Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem

Alshammari, Motaeb Eid; Ramli, Makbul A.M.; Mehedi, Ibrahim M.

doi:10.3390/en15134578

Cited by 8 publications

(3 citation statements)

References 57 publications

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“…The ergodicity of chaos can improve the global search ability of the algorithm.such as chaotic differential evolution algorithm [20] and chaotic grasshopper optimization algorithm [21] as well. The other is used for initialization, where the position of the generated population spreads across the search space, such as the chaotic artificial bee colony algorithm [22] and chaotic grey wolf optimization [23] and so on. The modified salp swarm algorithm initializes the population using circle maps as Eq.…”

Section: Initialization With Chaosmentioning

confidence: 99%

A Novel Variant of the Salp Swarm Algorithm for Engineering Optimization

Jia¹,

Luo

Yin

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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There are many design problems need to be optimized in various fields of engineering, and most of them belong to the NP-hard problem. The meta-heuristic algorithm is one kind of optimization method and provides an effective way to solve the NP-hard problem. Salp swarm algorithm (SSA) is a nature-inspired algorithm that mimics and mathematically models the behavior of slap swarm in nature. However, similar to most of the meta-heuristic algorithms, the traditional SSA has some shortcomings, such as entrapment in local optima. In this paper, the three main strategies are adopted to strengthen the basic SSA, including chaos theory, sine-cosine mechanism and the principle of quantum computation. Therefore, the SSA variant is proposed in this research, namely SCQ-SSA. The representative benchmark functions are employed to test the performances of the algorithms. The SCQ-SSA are compared with the seven algorithms in high-dimensional functions (1000 dimensions), seven SSA variants and six advanced variants on benchmark functions, the experiment reveals that the SCQ-SSA enhances resulting precision and alleviates local optimal problems. Besides, the SCQ-SSA is applied to resolve three classical engineering problems: tubular column design problem, tension/compression spring design problem and pressure vessel design problem. The design results indicate that these engineering problems are optimized with high accuracy and superiority by the improved SSA. The source code is available in the URL: https://github.com/ye-zero/SCQSSA/tree/main/SCQ-SSA.

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Section: Initialization With Chaosmentioning

confidence: 99%

A Novel Variant of the Salp Swarm Algorithm for Engineering Optimization

Jia¹,

Luo

Yin

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

show abstract

“…Most of the traditional power dispatching solutions are based on accurate modeling of the system, mainly using classical methods [1][2][3], meta-heuristic methods [4,5], and hybrid methods [6,7]. However, in modern power systems, the integration of renewable energy brings randomness into the energy output of unit commitment.…”

Section: Introductionmentioning

confidence: 99%

Combination Optimization Method of Grid Section Based on Deep Reinforcement Learning with Accelerated Convergence Speed

Zhao¹,

Wu²,

He³

et al. 2023

Preprint

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Modern power system integrates more and more new energy and use a large number of power electronic equipment. This makes it face more challenges in online optimization and real-time control. Deep reinforcement learning(DRL) has the ability of processing big data and high-dimensional features, as well as the ability of independently learning and optimizing decision-making in complex environments. In this paper, we explore DRL based online combination optimization method of grid section for large complex power system. In our method, to improve the convergence speed of the model, we propose to discretize the output action of the unit and simplify the action space. We also design a reinforcement learning loss function with strong constraints to further improve the convergence speed of the model and facilitate the algorithm to obtain the stable solution. Moreover, to avoid the local optimal solution problem caused by the discretization of the output action, we propose to use the annealing optimization algorithm to make the granularity of the unit output finer. We verify our method on IEEE 118-bus system. The experimental results show that our model has fast convergence speed and better performance, and can obtain stable solutions.

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“…10.3389/fenrg.2023.1269854 classical methods, Larouci et al (2022) improved four metaheuristic algorithms, while Modiri-Delshad et al (2016) presented a new backtracking search algorithm that utilizes crossover and mutation operators to efficiently explore search domains. Among the hybrid methods, Aydın and Özyön (2013) used incremental artificial bee colony (IABC) algorithm, together with local search, to solve the non-convex economic dispatch problem, whereas Alshammari et al (2022) extended IABC and introduced four various chaotic maps in all phases of the artificial bee colony algorithm to generate the random variables.…”

mentioning

confidence: 99%

Combination optimization method of grid sections based on deep reinforcement learning with accelerated convergence speed

Zhao,

Wu,

et al. 2023

Front. Energy Res.

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A modern power system integrates more and more new energy and uses a large number of power electronic equipment, which makes it face more challenges in online optimization and real-time control. Deep reinforcement learning (DRL) has the ability of processing big data and high-dimensional features, as well as the ability of independently learning and optimizing decision-making in complex environments. This paper explores a DRL-based online combination optimization method of grid sections for a large complex power system. In order to improve the convergence speed of the model, it proposes to discretize the output action of the unit and simplify the action space. It also designs a reinforcement learning loss function with strong constraints to further improve the convergence speed of the model and facilitate the algorithm to obtain a stable solution. Moreover, to avoid the local optimal solution problem caused by the discretization of the output action, this paper proposes to use the annealing optimization algorithm to make the granularity of the unit output finer. The proposed method in this paper has been verified on an IEEE 118-bus system. The experimental results show that it has fast convergence speed and better performance and can obtain stable solutions.

show abstract

Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem

Cited by 8 publications

References 57 publications

A Novel Variant of the Salp Swarm Algorithm for Engineering Optimization

A Novel Variant of the Salp Swarm Algorithm for Engineering Optimization

Combination Optimization Method of Grid Section Based on Deep Reinforcement Learning with Accelerated Convergence Speed

Combination optimization method of grid sections based on deep reinforcement learning with accelerated convergence speed

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