Quantum-inspired genetic algorithm with two search supportive schemes and artificial entanglement

In this paper, we present an enhanced quantum-inspired genetic algorithm (eQiGA) with a combination of proposed mechanisms: two search supportive schemes and artificial entanglement. This combination is aimed at balancing exploration and exploitation. Two schemes, namely Explore and Exploit scheme are designed with aggressive specific roles reflecting its name. Entanglement is considered to be one of the significant strengths in quantum computing aside the probabilistic representation and superposition. Hence we attempt to apply its concept as part of our strategy for its potential. In addition, two new sub-strategies are proposed: fitness threshold, and quantum side-stepping. The algorithm is tested on multiple numerical optimization functions, and significant results of improved performance are obtained, studied, and discussed.