On Parallel Immune Quantum Evolutionary Algorithm Based on Learning Mechanism and Its Convergence
A novel Multi-universe Parallel Immune Quantum Evolutionary Algorithm based on Learning Mechanism (MPMQEA) is proposed, in the algorithm, all individuals are divided into some independent sub-colonies, called universes. Their topological structure is defined, each universe evolving independently uses the immune quantum evolutionary algorithm, and information among the universes is exchanged by adopting emigration based on the learning mechanism and quantum interaction simulating entanglement of quantum. It not only can maintain quite nicely the population diversity, but also can help to accelerate the convergence speed and converge to the global optimal solution rapidly. The convergence of the MPMQEA is proved and its superiority is shown by some simulation experiments in this paper.
KeywordsLearn Mechanism Markov Chain Model Immune Algorithm Affinity Maturation Immune Operator
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