According to the new development pattern, implementing the development strategy driven by scientific and technological innovation (STI) is an important measure for each region to improve the quality of regional economic development. Exploring the effect of STI on the regional high-quality economic development (HED) is helpful to break the bottleneck and enhance the momentum of regional economic development. The Yangtze River Delta, located in the east coast of China, is the frontier of STI and economic development. Based on theoretical analysis, this paper uses the entropy method to calculate the STI index of the Yangtze River Delta from 2009 to 2018, and systematically investigates the impact of STI on HED in Yangtze River Delta by using two-way fixed effect, spatial Dubin model, panel threshold model and moderation effect model. The research finds that: First, under the control of other factors, STI has significantly promoted the HED of Yangtze River Delta region, while it also produces a spatial spillover effect. Second, the impact of STI on HED in the Yangtze River Delta has a double threshold effect based on the advanced industrial structure. Third, the analysis of adjustment mechanism shows that the scale of higher education has a positive adjustment effect in the relationship between STI and HED. Fourth, with the further analysis of regional heterogeneity, the economic effect of STI in central cities is significantly greater than that in noncentral cities. These findings provide theory-based direction and policy inspiration for improving the institutional mechanism of STI and accelerating the promotion of HED in Yangtze River Delta region.

The standard particle swarm optimization (PSO) algorithm converges very fast, while it is very easy to fall into the local extreme point. According to waiting effect among particles with mean-optimal position(MP), the quantum-behaved particle swarm optimization (QPSO) algorithm can prevent the particle prematurely from falling into local extreme point, but its convergence speed is slow and convergence precision is still low. In order to further improve the precision of QPSO algorithm, the evaluation method of $\delta$ trap characteristic length $L(t)$ of wave function for describing the particle's state is modified. In QPSO, a random weight to each particle in swarm is introduced, and according to the order of the each particle best position's fitting value, there are three evaluation programs for $L(t)$, which are random-weight mean-optimal position(RMP), reverse-order random-weight mean-optimal position(RRMP) and same-order random-weight mean-optimal position(SRMP). Through the test of several typical functions, its result shows that the convergence accuracy of QPSO with RMP and RRMP is better than those of QPSO with MP, so the evaluation of $L(t)$ with RMP and RRMP is feasible and effective.