A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment

Wang, Ying; Yang, Zhile; Guo, Yuanjun; Zhou, Bowen; Zhu, Xiaoliang

doi:10.3390/app9091776

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…Yang et al, 2017), Enh-hGADE (Trivedi et al, 2015), iDA-PSO (Khunkitti et al, 2019), BFMO (Pan et al, 2021), BCSO (Y. Wang et al, 2019) and BBA (Reddy et al, 2017). Form Table 10, HBBA and BBA have the lowest cost and the same value as all of the compared algorithms (563937.307 $).…”

Section: Objective Functionmentioning

confidence: 95%

A Novel Hybrid Binary Bat Algorithm for Global Optimization

Liang,

Pang,

Lin

et al. 2024

International Journal of Swarm Intelligence Research

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In this article, a novel hybrid binary bat algorithm named HBBA is proposed for global optimization problems. First, to avoid simultaneous updating of bat velocity's dimensional components, i.e., elements of velocity vector, a random black hole model is modified to adapt to binary algorithm for updating in unknown spaces for each dimensional component individually. Through this way, the search ability of bats around the current group best is increased greatly. Second, a time-varying v-shaped transfer function, rather than a time-invariant one as in closely related works, is proposed to map velocity in continuous search space to a binary one. This accelerates the speed to switch individuals' positions, i.e., solutions in binary space. Third, a chaotic map is utilized to replace monotonous parameters in original binary bat algorithm, which is beneficial for avoiding premature convergence. Simulation results demonstrate the effectiveness of the proposed algorithm by three types of benchmark functions and unit commitment problem.

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Section: Objective Functionmentioning

confidence: 95%

A Novel Hybrid Binary Bat Algorithm for Global Optimization

Liang,

Pang,

Lin

et al. 2024

International Journal of Swarm Intelligence Research

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“…Taking into consideration economic performances, this paper proposes an optimized energy-conversion interface model that simplifies the complex multi-energy system into a multi-input-multi-output dual-port one and provides a decision-making method for system planning. A high-dimensional mixed-integer optimization problem is proposed in [6], in which, for a high-dimensional multi-source system, the power system unit commitment, i.e., the techno-economic sources scheduling, is solved by applying a novel binary competitive swarm optimizer.…”

Section: Advances On Intelligent Energy Management Systemsmentioning

confidence: 99%

Intelligent Energy Management of Electrical Power Systems

Sechilariu

2020

Applied Sciences

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Smart grid implementation is facilitated by multi-source energy systems development, i.e., microgrids, which are considered the key smart grid building blocks. Whether they are alternative current (AC) or direct current (DC), high voltage or low voltage, high power or small power, integrated into the distribution system or the transmission network, multi-source systems always require an intelligent energy management that is integrated into the power system. A comprehensive intelligent energy system aims at providing overall energy efficiency with regard to the following: increased power generation flexibility, increased renewable generation systems, improved energy consumption, reduced CO2 emission, improved stability, and minimized energy cost. This Special Issue presents recent key theoretical and practical developments that concern the models, technologies, and flexible solutions to facilitate the following optimal energy and power flow strategies: the techno-economic model for optimal sources dispatching (mono and multi-objective energy optimization), real-time optimal scheduling, and real time optimization with model predictive control.

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“…Metaheuristic algorithms are a subset of approximate algorithms that have been used for solving many NP-hard problems in different fields of science, such as engineering design [39][40][41][42][43][44][45][46][47][48][49][50], task scheduling [51][52][53], engineering prediction [54][55][56][57][58], and optimal power flow [59][60][61][62][63][64] problems. When tackling the FS problem, metaheuristic algorithms have shown outstanding results in prior studies [65][66][67][68].…”

Section: Introductionmentioning

confidence: 99%

Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data

et al. 2022

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Many metaheuristic approaches have been developed to select effective features from different medical datasets in a feasible time. However, most of them cannot scale well to large medical datasets, where they fail to maximize the classification accuracy and simultaneously minimize the number of selected features. Therefore, this paper is devoted to developing an efficient binary version of the quantum-based avian navigation optimizer algorithm (QANA) named BQANA, utilizing the scalability of the QANA to effectively select the optimal feature subset from high-dimensional medical datasets using two different approaches. In the first approach, several binary versions of the QANA are developed using S-shaped, V-shaped, U-shaped, Z-shaped, and quadratic transfer functions to map the continuous solutions of the canonical QANA to binary ones. In the second approach, the QANA is mapped to binary space by converting each variable to 0 or 1 using a threshold. To evaluate the proposed algorithm, first, all binary versions of the QANA are assessed on different medical datasets with varied feature sizes, including Pima, HeartEW, Lymphography, SPECT Heart, PenglungEW, Parkinson, Colon, SRBCT, Leukemia, and Prostate tumor. The results show that the BQANA developed by the second approach is superior to other binary versions of the QANA to find the optimal feature subset from the medical datasets. Then, the BQANA was compared with nine well-known binary metaheuristic algorithms, and the results were statistically assessed using the Friedman test. The experimental and statistical results demonstrate that the proposed BQANA has merit for feature selection from medical datasets.

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A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment

Cited by 8 publications

References 32 publications

A Novel Hybrid Binary Bat Algorithm for Global Optimization

A Novel Hybrid Binary Bat Algorithm for Global Optimization

Intelligent Energy Management of Electrical Power Systems

Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data

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