11,729 research outputs found
Speedup in Quantum Adiabatic Evolution Algorithm
Quantum adiabatic evolution algorithm suggested by Farhi et al. was effective
in solving instances of NP-complete problems. The algorithm is governed by the
adiabatic theorem. Therefore, in order to reduce the running time, it is
essential to examine the minimum energy gap between the ground level and the
next one through the evolution. In this letter, we show a way of speedup in
quantum adiabatic evolution algorithm, using the extended Hamiltonian. We
present the exact relation between the energy gap and the elements of the
extended Hamiltonian, which provides the new point of view to reduce the
running time.Comment: 5 pages, Late
An inflationary differential evolution algorithm for space trajectory optimization
In this paper we define a discrete dynamical system that governs the
evolution of a population of agents. From the dynamical system, a variant of
Differential Evolution is derived. It is then demonstrated that, under some
assumptions on the differential mutation strategy and on the local structure of
the objective function, the proposed dynamical system has fixed points towards
which it converges with probability one for an infinite number of generations.
This property is used to derive an algorithm that performs better than standard
Differential Evolution on some space trajectory optimization problems. The
novel algorithm is then extended with a guided restart procedure that further
increases the performance, reducing the probability of stagnation in deceptive
local minima.Comment: IEEE Transactions on Evolutionary Computation 2011. ISSN 1089-778
Dynamics of quantum adiabatic evolution algorithm for Number Partitioning
We have developed a general technique to study the dynamics of the quantum
adiabatic evolution algorithm applied to random combinatorial optimization
problems in the asymptotic limit of large problem size . We use as an
example the NP-complete Number Partitioning problem and map the algorithm
dynamics to that of an auxilary quantum spin glass system with the slowly
varying Hamiltonian. We use a Green function method to obtain the adiabatic
eigenstates and the minimum excitation gap, ,
corresponding to the exponential complexity of the algorithm for Number
Partitioning. The key element of the analysis is the conditional energy
distribution computed for the set of all spin configurations generated from a
given (ancestor) configuration by simulteneous fipping of a fixed number of
spins. For the problem in question this distribution is shown to depend on the
ancestor spin configuration only via a certain parameter related to the energy
of the configuration. As the result, the algorithm dynamics can be described in
terms of one-dimenssional quantum diffusion in the energy space. This effect
provides a general limitation on the power of a quantum adiabatic computation
in random optimization problems. Analytical results are in agreement with the
numerical simulation of the algorithm.Comment: 32 pages, 5 figures, 3 Appendices; List of additions compare to v.3:
(i) numerical solution of the stationary Schroedinger equation for the
adiabatic eigenstates and eigenvalues; (ii) connection between the scaling
law of the minimum gap with the problem size and the shape of the
coarse-grained distribution of the adiabatic eigenvalues at the
avoided-crossing poin
An Improved Differential Evolution Algorithm for Maritime Collision Avoidance Route Planning
High accuracy navigation and surveillance systems are pivotal to ensure efficient ship route planning and marine safety. Based on existing ship navigation and maritime collision prevention rules, an improved approach for collision avoidance route planning using a differential evolution algorithm was developed. Simulation results show that the algorithm is capable of significantly enhancing the optimized route over current methods. It has the potential to be used as a tool to generate optimal vessel routing in the presence of conflicts
Fuzzy Differential Evolution Algorithm
The Differential Evolution (DE) algorithm is a powerful search technique for solving global optimization problems over continuous space. The search initialization for this algorithm does not adequately capture vague preliminary knowledge from the problem domain. This thesis proposes a novel Fuzzy Differential Evolution (FDE) algorithm, as an alternative approach, where the vague information of the search space can be represented and used to deliver a more efficient search. The proposed FDE algorithm utilizes fuzzy set theory concepts to modify the traditional DE algorithm search initialization and mutation components. FDE, alongside other key DE features, is implemented in a convenient decision support system software package. Four benchmark functions are used to demonstrate performance of the new FDE and its practical utility. Additionally, the application of the algorithm is illustrated through a water management case study problem. The new algorithm shows faster convergence for most of the benchmark functions
How Powerful is Adiabatic Quantum Computation?
We analyze the computational power and limitations of the recently proposed
'quantum adiabatic evolution algorithm'.Comment: 12 pages, LaTeX2e, requires fullpage, times, amssymb, and amsmath
packages. This article appeared in the proceedings of FOCS'01; original
submission date: April 27, 200
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