In order to solve the questions of the traditional quantum evolution algorithm, which are easy to fall into precocity and the slow convergence rate in the complex function optimizing, clone operator is introduced into the quantum evolution algorithm. The quantum clone algorithm which is a kind of new evolution algorithm is proposed. This algorithm both has drawn lessons from the highly effective parallelism of quantum evolution algorithm and has used the multiplicity of clone operator increasing population. It has enhanced the overall situation optimization ability in solving function optimization question. The simulation result indicates that the algorithm is superior to the traditional quantum evolution algorithm and has solved the optimization problem of complex function.
In order to solve the questions of the traditional quantum evolution algorithm, which are easy to fall into precocity and the slow convergence rate in the complex function optimizing, clone operator is introduced into the quantum evolution algorithm. The quantum clone algorithm which is a kind of new evolution algorithm is proposed. This algorithm both has drawn lessons from the highly effective parallelism of quantum evolution algorithm and has used the multiplicity of clone operator increasing population. It has enhanced the overall situation optimization ability in solving function optimization question. The simulation result indicates that the algorithm is superior to the traditional quantum evolution algorithm and has solved the optimization problem of complex function.
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.
In order to improve the slow convergence speed and avoid falling into the local optimum in ant colony optimization algorithm, an improved quantum ant colony optimization (IMAQACO) algorithm based on combing quantum evolutionary algorithm with ant colony optimization algorithm is proposed for solving complex function problems in this paper.In the IMAQACO algorithm, the quantum state vectors are used to represent the pheromone, the adaptively dynamical updating strategy is used to control pheromone evaporation factor, the quantum rotation gate is used to realize the ant movement and change the convergence tend of quantum probability amplitude, quantum non-gate is used to realize ant location variation, so the IMAQACO algorithm has better global search ability and population diversity than ACO algorithm.In order to test the optimization performance of IMAQACO algorithm, several benchmark functions are selected in here.The tested results indicate that the IMAQACO can effectively improve the convergence speed and avoid falling into the local optimum, and has a stronger global optimization ability and higher convergence speed in solving complex function problems.
Aimed at the difficulty in solving the continuous functions of general quantum genetic algorithm,an improved quantum genetic algorithm is studied.In this algorithm,H,gate,a novel quantum rotation gate,is adopted to update the population,which can prevent the algorithm from falling into local optimum,and improve the global searching ability of the algorithm.The test results indicate that, when this algorithm optimizes continuous function,its overall performance is superior to that of conventional genetic algorithm and general quantum genetic algorithm.
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 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.