Effect of Population Structures on Quantum-Inspired Evolutionary Algorithm (original) (raw)
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Quantum-inspired Evolutionary Algorithm: A Survey
DEStech Transactions on Engineering and Technology Research
To provide a reference for the study of Quantum-Inspired Evolutionary Algorithm (QIEA), the combination of qualitative and quantitative methods is applied to analyze the literatures and references about QIEA. The status, the hot spots and the development trends of QIEA are explored based on the literatures. The results show that the research on QIEA has been increasing every year since it is proposed. The theoretical research, algorithm research and application research have been expanded and deepened. There have been many achievements about the application and research about QIEA, and QIEA will be one of the protagonists of optimization algorithms in future.
A versatile quantum-inspired evolutionary algorithm
2007 IEEE Congress on Evolutionary Computation, 2007
This study points out some weaknesses of existing Quantum-Inspired Evolutionary Algorithms (QEA) and explains in particular how hitchhiking phenomenons can slow down the discovery of optimal solutions and encourage premature convergence. A new algorithm, called Versatile Quantuminspired Evolutionary Algorithm (vQEA), is proposed. With vQEA, the attractors moving the population through the search space are replaced at every generation without considering their fitness. The new algorithm is much more reactive. It always adapts the search toward the last promising solution found thus leading to a smoother and more efficient exploration. In this paper, vQEA is tested and compared to a Classical Genetic Algorithm CGA and to a QEA on several benchmark problems. Experiments have shown that vQEA performs better than both CGA and QEA in terms of speed and accuracy. It is a highly scalable algorithm as well. Finally, the properties of the vQEA are discussed and compared to Estimation of Distribution Algorithms (EDA).
Quantum-inspired evolutionary algorithm: a multimodel EDA
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The quantum-inspired evolutionary algorithm (QEA) applies several quantum computing principles to solve optimization problems. In QEA, a population of probabilistic models of promising solutions is used to guide further exploration of the search space. This paper clearly establishes that QEA is an original algorithm that belongs to the class of estimation of distribution algorithms (EDAs), while the common points and specifics of QEA compared to other EDAs are highlighted. The behavior of a versatile QEA relatively to three classical EDAs is extensively studied and comparatively good results are reported in terms of loss of diversity, scalability, solution quality, and robustness to fitness noise. To better understand QEA, two main advantages of the multimodel approach are analyzed in details. First, it is shown that QEA can dynamically adapt the learning speed leading to a smooth and robust convergence behavior. Second, we demonstrate that QEA manipulates more complex distributions of solutions than with a single model approach leading to more efficient optimization of problems with interacting variables.
Survey of Quantum-Inspired Evolutionary Algorithms
Abstract. This paper presents a concise survey of a new class of metaheuristics, drawing their inspiration from both: biological evolution and unitary evolution of quantum systems. In the first part of the paper, general concepts behind quantum-inspired evolutionary algorithms have been presented. In the second part, a state of the art of this field has been discussed and a literature review has been conducted.
IEEE Transactions on Evolutionary Computation, 2004
From recent research on combinatorial optimization of the knapsack problem, quantum-inspired evolutionary algorithm (QEA) was proved to be better than conventional genetic algorithms. To improve the performance of the QEA, this paper proposes research issues on QEA such as a termination criterion, a Q-gate, and a two-phase scheme, for a class of numerical and combinatorial optimization problems. A new termination criterion is proposed which gives a clearer meaning on the convergence of Q-bit individuals. A novel variation operator gate, which is a modified version of the rotation gate, is proposed along with a two-phase QEA scheme based on the analysis of the effect of changing the initial conditions of Q-bits of the Q-bit individual in the first phase. To demonstrate the effectiveness and applicability of the updated QEA, several experiments are carried out on a class of numerical and combinatorial optimization problems. The results show that the updated QEA makes QEA more powerful than the previous QEA in terms of convergence speed, fitness, and robustness.
IEEE Transactions on Evolutionary Computation, 2004
From recent research on combinatorial optimization of the knapsack problem, quantum-inspired evolutionary algorithm (QEA) was proved to be better than conventional genetic algorithms. To improve the performance of the QEA, this paper proposes research issues on QEA such as a termination criterion, a Q-gate, and a two-phase scheme, for a class of numerical and combinatorial optimization problems. A new termination criterion is proposed which gives a clearer meaning on the convergence of Q-bit individuals. A novel variation operator gate, which is a modified version of the rotation gate, is proposed along with a two-phase QEA scheme based on the analysis of the effect of changing the initial conditions of Q-bits of the Q-bit individual in the first phase. To demonstrate the effectiveness and applicability of the updated QEA, several experiments are carried out on a class of numerical and combinatorial optimization problems. The results show that the updated QEA makes QEA more powerful than the previous QEA in terms of convergence speed, fitness, and robustness.
A Quantum-Inspired Evolutionary Algorithm for Optimization Numerical Problems
Lecture Notes in Computer Science, 2012
This paper proposes a new quantum-inspired evolutionary algorithm for solving ordering problems. Quantum-inspired evolutionary algorithms based on binary and real representations have been previously developed to solve combinatorial and numerical optimization problems, providing better results than classical genetic algorithms with less computational effort. However, for ordering problems, order-based genetic algorithms are more suitable than those with binary and real representations. This is because specialized crossover and mutation processes are employed to always generate feasible solutions. Therefore, this work proposes a new quantum-inspired evolutionary algorithm especially devised for ordering problems (QIEA-O). Two versions of the algorithm have been proposed. The so-called pure version generates solutions by using the proposed procedure alone. The hybrid approach, on the other hand, combines the pure version with a traditional order-based genetic algorithm. The proposed quantum-inspired order-based evolutionary algorithms have been evaluated for two well-known benchmark applications-the traveling salesman problem (TSP) and the vehicle routing problem (VRP)as well as in a real problem of line scheduling. Numerical results were obtained for ten cases (7 VRP and 3 TSP) with sizes ranging from 33 to 101 stops and 1 to 10 vehicles, where the proposed quantum-inspired order-based genetic algorithm has outperformed a traditional order-based genetic algorithm in most experiments.
A Hybrid Quantum Evolutionary Algorithm
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This paper proposes a Hybrid Quantum Evolutionary Algorithm. Quantum computing is capable of processing huge numbers of quantum states simultaneously, in parallel, (“quantum parallelism”), whereas evolutionary computing can only process one chromosome per processor. In theory, QC ought to be able to process upto all possible points in a 2 search space (of N bit chromosomes). An evolutionary algorithm is effectively a guided search algorithm that samples the search space and in general is a slow process. Since QC can examine all or some of 2 points simultaneously, this Quantum Parallelism can be cleverly used to speed the Evolutionary Algorithms. This paper presents a Hybrid Quantum Evolutionary Algorithm that uses the Quantum Parallelism and an other well-known Quantum Algorithm to speed up the Evolutionary Algorithms.
Quantum Inspired Evolutionary Algorithms with Parametric Analysis
Quantum inspired evolutionary algorithms are heuristic search methods, where all individuals in the search space directed to the best solution position. Using quantum gate operator with other evolutionary operators such as selection, crossover and mutation constitute a challenge in terms of their types and their parameters. In this paper we design several quantum crossover and quantum mutation operators with different parameters, the contribution of each operator to the success of our proposed algorithm analyzed via relative percentage deviation method. The proposed work gives a decision whether to use selection operator or not, it uses catastrophe operator to overcome local minima. The experimental results demonstrate the superiority of the proposed approach to solve non-linear programming problems.