A binary bat algorithm with improved crossover operators and Cauchy mutation for unit commitment problem

Power system operators are faced with the problem of unit commitment belonging to mixed integer programming, which becomes very complicated, as units become large-scale and highly constrained. Because unit commitment problem is a binary problem with commitment and de-commitment, a discrete/binary op...

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Vydáno v:The Journal of supercomputing Ročník 80; číslo 8; s. 11261 - 11292
Hlavní autoři: Pang, Aokang, Liang, Huijun, Lin, Chenhao, Yao, Lei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.05.2024
Springer Nature B.V
Témata:
Algorithms
Compilers
Computer Science
Constraints
Cost control
Crossovers
Genetic algorithms
Heuristic
Integer programming
Interpreters
Iterative methods
Linear programming
Mixed integer
Mutation
Operators (mathematics)
Optimization
Optimization algorithms
Power dispatch
Processor Architectures
Programming Languages
Scheduling
Unit commitment
Local mutation
Bat algorithm
Unit commitment problem
Crossover operator
Binary algorithm
ISSN:0920-8542, 1573-0484
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Shrnutí:Power system operators are faced with the problem of unit commitment belonging to mixed integer programming, which becomes very complicated, as units become large-scale and highly constrained. Because unit commitment problem is a binary problem with commitment and de-commitment, a discrete/binary optimization algorithm with superior performance is required. This paper proposes a novel hybrid binary bat algorithm for unit commitment problem, which consists of two process. To begin with, the proposed binary bat algorithm is applied to determining the commitment schedule of unit commitment problem. Specifically, an improved crossover operator based on exponential-logic-modulo map is proposed to enhance the convergence and maintain the diversity of populations. To prevent the algorithm from falling into a local optimum, a local mutation strategy performs local perturbation. Chaotic map is responsible for updating some parameters to increase the performance of the proposed algorithm. Furthermore, Lambda-iteration method is adopted to solve economic load dispatch in continuous space. Constraint handling is performed using the heuristic constraint produce. The effectiveness of the proposed algorithm is verified by benchmark functions and test systems. Additionally, the simulation results are compared with other well-established heuristic and binary approaches.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-023-05865-1