1. Introduction
Due to an immense population, rapid urbanization and industrialization, land resources in China have been facing unprecedented pressure in the past 20 years, highlighted by the rapid loss of quality agricultural land and excessive sprawl of urban boundaries [
1]. Under these circumstances, the “resource conservation and environmentally friendly” development concept, whose core aim is to “reasonably use land resources, improve land resource use efficiency, and realize the harmonious development of humans and nature,” has become the consensus in land management in China. In China’s land use and land management system, not only is land use planning a technical means to promote sustainable land use, but it is also a management means with binding legal impact. As the core of land use planning, land use allocation (LUA) optimization is considered one of the most effective means for realizing sustainable land use. LUA optimization aims to balance the land use supply and demand based on the future dynamic demands from different land use stakeholders in the region and optimize the allocated quantity of limited land resources for different uses [
2].
LUA problems are often defined as multi-objective optimization problems with constraint conditions. In order to solve LUA problems, researchers have successively designed a number of LUA models from different perspectives. Based on their optimization methods, these models can be classified into the following types: mathematical programming (MP)-based models, system dynamics (SD)-based models, and heuristic algorithm (HA)-based models.
MP-based models were the earliest models used for solving LUA problems [
3]. So far, a number of MP methods have been used for solving LUA problems, including linear programing [
2,
4,
5,
6,
7], goal programing [
8,
9,
10], integer programming [
11], and interval programming [
12,
13,
14,
15], among others [
16]. Compared to other types of models, MP-based models can be relatively conveniently obtained from the optimization toolbox in software such as MATLAB, LINDO/LINGO, and SPSS. Therefore, MP methods are still the most popular type of method in the LUA model research field. However, it is difficult for the MP algorithms to cope with complicated and non-linear multi-objective optimization problems. Therefore, most of the MP-based models mentioned above were designed based on a single objective or converted the original problem with multiple objectives into a single-objective optimization problem.
SD-based models view the regional LUA problem as a complex system. On this basis, SD-based models analyze the components of the land-use system as well as the relationship among the components in order to obtain a future LUA scheme for this region [
17]. SD-based models can explain the driving factors for and the trend of the land use change in a region fairly well. Additionally, SD-based models can also dynamically allocate future land use in a region. However, SD-based models mainly focus on the dynamic simulation of the components of the system as well as the relationship among the components. Therefore, SD-based models are usually used to simulate land use demands influenced by economy, technology, population, policy, and their interactions at macro-scales [
18]. Therefore, theoretically, SD-based LUA models are simulation models more than optimization models.
In the past 20 years, the HA method has been considered an effective means to obtain tradeoff solutions for multi-objective optimization problems. So far, a series of heuristic or meta-heuristic algorithms have been used for solving resource allocation optimization or spatial optimization problems [
19] including Genetic Algorithms [
20,
21,
22,
23], Tabu Search [
24], Evolutionary Algorithm [
25,
26,
27], Self-Organizing Algorithms [
28,
29], and Simulated Annealing [
30].
The above LUA optimization models can effectively handle the LUA problems. However, there are still certain limitations in the previous studies: (a) many of the LUA optimization models just consider one objective or convert the original problem with multiple objectives into a single-objective optimization problem by using the weighted method. However, in many application scenarios, it is difficult to reasonably determine the weights of the LUA optimization objectives; (b) Most of the multi-objective optimization models for LUA or land use spatial allocation are based on genetic algorithms or improved genetic algorithms, mainly because of their ability to find a wide spread of Pareto-optimal solutions. However, in some cases, previous studies have demonstrated some shortcomings of these algorithms, such as the ability to converge to Pareto front, diversity of obtained non-dominated solutions, and the running time [
31,
32]. Therefore, it is necessary to design a multi-objective optimization model for LUA based on some other more effective multi-objective optimization algorithms.
Artificial Immune Systems (AIS) can be defined as intelligent and adaptive computational systems inspired by theoretical immunology principles and mechanisms and used to solve real-world problems [
33,
34]. While the main classical models of AIS were not proposed until the early 21st century [
35,
36,
37,
38,
39], AIS models have been widely used in a number of fields in the past 10 years, including pattern recognition [
40,
41], data mining [
42], land use dynamics [
43,
44], and multi-objective decision making [
45]. AIS models have shown their advantages in the multi-objective optimization field. The results of the existing studies demonstrate that the AIS models perform exceptionally well in solving high-dimensional, non-linear, and complex problems, and they have become the focus of the intelligent optimization field [
32].
Consequently, the purpose of this study is to develop a multi-objective optimization LUA model based on AIS to further improve the precision and performance of the LUA optimization. Furthermore, in order to obtain more sustainable optimal LUA schemes, the model is designed under the guidance of “resource conservation and environmentally friendly” philosophy. Theories and approaches in different disciplines are applied to design the LUA optimization model: (a) Land economics theories are adopt to build the framework for analyzing the dynamic balance between the land use supply and demand under the social and economic development situations in the region; (b) The ecosystem service value theory and land rent theory are introduced to estimating the ecological and economic benefits of land use. MOAIM-LUA is currently applied to optimizing and adjusting the LUA scheme in Anlu County, Hebei province, China, but it is easily adaptable to other geographic areas.
The structure of this paper is as follows:
Section 2 describes the MOAIS framework for LUA;
Section 3 uses the optimization model proposed in the present study to solve the LUA problem for Anlu County, Hebei province, China;
Section 4 discusses the experimental results and the optimization performance of the model;
Section 5 provides the conclusions of the present study.
5. Conclusions
This research proposes a novel LUA optimization model for solving land use allocation optimization problems by comprehensively using the analysis method for the balance between land supply and demand, the ecosystem service value theory, the land rent and price theory, and the multi-objective artificial immune optimization algorithm. On this basis, the present study selects Anlu County, in Hubei Province, China, as the experimental area and solves the LUA optimization problem for Anlu County using the population and social and economic development objectives of Anlu County for 2030 as the case study. Compared to the current LUA situation in Anlu County, the optimized LUA schemes offer significant improvements in the economic and ecological objectives. The experimental results demonstrate that the model designed in the present study can obtain better LUA optimization schemes than the Genetic Algorithms-based models.
The contribution of this article is threefold. Firstly, we present an analysis framework of the dynamic balance between the land use supply and demand for LUA optimization problems. Secondly, the ecosystem service value theory and land rent and price theory are introduced to the definition and modeling of LUA optimization problems. Finally, the original artificial immune algorithm is customized by redefining the initialization and mutation operators to be suitable for solving LUA optimization problems.
The functionality of LUA optimization is to balance the land use supply and demand based on the future dynamic demands from different land use stakeholders in the region at the macro-level. However, the quantity allocation schemes for land resources do not have the information necessary for land managers to know how to adjust the use of each parcel to realize the expected quantity allocation scheme for land resources in future land use based on the current regional land use situation at the micro-level. Therefore, in future studies, we will comprehensively use multi-agent systems and multi-objective optimization algorithms to investigate spatial LUA models under the constraints of quantity allocation optimization schemes for land use to formulate spatial land planning schemes that can guide land management and improve the sustainability and feasibility of optimization schemes.