1. Introduction
Urban land serves as the foundation for the economic and social advancement of cities. However, the limited availability of land resources presents a challenge in achieving urban development objectives related to economic growth and service enhancement [
1,
2]. More importantly, within well-developed urban areas, altering existing land types entails significant costs, while issues such as aging infrastructure and increasing population demands have resulted in the inefficient utilization and diminished efficacy of urban land resources [
3,
4]. Therefore, the rational redevelopment and optimization of urban land are critical for the future high-quality development of cities [
5,
6]. Land resources can be utilized more efficiently through scientific land use optimization, thus promoting urban economic development [
7,
8].
Land use optimization entails the quantitative design and spatial allocation of different land types based on specific objectives and constraints [
9]. Existing optimization models can be categorized into three types: bottom-up, top-down [
10], and hybrid algorithms. Bottom-up models achieve global optimization through micro-scale land use changes [
11]. Specifically, the individual behaviors and decision-making processes of each small area are simulated to achieve global optimality. Models including cellular automata [
12], multi-agent systems [
13], and game theory [
14], are used to construct individual simulation processes. The advantage of such methods lies in their ability to consider the diversity and complexity among individuals, thus closely resembling real-world land use situations. For instance, Gao et al. employed cellular automata to predict the urban land expansion in Wenzhou city for the years 2025 and 2030 [
15]. Wu et al. combined linear optimization models with the CLUE-S (Conversion of Land Use and its Effects at Small Regional Extent) model to simulate land use changes in Jiangsu Province for the year 2025 [
16].
In contrast, top-down models prioritize global optimization objectives, allocating different land use types to various land grids through mathematical methods to obtain a series of optimal solutions [
10]. The linear programming model [
17], particle swarm algorithm [
18], ant colony algorithm [
19], and genetic algorithm [
20] are the commonly used top-down models. These models focus more on overall situations and global optimization objectives, making them better suited to adapt to various land planning policies and are therefore more widely used in land use optimization [
11]. For instance, Li et al. implemented land use optimization in Changzhou City based on the particle swarm algorithm [
21]. Wang et al. established a multi-objective type based on genetic algorithms targeting ecological and economic benefits, successfully simulating the optimized land use quantity structure in 2020 [
22]. Due to the large volume of data and high computational complexity involved in land use optimization, some researchers have proposed parallel algorithms to improve the computational efficiency of the optimization process [
23]. For instance, Porta et al. introduced a parallel genetic algorithm for land use zoning, which was successfully applied to land use planning in Galicia [
24].
Hybrid algorithms are models that integrate both top-down and bottom-up approaches. Through effective interaction, hybrid algorithms facilitate comprehensive consideration and coordination in the land use optimization process [
25]. For instance, Huang et al. coupled the Multi-Agent Systems with Fuzzy Logic Algorithm (MASFLA) model with the Hybrid Frog-Leaping Algorithm (HFLA), achieving an optimal spatial configuration of regional land use in terms of both structure and quantity [
13]. Similarly, Liu et al. employed a Genetic Optimization loosely coupled with the Game Theory Spatial Optimization Model, using competitive land grids as basic units. This model utilizes multi-stakeholder game theory and land use planning knowledge to harmonize local land use competitions [
26].
In past land use optimization studies, the genetic algorithm has been widely employed [
9], as it can effectively handle large-scale and multi-objective optimization problems while considering the interaction between different lands, making them suitable for complex optimization problems [
27]. Compared to conventional single-objective genetic algorithms, the non-dominated sorting genetic algorithm II (NSGA-II) can generate a set of balanced and diverse solutions, forming the so-called Pareto front [
28]. This means that it can not only find a single optimal solution but also provide multiple optimal solutions in the solution space, thus better meeting the diverse target requirements in complex problems and demonstrating superior performance in land use optimization [
29].
In optimization algorithms, optimization objectives can guide the generation of optimal urban land use allocation schemes. Among them, economic benefits reflect various requirements such as urban economic development and land use efficiency, making them pivotal in achieving rational urban land use allocation. Hence, many land use optimization studies adopt the objective of maximizing economic benefits, measuring economic benefits using GDP [
21] or land rent theory [
30]. However, these studies often overlook the implementation costs generated during the urban land use optimization process. In practical urban planning, it is necessary to consider the implementation costs arising from factors such as land prices, population density, building structures, and geographical conditions. Thus, the pursuit of economic benefits without regard to the implementation costs may hinder the application of optimization schemes in realistic urban planning. Balancing the relationship between implementation costs and economic benefits is crucial for achieving rational resource allocation and high-quality development in cities. In 2021, the Ministry of Housing and Urban-Rural Development of China issued an official notice, emphasizing the avoidance of the negative impacts of “demolition and reconstruction” in urban renewal [
31]. Therefore, urban planners are more focused on achieving optimization without changing the existing land use types. For instance, by renovating old buildings to increase the economic benefits of land utilization [
32].
Therefore, we propose two directions for optimization: land renovation and land reconstruction. Land renovation does not involve changes in land types but still incurs the consumption of costs and the improvement in economic income, while land reconstruction signifies the transformation in urban land type, with relatively higher costs and benefits. Then, this paper proposes a cost-heuristic genetic algorithm (CHGA). By considering the renovation and reconstruction costs of urban land cells and two optimization directions, the probability of land cell transformation is determined, thereby enabling the optimization model to more comprehensively simulate the actual situation of urban planning. The application of the model in the demonstration city Shenzhen in China validated its reliability and provides a more comprehensive and practical approach for urban land use optimization.
In general, the main contributions of this study are as follows:
We introduced land renovation to the optimization process to accommodate the demand for upgrading the service capacity through refurbishment in mature areas of development so that the optimization schemes are more aligned with the actual requirements of urban redevelopment.
We balanced economic benefits and implementation costs in urban land transformation, emphasizing the importance of considering actual costs over solely maximizing economic objectives, resulting in more feasible and economical optimization schemes.
We proposed a cost-heuristic genetic algorithm, which aims to integrate both implementation costs and economic benefits into the optimization process of urban land use types. It selects optimized land cells and determines optimization directions according to renovation and reconstruction costs, which enhances optimization rationality.
The remainder of this paper is organized as follows.
Section 2 presents the study area, data sources, research framework, and research methodology used in this study.
Section 3 showcases a case study of Shenzhen City, including specific settings of objectives and constraints, as well as the presentation and analysis of case study results. Finally,
Section 4 and
Section 5 discuss the significance, conclusions, and implications of this research.
4. Discussion
As urban land use in China gradually enters the stage of stock optimization, urban planning decision-makers increasingly prioritize the upgrading and renovation of existing land use types over re-planning to change current land use types [
31]. They strive to meet the demands of urban economic development with minimal implementation costs. However, the past land use optimization models often relied on random land cell selection and land type conversion, overlooking the implementation costs arising from differences in land conditions such as land prices and population density during the urban land use optimization process. To address the above-mentioned issues, this paper proposes a cost-heuristic genetic algorithm (CHGA)-based urban land use optimization model.
The CHGA algorithm calculates the selection probability of different urban land units based on reconstruction and renovation costs to optimize the selection of the best units. Additionally, guided by these costs, the algorithm determines the probabilities for renovation and reconstruction directions, allowing the urban land use optimization algorithm to fully consider the diversity and complexity of urban land use. This approach results in a more comprehensive and effective urban land use optimization plan. To validate the effectiveness of the algorithm, we conducted an ablation experiment with Shenzhen as the study area. The results show that compared to NSGA-II and NSGA-II_R, our algorithm reduces implementation costs by 35.86% and 9.11%, respectively. This confirms that our algorithm, guided by costs, determines the optimization directions for renovation and reconstruction, thereby effectively reducing the costs of the optimization process and achieving a more reasonable and sustainable optimization plan.
This study introduces renovation into the optimization process, offering a new perspective that allows the optimization to more comprehensively and realistically simulate urban renewal processes. By analyzing the optimal land use structure in Shenzhen, we found that the area allocated for renovation exceeded that for reconstruction. Additionally, for different urban land use types, we discovered that the area of industrial land reconstruction accounted for about 60% of the total optimized industrial land area, significantly more than the area for renovation. This result aligns with the requirements of Shenzhen’s urban renewal and land consolidation plan for industrial land. Therefore, incorporating renovation into the urban land optimization process is necessary for obtaining informative solutions that meet urban renewal needs.
To further validate the importance of considering costs and renovation in urban land use optimization, we examined scenarios focused on maximizing economic benefits, minimizing costs, and promoting sustainable development. The results indicate that optimizing land use in high-cost areas, while leading to increased costs, can also yield substantial economic benefits. This demonstrates that despite the potentially high costs of land use optimization in certain areas, considering their economic development potential is crucial.
5. Conclusions
As an important means to address the shortage of construction land, urban land use optimization plays a significant role in alleviating land scarcity and improving land use efficiency. This paper proposes a cost-heuristic genetic algorithm (CHGA) that considers implementation costs and urban renovation, providing a more comprehensive and practical method for simulating urban planning scenarios. Empirical research on Shenzhen’s urban land use optimization shows that the proposed method can reduce optimization costs by 35.86% and display the spatial distribution of both reconstruction and renovation optimization strategies. This confirms that the algorithm can effectively reduce optimization costs and make the optimized plans more aligned with the actual development needs of the city. The CHGA algorithm designed in this study approaches the optimization of urban land use from a more comprehensive perspective. It not only focuses on changes in land function but also emphasizes land reuse and implementation costs, aiming to achieve the sustainable use and development of urban land. The renovation strategy proposed in this study provides a new perspective for future land use optimization research. This perspective helps researchers explore the diversity and complexity of urban land use, offering insights for addressing future urban land use challenges and meeting various urban development needs.
However, in practical applications, further improvements can be made in this study. Firstly, with the continuous advancement of ecological civilization construction, urban planners may shift their focus from pursuing short-term economic benefits to considering long-term ecosystem development in the future. Ecological construction will become a key focus of future urban planning. Therefore, we will consider incorporating the ecological environment into the optimization objectives in future research to achieve sustainable urban development. Secondly, considering the heterogeneity of urban development in different cities, this study will explore urban land use optimization for different types of cities in the future. Through comparative analysis across cities, a better understanding of the commonalities and differences in urban planning and land use can be achieved, providing more personalized optimization solutions for the development of different cities.