This paper combines quantum evolutionary algorithm with the available evolutionary algorithms, uses quantum computing parallelism to search solution space efficiently, uses the traditional evolutionary algorithm to optimize the result observed by quantum population and then uses the optimized result to guide the generation of some individuals of quantum population. It maintains the population diversity of quantum evolutionary algorithm and avoids premature. Meanwhile it adopts the different evolutionary algorithms to combine with quantum evolutionary algorithm to solve different problems, which will improve the adaptability of algorithm.
This paper combines quantum evolutionary algorithm with the available evolutionary algorithms, uses quantum computing parallelism to search solution space efficiently, uses the traditional evolutionary algorithm to optimize the result observed by quantum population and then uses the optimized result to guide the generation of some individuals of quantum population. It maintains the population diversity of quantum evolutionary algorithm and avoids premature. Meanwhile it adopts the different evolutionary algorithms to combine with quantum evolutionary algorithm to solve different problems, which will improve the adaptability of algorithm.
This paper 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, evaluation function, and 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 to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments were 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 population, without premature convergence as compared to the conventional genetic algorithm.
This paper proposed a new evolutionary algorithm based on concept and principles of quantum computing, called Multi-update Mode Quantum Evolution Algorithm (MMQEA), in which having two update modes a-update and β-update, and between their modes each update procedure provide its evolutionary information to other one, that to guide the other update-mode to maintain the population diversity and avoid premature. Meanwhile in MMQEA, proposed a new individual structure that composed by two Q-bit strings. Applying the multi-update mode to individual evolution improved the Qgate updating efficiency in each generation of MMQEA. To demonstrate its effectiveness and applicability, the proposed algorithms were tested on a famous combinatorial optimization problem, the knapsack problem. The results show that MMQEA performs very well compared with Quantum Evolutionary Algorithm (QEA).
According to the characteristics of combinatorial optimization problems,a novel improved quantum evolutionary algorithm for combinatorial optimization problems(NIQEA) is proposed.The NIQEA initializes population based on the idea of niche cooperative evolutionary,increasing the diversity of individuals,and adopts the dynamic adjustment strategy to adjust the quantum rotation corner,speeding up convergence rate,and uses the cross strategy of excellent individuals to get crossover operation,enhancing local search ability.To validate its performance,the NIQEA is applied to solve the typical combinatorial optimization problems: two examples of multidimensional 0/1 knapsack problem,and the results show that the NIQEA is feasible and effective.
In this paper, we propose a novel quantum-inspired evolutionary algorithm, called NQEA, for solving combinatorial optimization problems. NQEA uses a new Q-bit update operator to increase the balance between the exploration and exploitation of the search space. In the operator, first, the Q-bits of each individual in the population are updated based on the personal best measurement of that individual and the best measurement of current generation. Then, a restriction is applied to each Q-bit to prevent the premature convergence of its values. The results of experiments on the 0-1 knapsack and NK-landscapes problems show that NQEA performs better than a classical genetic algorithm, CGA, and two quantum-inspired evolutionary algorithms, QEA and vQEA, in terms of convergence speed and accuracy.
Quantum-Inspired Evolutionary Algorithm (QEA), a type of stochastic algorithm for solving combinatorial optimization problems, is evolutionary computation using quantum bits and superposition states in quantum computation. Although coarse-grained parallel, QEA has many parameters that must be adjusted manually. The simpler algorithm, Quantum-inspired Evolutionary Computation with Pair Swap operator (QEAPS), the authors propose involves just one population and a simple genetic operation exchanging best solution information between two individuals chosen randomly, instead of the migration operation used in QEA, and thereby fewer parameters to be adjusted. The authors found in experiments that QEAPS finds highly qualified solutions, is more robust against constraint handling, and has a higher search performance of thanks to diversified best solution information.