Quantum evolutionary algorithm applied to transient identification of a nuclear power plant

Nicolau, Andressa dos Santos; Schirru, Roberto; Meneses, Anderson Alvarenga de Moura

doi:10.1016/j.pnucene.2010.08.004

Cited by 26 publications

(6 citation statements)

References 7 publications

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“…Second, to make the binary observed solutions suitable for the real-domain localization problem in WSNs, the algorithm applies the real-to-binary procedure to convert the binary solutions to the real solutions and the binary-to-real procedure to do the opposite. The algorithm also utilizes the Solis Wets' local search (SW-LS) [26] in the Baldwinian scheme for the best observed solution at each specific generation. We called it the Baldwinian evolution because similar to the Baldwin effect in genetic algorithms, it does not have a direct effect on the genotypes of the individuals.…”

Section: Solution Representationmentioning

confidence: 99%

“…In general, optimization techniques for the fine-grained localization problem in WSNs can be categorized into two different groups. The first group uses only stochastic technique such as SAL [16], PSOL [25] or GAL [26]. The second group, on the other hand, uses not only a stochastic optimizer but also an approximation stage such as multitrilateration [27] or priori knowledge such as node-categorizing information [28] to find initial locations of sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

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Baldwinian Learning in Quantum Evolutionary Algorithms for Solving the Fine-Grained Localization Problem in Wireless Sensor Networks

Aziz¹,

Meybodi²

2016

Int J Swarm Intel Evol Comput

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A Local Search (LS) procedure is a search facilitator, giving memetic algorithms a hand to enhance their exploitation ability resulting in converging to higher quality solutions. In this paper, using the LS procedure in the form of Baldwinian Learning (BL) a Memetic Quantum Evolutionary Algorithm (QEA) is proposed for tackling the fine grained localization problem in Wireless sensor networks (WSNs). Since the QEA can be used only for binarydomain problems like the knapsack problem, we utilize the binary-to-real mapping procedure to make it suitable for solving the localization problem in WSNs. To provide good initial positions of sensor nodes, the algorithm employs a Multi-Trilateration (MT) procedure on the best observed solutions. To test the proposed algorithm, it is first compared with its two spin-offs (the proposed algorithm without the MT procedure and the proposed algorithm without the BL and MT procedures) and then compared with six existing optimization algorithms on ten randomly generated network topologies with four different connectivity ranges. The simulation results suggest that the proposed algorithm significantly outperforms the other algorithms in terms of estimating the positions of sensor nodes in WSNs. They also point out the effectiveness of applying the MT procedure and BL method to the proposed algorithm in solving the problem.

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Section: Solution Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Baldwinian Learning in Quantum Evolutionary Algorithms for Solving the Fine-Grained Localization Problem in Wireless Sensor Networks

Aziz¹,

Meybodi²

2016

Int J Swarm Intel Evol Comput

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“…Chaotic quantum genetic algorithm (CQGA) is an improved version of the well-known quantum evolutionary algorithm (QEA) [15], which incorporates the global searching ability of GA, the local search ability of quantum probability technique, and the adaptability and transverse mobile ability of chaotic algorithm. Hence, CQGA is widely acknowledged as an effective tool to solve EED problem.…”

Section: Variable Step-size Chaotic Fuzzy Quantum Genetic Algorithmmentioning

confidence: 99%

An Environmental-Economic Dispatch Method for Smart Microgrids Using VSS_QGA

Liu

Zhang

Zeng

et al. 2014

Journal of Applied Mathematics

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The increasing penetration of distributed generation resources demands better economic performance of microgrids under the smart-grid era. In this paper, a comprehensive environmental-economic dispatch method for smart microgrids is proposed, with the objective for minimizing the summation of generation and emission costs in the system. As the proposed model belongs to a large-scale nonlinear and nonconvex programming problem, a hybrid heuristic algorithm, named variable step-size chaotic fuzzy quantum genetic algorithm (VSS QGA), is developed. The algorithm utilizes complementarity among multiple techniques including the variable step size optimization, the rotation mutational angle fuzzy control, and the quantum genetic algorithm and combines them so as to solve problems with superior accuracy and efficiency. The effectiveness of the proposed model is demonstrated through a case study on an actual microgrid system and the advantages in the performance of VSS QGA is also verified through the comparison with genetic algorithm (GA), the evolutionary programming approach (EP), the quantum genetic algorithm (QGA), and the chaotic quantum genetic algorithm (CQGA).

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“…O uso de alguns conceitos da computação quântica associados à estrutura dos algoritmos evolucionários deram origem aos algoritmos evolucionários com inspiração quântica (Han & Kim, 2002;Nicolau et al, 2011;da Silva et al, 2011). Eles utilizam uma representação probabilística conhecida como bit quântico, ou qubit que é sua unidade fundamental de informação, como mostrado na expressão a seguir:…”

Section: Otimização Quântica Por Colônia De Formigas -Qacounclassified

Computação evolucionária aplicada ao problema da recarga de reatores nucleares

Lima¹,

Nicolau²,

Oliveira³

et al. 2011

Computação Evolucionária Em Problemas De Engenharia

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Resumo: O objetivo deste capítulo é apresentar o Problema da Recarga de Reatores Nucleares (PRRN), mostrando a viabilidade da utilização de alguns métodos da Inteligência Computacional na solução deste problema. Os métodos aqui abordados correspondem às estratégias de otimização por colônia de formigas, colônia de abelhas, enxames de partículas, algoritmos genéticos, além de uma abordagem com inspiração quântica para o algoritmo de colônia de formigas. São introduzidos os principais aspectos do PRRN, bem como as principais características de cada método utilizado. Os resultados apresentados comprovam a viabilidade e eciência destes métodos como técnicas de solução de problemas da área nuclear.

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Quantum evolutionary algorithm applied to transient identification of a nuclear power plant

Cited by 26 publications

References 7 publications

Baldwinian Learning in Quantum Evolutionary Algorithms for Solving the Fine-Grained Localization Problem in Wireless Sensor Networks

Baldwinian Learning in Quantum Evolutionary Algorithms for Solving the Fine-Grained Localization Problem in Wireless Sensor Networks

An Environmental-Economic Dispatch Method for Smart Microgrids Using VSS_QGA

Computação evolucionária aplicada ao problema da recarga de reatores nucleares

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