO is a nature-inspired algorithm, developed based on the 'male-attracts-the-female for breeding' principle of the specialized stick structure mechanism of satin birds. The proposed approach is effectively tested on small and large test systems, namely, modified IEEE 30-bus, modified IEEE 57-bus, and IEEE 118-bus test systems. The constraints like line loading, line limits, generator limits, and bus voltage impact, etc., are incorporated into this study. The proposed technique gives superior results with regards to congestion cost and losses compared with various recent optimization algorithms.

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“…In recent years, new metaheuristic algorithms have been proposed, such as cuckoo search (CS) [14], grey wolf optimization (GWO) [32], dragonfly algorithm (DA) [31] and flower pollination algorithm (FPA) [42]. Seyed H. S. M, et al proposed the satin bowerbird optimization algorithm in 2017 [33]. To enhance the optimization ability of the satin bowerbird optimization (SBO) algorithm, in this paper, a novel quantum-inspired SBO with the Bloch coordinates of quantum bits (QBSBO) is proposed.…”

mentioning

confidence: 99%

Quantum-inspired satin bowerbird algorithm with Bloch spherical search for constrained structural optimization

Zhang¹,

Zhou²,

Zhou³

et al. 2021

JIMO

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To enhance the optimization ability of the satin bowerbird optimization (SBO) algorithm, in this paper, a novel quantum-inspired SBO with Bloch spherical search is proposed. In this algorithm, satin bowerbirds are encoded using qubits described on the Bloch sphere, each satin bowerbird occupies three locations in the search space and each location represents an optimization solution. Using the search method of general SBO to adjust the two parameters of the qubit, qubit rotation is performed on the Bloch sphere, which simultaneously updates the three locations occupied by a qubit and quickly approaches the global optimal solution. Finally, the experimental results of five examples of structural engineering design show that the proposed algorithm is superior to other state-of-the-art metaheuristic algorithms in terms of the performance measures.

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Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation

Cited by 229 publications

References 56 publications

The Application of Water Cycle Optimization Algorithm for Optimal Placement of Wind Turbines in Wind Farms

The Application of Water Cycle Optimization Algorithm for Optimal Placement of Wind Turbines in Wind Farms

Real-Power Rescheduling of Generators for Congestion Management Using a Novel Satin Bowerbird Optimization Algorithm

Quantum-inspired satin bowerbird algorithm with Bloch spherical search for constrained structural optimization

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