Quantum-inspired Particle Swarm Optimization Multi-objective Optimization Algorithm

The deficiencies of ordinary multi-objective optimization algorithms, together with their implement problems in reality, always prevent the objective optimization methods from development. In the mid-1980s, Evolutionary Algorithm was introduced to solve the Multi-objective Optimization Problem. A variety of Multi-objective Evolutionary Algorithms are proposed at present. Some of them have been successfully applied to practical applications. More and more researchers begin to engage in this field. Quantum Particle SwarmOptimization algorithm (QPSO) is a new optimum method that combines quantum computation with Particle Swarm Optimization (PSO). It appears strong life-force and be valuable for research. Quantum computation absorbed many essential characters of quantum mechanics, which improved the computation efficiency, and become a brand new model of computation.QPSO has greatly enhanced the efficiency of search and can prevent premature of PSO and it has a wide research foreground. The following are the major contributions:1. On the basis of analyzing the advantages and disadvantages of Multi-objective Optimization algorithms, this article presents a Quantum-bit Particle Swarm Optimization (QBPSO) algorithm for Multi-objective Optimization Problems so as to improve the convergence and it is well-distributed. QBPSO adopts the non-dominated storing method for solutions population and use a new population diversity preserving strategy which is based on the Pareto max-min distance.2. The multidimensional 0-1knapsack problems are tested and the results show that the proposed method can efficiently find Pareto optimal solutions that are closer to Pareto font and better on distribution. Compared with NSGA2 and SPEA2, the value of S increases 11.10% and 11.06% on avenge. Especially, this proposed method is outstanding on more complex high-dimensional optimization problems.3. Apply our method on Advanced Logistics Problem (ALP) of army's assignment and scheduling. Generally speaking, there are many parameters in ALP and sum of them is always1. According to this character, we proposed a new sum constraint oriented method, in which trigonometric formulas are used to transform constraint condition, so as to save storage space and improve the search efficiency. The experimental results indict its feasibility and effectiveness.