This thesis proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator that drives the individuals toward better solutions. The termination condition of QEA is designed by defining a new measure on the convergence of Q-bit individuals. To analyze the characteristics of QEA, the theoretical analysis of the QEA algorithm as well as the effects of changing parameters of QEA are examined. In particular, some issues of QEA such as the analysis of changing the initial values of Q-bits, a novel variation operator H2 gate, and a two-phase QEA (TPQEA) scheme are addressed to improve the performance of QEA. To demonstrate the effectiveness and applicability of QEA, experiments are carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small number of population, without premature convergence as compared to the conventional genetic algorithms. Moreover, through the experiments on numerical optimization problems, the superior performance of QEA is also verified. These results show that QEA can be applied to a class of numerical as well as combinatorial optimization problems. iv

Quantum-inspired evolutionary algorithm = 양자 개념을 도입한 진화 알고리즘

Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC is used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system to increase the saving in the power system by applying the Unit Commitment (UC) to the power system. This work proposes Local Attracting Quantum Particle Swarm Algorithm (LAQPSO) to solve the unit commitment problem in power systems. The local attractor in the LAQPSO algorithm is used to obtain the rotation angle direction and magnitude for updating the quantum angle using the quantum rotation gate. The proposed algorithm is applied to solve the UC problem for a 26 units power system. A comparison with the Binary PSO (BPSO), Improved Quantum BPSO (IQBPSO) and other techniques in the literature was implemented to show the efficiency and the accuracy of the proposed algorithm. The results show the superior performance of the proposed LAQPSO algorithm to minimize the total cost when compared with BPSO, IQBPSO and the literature works.

Solving the unit commitment problem in large systems using hybrid PSO algorithms

https://iopscience.iop.org/article/10.1088/1757-899X/745/1/012008/pdf

Unit commitment problem solution using Local Attracting Quantum PSO algorithm

This paper proposes a new quantum particle swarm optimization algorithm with local attracting (LAQPSO), which is based on quantum-inspired evolutionary algorithm (QEA) and particle swarm optimization algorithm (PSO). In the proposed LAQPSO, a novel quantum bit expression mechanism called quantum angle is employed to encode the solution onto particle, and a new local attractor is proposed to determine the rotation angle of quantum rotation gate automatically. During the process of seeking the global solution, the magnitude of rotation angle is adjusted by an important parameter called contraction coefficient, which can quantitatively determine the tradeoff between exploration ability and exploitation ability. The simulation results for different contraction coefficients are helpful for selecting the key parameter. A set of benchmark functions are used to evaluate the performance of LAQPSO, QEA and QBPSO, and the results show that the proposed algorithm has a fast convergence rate and can effectively avoid premature convergence.

/pdf/a-new-quantum-particle-swarm-optimization-algorithm-with-8l3q8xejun.pdf

A new quantum particle swarm optimization algorithm with local attracting

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A new quantum particle swarm optimization algorithm with local attracting