Exploring planning tribunal decisions on travel plans for new developments

Gruyter, Chris De; Rose, Geoff; Truong, Long T.; Wilson, Kailey; Đukanović, Zoran

doi:10.1016/j.cstp.2020.07.002

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tourism planning has a strategic guiding significance, and it clearly puts forward the direction, scale, speed, and goal of tourism development, as well as the strategy to achieve the goal [1][2][3]. Its function is mainly manifested in the following aspects: 1. set goals for tourism development.…”

Section: Introductionmentioning

confidence: 99%

Tourism Route Selection Model for Tourism Sustainable Development Based on Improved Genetic Algorithm

Qiu-hong

2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

The improvement of people’s quality of life also promotes the development of tourism. The traditional travel mode is no longer suitable for the needs of modern people. How to quickly determine the optimal tourist route according to the needs of different families is the current sustainable development direction of tourism. Scientific planning of tourist routes to minimize the cost and time of tourists is very important for the improvement of tourism experience. This study employs an improved genetic algorithm (IGA) to find the best tourist route based on this requirement. With the increase of tourist attractions, routes, and demands, the traditional genetic algorithm (GA) has problems such as premature convergence and poor local search ability when planning the tourist route. The existence of these problems will affect the effect of route planning. IGA proposes three improvements to traditional GA. One is to introduce the ant colony algorithm (ACA) to initialize the parameters in the GA. This algorithm is introduced to alleviate the over-reliance of GA on initializing the population and the poor adaptability of individual populations. Second, because the crossover probability parameter in GA has such a large influence on the final solution, this study proposes an adaptive strategy for adjusting the crossover probability to improve population fitness. Third, considering the weak local search ability of GA and the problem of premature convergence, this study introduces the 2-opt optimization algorithm to improve the quality of the solution. The results of the experimental analysis confirm the effectiveness of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%