Improved Multi-objective Quantum Genetic Algorithm Of Low Power State Assignments

Quantum evolutionary algorithm is a new evolutionary algorithm, which is based on the latest quantum theory, and introduce quantum coding and quantum rotation gate updating chromosome to search the global optimal solution. So quantum evolutionary algorithm has strong global search capability, rapid convergence and chromosomal characteristics of small-scale groups. The genetic algorithm is an intelligent algorithm to mimic biological evolution of the law of survival of the fittest. Integrate the traditional genetic algorithms and the new quantum theory into a new quantum genetic algorithm. This new algorithm has high performance on parallel search, and global search, so much attention on it, and is used to solve a variety of optimization problems. The multi-objective quantum genetic algorithm has good global search ability, while the Tabu search algorithm has good local search ability. Integrating the advantages of these two algorithms, a new hybrid algorithm, called improved multi-objective quantum genetic algorithm, can be obtained. In this paper, improved multi-objective quantum genetic algorithm is applied to a finite state machine of small area and low power design.The main work of this paper is to present an optimization algorithm for both the area and power dissipation in FSM circuits. The quantum genetic algorithm (QGA) is a well-known effective method to achieve fast convergence. With respect to the State Assignment problem, we propose improved multi-object QGA (IMOQGA) to obtain a FSM state assignment with less power and area consumption in an efficient way. By comparison with the published results, the proposed algorithm saves more power and area dissipation. This makes the circuit longer standby time, smaller area.