Economic Load Dispatch Using Hybrid MpBBO-SQP Algorithm

Al-Roomi, Ali R.; El‐Hawary, Mohamed E.

doi:10.1007/978-3-319-30235-5_11

Cited by 15 publications

(3 citation statements)

References 48 publications

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“…Each unit has a non-smooth cost function f j (P j ) = a j + b j P j + c j P 2 j + |d j sin[e j (P min j − P j )]|;[ a j , b j , c j , d j , e j , P min j , P max j ] is the parameters set of unit j, which are available in ref. 55. The goal is to optimize the output powers of d units, that is,…”

Section: Dispatch Of Power Gridmentioning

confidence: 99%

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Machine learning-enabled globally guaranteed evolutionary computation

Wei

et al. 2023

Nat Mach Intell

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Evolutionary computation, for example, particle swarm optimization, has impressive achievements in solving complex problems in science and industry; however, an important open problem in evolutionary computation is that there is no theoretical guarantee of reaching the global optimum and general reliability; this is due to the lack of a unified representation of diverse problem structures and a generic mechanism by which to avoid local optima. This unresolved challenge impairs trust in the applicability of evolutionary computation to a variety of problems. Here we report an evolutionary computation framework aided by machine learning, named EVOLER, which enables the theoretically guaranteed global optimization of a range of complex non-convex problems. This is achieved by: (1) learning a low-rank representation of a problem with limited samples, which helps to identify an attention subspace; and (2) exploring this small attention subspace via the evolutionary computation method, which helps to reliably avoid local optima. As validated on 20 challenging benchmarks, this method finds the global optimum with a probability approaching 1. We use EVOLER to tackle two important problems: power grid dispatch and the inverse design of nanophotonics devices. The method consistently reached optimal results that were challenging to achieve with previous state-of-the-art methods. EVOLER takes a leap forwards in globally guaranteed evolutionary computation, overcoming the uncertainty of data-driven black-box methods, and offering broad prospects for tackling complex real-world problems.

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Section: Dispatch Of Power Gridmentioning

confidence: 99%

“…30,45. The powerdispatch dataset, that is, the Economic Load Dispatch Test Systems Repository 55 , is available at https://al-roomi.org/economic-dispatch. The dataset of inverse design of multilayer thin-film structures is available at Gitbub (https://github.com/BinLi-BUPT/EVOLER.git) 71 , which was generated by the FDTD method.…”

Section: Data Availabilitymentioning

confidence: 99%

Machine learning-enabled globally guaranteed evolutionary computation

Wei

et al. 2023

Nat Mach Intell

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“…Moreover, this method involves multiple searches to discover the locality with the best-suited global optimal solutions. The metropolis biogeography-based optimization-sequential quadratic programming (MpBBO-SQP) algorithm has been proposed to cope with the weakness of BBO [62]. A metropolis criterion of the simulated annealing (SA) algorithm is introduced in BBO to provide control over migrated individuals, thus improving the exploration quality.…”

Section: Introductionmentioning

confidence: 99%

A Cauchy-Gaussian Quantum-Behaved Bat Algorithm Applied to Solve the Economic Load Dispatch Problem

et al. 2020

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In this paper, a novel Cauchy-Gaussian quantum-behaved bat algorithm (CGQBA) is applied to solve the economic load dispatch (ELD) problem. The bat algorithm (BA) is an acknowledged metaheuristic optimization algorithm owing to its performance. However, the classical BA presents some weaknesses, such as premature convergence. To withstand the drawbacks of the BA, quantum mechanics theories and Gaussian and Cauchy operators are integrated into the standard BA to enhance its effectiveness. Since the economic load dispatch is a nonlinear, complex and constrained optimization problem, its main objective is to reduce the total generation cost while matching the equality and inequality constraints of the system. The validity of the CGQBA is tested on six standard benchmark functions with different characteristics. The numerical results indicate that the CGQBA is effective and superior to many other algorithms. Moreover, the CGQBA is applied to solve the ELD problems on various test systems including 3,6,20, 40,110 and 160 implemented generating units. The simulation results illustrate the strength of the CGQBA compared with other algorithms recently reported in the literature.

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References

2021

Optimal Coordination of Power Protective Devices With Illustrative Examples

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Economic Load Dispatch Using Hybrid MpBBO-SQP Algorithm

Cited by 15 publications

References 48 publications

Machine learning-enabled globally guaranteed evolutionary computation

Machine learning-enabled globally guaranteed evolutionary computation

A Cauchy-Gaussian Quantum-Behaved Bat Algorithm Applied to Solve the Economic Load Dispatch Problem

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