Quantum-inspired Evolutionary Algorithm: A Survey (original) (raw)
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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).
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.
Effect of Population Structures on Quantum-Inspired Evolutionary Algorithm
Applied Computational Intelligence and Soft Computing, 2014
Quantum-inspired evolutionary algorithm (QEA) has been designed by integrating some quantum mechanical principles in the framework of evolutionary algorithms. They have been successfully employed as a computational technique in solving difficult optimization problems. It is well known that QEAs provide better balance between exploration and exploitation as compared to the conventional evolutionary algorithms. The population in QEA is evolved by variation operators, which move the Q-bit towards an attractor. A modification for improving the performance of QEA was proposed by changing the selection of attractors, namely, versatile QEA. The improvement attained by versatile QEA over QEA indicates the impact of population structure on the performance of QEA and motivates further investigation into employing fine-grained model. The QEA with fine-grained population model (FQEA) is similar to QEA with the exception that every individual is located in a unique position on a two-dimensional ...
Quantum-inspired evolutionary algorithm: a multimodel EDA
… , IEEE Transactions on, 2009
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.
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.
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.
2014
In this paper, multi-criterion, evolutionary and quantum decision making supported by the Adaptive Quantum-based Multi-criterion Evolutionary Algorithm (AQMEA) has been considered. AQMEA has been developed to the task assignment problem and to underwater vehicle planning. Moreover, the other algorithms like QMEA and QEA have been discussed. Key-Words: quantum algorithms, decision making, multi-criterion optimization, evolutionary algorithms.
Quantum Evolutionary Algorithm based on Particle Swarm theory in multiobjective problems
2010
Abstract Quantum Evolutionary Algorithm (QEA) is an optimization algorithm based on the concept of quantum computing and Particle Swarm Optimization (PSO) algorithm is a population based intelligent search technique. Both these techniques have good performance to solve optimization problems. PSEQEA combines the PSO with QEA to improve the performance of QEA and it can solve single objective optimization problem efficiently and effectively.
An Adaptive Quantum Evolutionary Algorithm for Engineering Optimization Problems
International Journal of Computer Applications, 2010
Real world problems in engineering domain are typically constraint optimization problems. An Adaptive Quantum Evolutionary Algorithm for solving such problems is proposed in this paper. The proposed technique uses a novel qubits representation for search and optimization and uses feasibility rules for handling constraints. Moreover, it does not need stochastic ranking or niching or other methods for maintaining diversity. It does not even require mutation and local heuristics. The algorithm is tested on a standard set of four widely studied benchmark engineering design optimization problems. The results obtained are better than the existing state of the art approaches. The proposed algorithm is simple in concept and implementation, while being robust.