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International Conference on Natural Computation (ICNC)

An Improved Many Worlds Quantum Genetic Algorithm

Dan Li, Junsuo Zhao, Heng Zhang, Peng Qiao, Jiayu Zhuang

Published Aug 1, 2015• 1 citation
Publisher

Abstract

An Improved Many Worlds Quantum Genetic Algorithm (IMWQGA) was proposed aiming at the shortcomings of the Quantum Genetic Algorithm, such as the multimodal function optimization problems easily falling into the local optimum and vulnerability to premature convergence. Using the concept of Many Worlds and the derivative way of parallel worlds' parallel evolution, we propose to update the population according to the main body and adopt the transition methods, such as parallel transition, backtracking, travel forth and so on. In addition, the quantum training operator and the combinatorial optimization operator as new operators of quantum genetic algorithm were also proposed.

DOI
10.1109/icnc.2015.7377992

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An Improved Many Worlds Quantum Genetic Algorithm (IMWQGA) was proposed aiming at the shortcomings of the Quantum Genetic Algorithm, such as the multimodal function optimization problems easily falling into the local optimum and vulnerability to premature convergence. Using the concept of Many Worlds and the derivative way of parallel worlds' parallel evolution, we propose to update the population according to the main body and adopt the transition methods, such as parallel transition, backtracking, travel forth and so on. In addition, the quantum training operator and the combinatorial optimization operator as new operators of quantum genetic algorithm were also proposed.
Abstract With the in-depth study of quantum genetic algorithm (QGA), the defect of premature convergence limits its development more and more. In order to further improve QGA, we propose an improved QGA based on multi population (IQGA). In the process of population initialization in IQGA, we generate multiple populations to avoid a single population falling into a local optimal value. We also abandon the fixed angle used by the traditional quantum revolving gate, and design a strategy to adjust the angle adaptively according to the difference from the optimal solution. In addition, we introduce a population catastrophe strategy to deal with the premature convergence of each population. The populations are connected based on migration operation, and the optimal solution of each population obtained by each iteration is collected in the elite group. A series of simulation experiments show that our improvement strategies of IQGA are effective.
An improved quantum genetic algorithm(IQGA) was proposed to overcome the shortcoming of the quantum genetic algorithm(QGA),i.e. local optimization, when it is used for the optimization of continuous functions with many extreme values. In IQGA, the strategies of updating quantum gate using the best solution obtained and population catastrophe were adopted. The test results for two typical functions show that the convergence speed of IQGA is faster than that of QGA, and IQGA can converge in a global solution space, overcoming the shortcoming of QGA. The application results indicate that IQGA is better than QGA and other genetic algorithms.
Quantum genetic algorithm (QGA) is the combination between genetic algorithm and quantum computing. In this paper, a chromosome of the standard QGA is seen as a node and the chromosome population is regarded as a network. Then the reasons for the prematurity and the stagnation of the standard QGA are analyzed from the perspective of network structure. To solve the two problems, an improved quantum genetic algorithm (IQGA) based on the small world theory is proposed. In IQGA, chromosomes encoded with qubits are divided into some sub-groups and the NW network model is introduced into the population structure. When updating chromosomes, an optimal chromosome in locality or in other sub-groups is chosen based on a certain probability as the evolution target for each chromosome. The new network structure of the chromosome population has a relatively moderate clustering coefficient and is favorable to the diversity of individual chromosomes. Tests of three classic functions prove the effectiveness and superiority of IQGA.
Intelligent optimization algorithm based on quantum behavior is the hot spot of intelligent computation. Quantum clone genetic algorithm has many shortcomings such as: low efficiency, poor population diversity, slow convergence speed, easy to trap in local minimums, blindness in global optimal searching direction and so on. An improved quantum clone genetic algorithm (IQCGA) is proposed in this paper. The algorithm have many merits: (1) Adopting the nicking probability division initialization strategy, population diversity is enhanced; (2) Introducing the quantum whole interference crossover, the information is spread in the whole population, so it helps to avoid trapping in local minimums, and accelerates convergence speed; (3) Making use of adaptation strategy in quantum rotate gate updating, searching speed for optimal solution is accelerated; (4) In order to avoid premature and evolution stagnation, the superior individual whole crossover quantum catastrophe strategy is adopted, which helps the population to search the objective solution in different directions. IQCGA is composted of initialization stage and iteration evolution stage. Furthermore, theory analysis is given to prove the convergence. Finally, Simulation results prove that the algorithms can maintain the population diversity, reduce calculation complexity, accelerate searching speed for optimal solution, and avoid trapping in local minimums. So it has the better searching optimal ability and convergence speed.
Aiming at the problems that the Quantum Genetic Algorithm(QGA) easily falls into local extremum,an improved QGA algorithm of quantum revolving gate is proposed.In order to achieve the goal of optimization solving,quantum bit probability amplitude is applied to the chromosome encoding,and quantum rotation gate is used to chromosome updating.The theoretical derivation proves that the proposed algorithm converges with probability 1,and the algorithm converges strongly to 1?e.Comparing with double QGA,simulation results show that the algorithm increases complexity,the average time extension,but convergence times increase,convergence times of the function 1 increases from 3 to 7,and convergence times of the function 2 increases from 8 to 9.
The quantum genetic algorithm is a kind of probability evolution algorithm but when it solves multimodal optimization problems,the update strategy of rotation gate will easy lead to local optimal solution and also can't find all best solutions in some optimization problem.Therefore in this paper a multi-population synergy quantum genetic algorithm(MPSGQA) proposed to solve these problems and find all the best solutions.The main idea is dividing m sub-populations by using clustering analysis and the update them.If some of the sub-populations have large similarity,the quantum mutation strategy is adopted to maintain the diversity of population.Experiment and comparison with multimodal optimization function F_1 to F_4,the result shows that MPSGQA can jump out of local optimal solution better than quantum genetic algorithm and find all global optimum solutions efficiently.