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Microelectronics & Computer

A Multi-Population Synergy Quantum Genetic Algorithm for Multimodal Optimization

Geng Huan-ton

Published Jan 1, 2013• 0 citations
OpenAlex

Abstract

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.

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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.
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.
Ordinary quantum genetic algorithm is easy to fall into local optimum in the process of complex function optimization, especially in the case of random sampling in which it is difficult to find a solution close to the true value. Therefore, a multi-group quantum genetic algorithm combining the idea of order optimization is proposed, and the number of simulations is reasonably allocated through the optimal calculation allocation technology, which further improves the convergence speed and reliability of the algorithm. Experiments show that a solution close to the true value by this proposed algorithm can be found efficiently.
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.
Aiming at the issue that quantum genetic algorithm is not suitable for continuons function optimization,an improved quantum genetic algorithm is proposed.This algorithm directly compares the quantum chromosomes with the current best solution to determine the rotating angle of the rotating gate.Every individual in population evolves with different rates to complete local search and global search simultaneously.Mutation operation is used to prevent the premature convergence.After analyzing the algorithm and its global convergence,this algorithm is applied to solve the function extremum and to optimize the PID controller parameters,and it is compared with the standard genetic algorithm and the quantum genetic algorithm.Experimental results illustrate that the proposed algorithm has better performance.
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.
This paper first proposes a novel Multi-Universe Parallel Quantum Genetic Algorithm (MPQGA) and proves its global convergence in theory.In the algorithm,all individuals are divided into some independent sub-colonies,called universes,according to their definite topological structure.Individuals in a universe are represented by multi-state gene qubits.In the individual's updating,the general quantum rotation gate strategy and dynamic adjusting rotation angle mechanism are applied to accelerate convergence.Quantum NOT gate is used to realize quantum mutation to avoid premature convergence.Each universe evolving independently enlarges the search space.Information among the universes is exchanged by adopting the best emigration and the quantum crossover operation for the improvement of search efficiency.Then it puts forward a new Blind Source Separation (BSS) method based on the combination of MPQGA and Independent Component Analysis (ICA).The simulation results show that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA) and the Quantum Genetic Algorithm (QGA).