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IEEE International Conference on Communication Software and Networks

An improved quantum clone genetic algorithm

Lihua Zhang, Liping Zhang, Haiyan Peng

Published May 1, 2011• 0 citations
Publisher

Abstract

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.

DOI
10.1109/iccsn.2011.6014655

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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.
Multi-user detection based on quantum optimization algorithm has aroused intense interests recently. 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 nicking probability division initialization strategy, the population diversity is enhanced; (2) Introducing 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, the 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. Simulation results prove that the multiuser detection based on given algorithm has lower bit error, rapider converge speed, better resisting MAI ability and better Near-far Resistance ability than other multi-user detections based on quantum clone genetic algorithm and classical genetic algorithms obviously.
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.
New methods are joined into the quantum genetic algorithm to solve the defects of poor local search ability and more iterative times.The algorithm is the core of a new quantum rotation gate adjustment strategy,which updates the population,so it has better diversity than the classical genetic algorithm,rapid convergence and good global search capacity characterize the performance of the quantum genetic algorithm.According to the test of typical complex function,the results show that the algorithm to the quality of opti-mization and efficiency are superior to traditional genetic algorithm and general quantum genetic algorithm.
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 algorithms(QGA) typically construct traditional quantum chromosomes as independent quantum bit systems, which has facilitated the enhancement of search speed and precision in genetic algorithms, demonstrating powerful problem-solving capabilities in optimization issues. Although traditional QGA have enriched population diversity, they still tend to fall into local optima, and the evolutionary update operations require rotation directions to be determined by lookup tables, a process involving multiple conditional judgments that affect algorithm efficiency. To address these issues, an improved method has been proposed to further enhance the performance of QGA. In the initial population selection, a population grouping evolution method based on microhabitat coevolution strategy is used to prevent the algorithm from getting trapped in local optima when solving complex function problems. Additionally, quantum entanglement is introduced on top of quantum super-position by implementing quantum entanglement using quantum registers, and higher-order quantum rotation gates are employed that do not involve multiple conditional judgments of lookup tables, thus improving the efficiency of quantum chromosome measurement and evolutionary updates. Finally, traditional QGA and higher-order quantum genetic algorithms were applied to test functions in multiple experiments. The results indicate that, compared to traditional QGA, the higher-order quantum genetic algorithms have achieved an increase in convergence speed, an approximate 14.3% improvement in convergence precision, and possess enhanced global search capabilities.
In order to save computation time and avoid falling into local optima in quantum genetic algorithm (QGA), this paper proposes a quantum genetic algorithm based on Boltzmann selection mechanism (B_QGA). The proposed algorithm has several novel characteristics. Firstly, Quantum bits are encoded by angle and introduce the constant factor, aiming at enhancing calculated amount. Secondly, a new dynamic rotation angle adjustment strategy is presented to achieve updated chromosome, and individuals directly evolve to the current optimal solution with linear increasing rate, which greatly improve the rate of convergence and the ability of global search. Thirdly, instead of roulette selection mode, Boltzmann selection mechanism is employed for avoiding the local optimal problem. Finally, for the selected offspring's individuals, Hadamard door is proposed to perform mutation operation to sustain stability and diversity of population. The experiment results on three complicated continuous functions show the proposed algorithm is able to find the best solution in very short time and has excellent global search ability comparing to quantum genetic algorithm (QGA) and genetic algorithm (GA).