On Improved Parallel Immune Quantum Evolutionary Algorithm Based on Learning Mechanism

Abstract

A new 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. Information among the universes is exchanged by adopting emigration based on the improved learning mechanism and quantum interaction simulating entanglement of quantum. It not only can maintain quite nicely the population diversity, but also can help to converge to the global optimal solution rapidly. The typical function tests show that MPMQEA has nice performances such as avoiding local optima, high precision solution, and quick convergence