Real-Coded Quantum Inspired Evolution Algorithm Applied to Engineering Optimization Problems

Esat, I.I.

doi:10.1109/isola.2006.12

Cited by 15 publications

(19 citation statements)

References 25 publications

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“…The results in Table 1 and 5 show that the QRACO is an improvement over an efficient BLX-α crossover operator. Table 6 presents comparison of the proposed Adaptive RQEA (ARQEA) with existing state of art algorithms like RQIEA [4], ECPSO [8] and DTS [9] on best solution on problems P-1, P-2 and P-4. The results of Table 1 and Table 6 show that the proposed algorithm named ARQEA is far better than the earlier proposed RQIEA, which is a quantum inspired EA and is also better than ECPSO and DTS.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…In QEA, the probabilistic nature of qubits has been widely used for maintaining diversity [4]. A single qubit is attached to the solution vector and the solution is obtained by taking measurement or collapsing in binary coded as well as real coded QEA.…”

Section: Algorithmmentioning

confidence: 99%

“…The important principles of Quantum mechanics are superposition, entanglement, interference and measurement [3]. The principles mostly utilized in designing QEAs are superposition and measurement [4] and have been used for improving diversity as reported in the literature. Another interesting observation is the use of single qubit (quantum analog of classical bit, and is governed by the principles of quantum mechanics) representation in almost all the efforts, thus ruling out use of any other principles of quantum mechanics such as entanglement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Quantum Evolutionary Algorithm for Engineering Optimization Problems

Mani¹,

Patvardhan²

2010

IJCA

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Real world problems in engineering domain are typically constraint optimization problems. An Adaptive Quantum Evolutionary Algorithm for solving such problems is proposed in this paper. The proposed technique uses a novel qubits representation for search and optimization and uses feasibility rules for handling constraints. Moreover, it does not need stochastic ranking or niching or other methods for maintaining diversity. It does not even require mutation and local heuristics. The algorithm is tested on a standard set of four widely studied benchmark engineering design optimization problems. The results obtained are better than the existing state of the art approaches. The proposed algorithm is simple in concept and implementation, while being robust.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Adaptive Quantum Evolutionary Algorithm for Engineering Optimization Problems

Mani¹,

Patvardhan²

2010

IJCA

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“…An interesting point of such implementation is no direct correspondence between the solution vector and qubits especially in the case of real coded QiEA [17], [10]. The quantum rotation gates / operators also behave independent by the information from the problem and solution domain assuming that the quantum behavior would help in reaching the solution.…”

Section: Quantum Inspired Evolutionary Algorithmsmentioning

confidence: 99%

“…A single qubit is attached to the solution vector and the solution is obtained by taking measurement or collapsing in binary coded as well as real coded QiEA [17]. However, due to reasons explained in the previous section, this paper uses a real coded representation of the solution with two sets of qubits.…”

Section: Representationmentioning

confidence: 99%

Analysis of Photoluminescence measurement data from interdiffused Quantum Wells by Real coded Quantum inspired Evolutionary Algorithm

Mani¹,

Patvardhan²

2011

Proceedings of 13th International Workshop on Advanced Computing and Analysis Techniques in Physics Research — PoS(ACAT2010)

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Abstract:Reliable analysis of any experimental data is always difficult due to the presence of noise and other types of errors. This paper analyzes data obtained from photoluminescence measurement, after annealing of interdiffused Quantum Well hetrostructures, using an Adaptive Real coded Quantum inspired Evolutionary Algorithm (ARQiEA). The proposed algorithm directly computes interdiffusion parameters without using Arrhenius plot. Further, the results obtained have been compared with those reported with Genetic Algorithm and Least Square Analysis methods. The ARQiEA is suitable for parameter estimation problems as it is free from parameter fine-tuning and provides result with least overall error.

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Blind separation of noncooperative paired carrier multiple access signals based on improved quantum‐inspired evolutionary algorithm and receding horizon optimization

Deng

Zhang

Chen

et al. 2021

Concurrency and Computation

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The single‐channel blind source separation of paired carrier multiple access (PCMA) signal is a key technology in satellite communications. Due to the high‐order complexity of existing separation methods and the uncertainty of channel parameters, the processing of noncooperative PCMA signals with long memory remains a great challenge. In this article, the blind separation was solved as the joint channel estimation and sequence detection, and a novel blind separation algorithm based on the improved quantum‐inspired evolutionary algorithm (IQEA) and receding horizon optimization was proposed. Considering the practical scenarios, a mixed PCMA signal model with noninteger period sampling was presented in advance. Combined with the proposed signal model, the IQEA was applied to recover the uplink signals from the mixed PCMA signals by its iterative estimation. Meanwhile, to reduce the computational complexity caused by long memory of PCMA signals, the receding horizon optimization was introduced to process the whole signal sequence as a dynamic programming problem. The simulation results verified that the proposed RH‐IQEA outperforms existing state‐of‐the‐art separation algorithms in the blind separation of noncooperative PCMA signals, with higher accuracy, lower‐order complexity and stronger robustness to parameter estimation errors.

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Real-Coded Quantum Inspired Evolution Algorithm Applied to Engineering Optimization Problems

Cited by 15 publications

References 25 publications

An Adaptive Quantum Evolutionary Algorithm for Engineering Optimization Problems

An Adaptive Quantum Evolutionary Algorithm for Engineering Optimization Problems

Analysis of Photoluminescence measurement data from interdiffused Quantum Wells by Real coded Quantum inspired Evolutionary Algorithm

Blind separation of noncooperative paired carrier multiple access signals based on improved quantum‐inspired evolutionary algorithm and receding horizon optimization

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