A hybrid quantum evolutionary algorithm for solving engineering optimization problems

Mani, Ashish; Patvardhan, C.

doi:10.3233/his-2010-0115

Cited by 21 publications

(15 citation statements)

References 17 publications

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“…The initial values of Yj and CVj can be determined randomly or provided by any other classical techniques like in [1]. c. Construct the time dependant Hamiltonian Ĥ(s) = (1-s)*Hb + s*Hp, where s = t/T.…”

Section: A Proposed Algorithmmentioning

confidence: 99%

“…x il < x i < x iu ; where x i is the i th variable with x il and x iu as its lower and upper limits. Traditional deterministic optimization techniques like calculus based methods and enumerative strategies are often incapable of effectively solving such problems [1]. Recently some investigations have been made to solve unconstrained GOP in [2] and [3] by employing Grover`s Algorithm as a basic routine.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the global optimization problems (GOP) in real world are constrained optimization problems (COP) in continuous variables, which are formulated as follows [1]:…”

Section: Introductionmentioning

confidence: 99%

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Some Investigations for Solving Constrained Optimization Problems using Adiabatic Quantum Computation

Mani

Patvardhan

2011

2011 Eighth International Conference on Information Technology: New Generations

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Adiabatic Quantum Computation has emerged as a new approach for designing quantum algorithms, which is equivalent to the circuit model but appears to be more robust and suffers from less number of bottlenecks than it. Some investigations have been undertaken in this paper for solving constrained optimization problems using Adiabatic Quantum Computation, as there is no reported approach yet for solving such problems using Quantum Algorithms.

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Section: A Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Some Investigations for Solving Constrained Optimization Problems using Adiabatic Quantum Computation

Mani

Patvardhan

2011

2011 Eighth International Conference on Information Technology: New Generations

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“…The possibility of utilizing quantum-mechanical systems for reasonable computing, introduced by Feynman [6], resulted in the design of several quantum computing models and algorithms, and subsequent research in the field of quantum computing is now highly developed [7][8][9][10]. Interest in artificial neural networks based on quantum computing has also increased, and many studies of quantum neural computing have been undertaken in the belief that they may provide a better understanding of certain brain functions and also help in solving classically intractable problems [11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive-type servo controller utilizing a quantum neural network with qubit neurons

Takahashi

2013

HIS

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This study presents an adaptive-type controller based on a quantum neural network and investigates its characteristics for control systems. A multi-layer quantum neural network which uses qubit neurons as an information processing unit is utilized to design three types of the adaptive-type quantum-neural-network-based controllers which conduct the learning of the quantum neural network as an online process: a direct controller, a parallel controller and an indirect controller. Computational experiments to control the single-input single-output non-linear discrete-time plant are conducted to evaluate the learning performance and capability of quantum neural controllers. The results of the computational experiments confirm the feasibility and effectiveness of adaptive-type quantum neural controllers.

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“…Yet another joint approach used the combination of Nelder-Mead simplex search with evolutionary algorithm [20]. More hybrid constraint handling studies can be found in [21], [22].…”

mentioning

confidence: 99%

Probabilistic constraint handling in the framework of joint evolutionary-classical optimization with engineering applications

Datta

Bittermann

Deb

et al. 2012

2012 IEEE Congress on Evolutionary Computation

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Abstract-Optimization for single main objective with multi constraints is considered using a probabilistic approach coupled to evolutionary search. In this approach the problem is converted into a bi-objective problem, treating the constraint ensemble as a second objective subjected to multi-objective optimization for the formation of a Pareto front, and this is followed by a local search for the optimization of the main objective function. In this process a novel probabilistic modeling is applied to the constraint ensemble, so that the stiff constraints are effectively taken care of, while the model parameter is adaptively determined during the evolutionary search. In this way the convergence to the solution is significantly accelerated and an accurate solution is established. The improvements are demonstrated by means of example problems including comparisons with the standard benchmark problems, the solutions of which are reported in the literature.

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A hybrid quantum evolutionary algorithm for solving engineering optimization problems

Cited by 21 publications

References 17 publications

Some Investigations for Solving Constrained Optimization Problems using Adiabatic Quantum Computation

Some Investigations for Solving Constrained Optimization Problems using Adiabatic Quantum Computation

Adaptive-type servo controller utilizing a quantum neural network with qubit neurons

Probabilistic constraint handling in the framework of joint evolutionary-classical optimization with engineering applications

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