Sustainable Land-Use Allocation: A Multiobjective Particle Swarm Optimization Model and Application in Changzhou, China

Development of a Composite Model for Simulating Landscape Pattern Optimization Allocation: A Case Study in the Longquanyi District of Chengdu City, Sichuan Province, China

“…The traffic land area was set as 15% of the total area of city construction land, countryside construction land, and industrial land [77]. After obtaining the maximum residential land area per capita and the anticipated resident populations of the city and countryside areas, the industrial land area, and traffic land area, we calculated the maximum area of urban-rural residential and industrial-mining in the target year according to Equation (9). In addition to urban-rural residential and industrial-mining, the maximum areas of other landscape types were all set as the total areas of the various landscape types in the study area.…”

“…Thus, the current ecological protection processes were often implemented blindly and in an inefficient manner. Moreover, the conflict between the requirements for economic development and ecological protection has intensified [6][7][8][9]. Thus, coordinating the contradictory relationship between economic development and ecological protection in order to maximize the value of natural resources without affecting ecological protection has become a key issue that affects the sustainable development of China.…”

“…Thus, coordinating the contradictory relationship between economic development and ecological protection in order to maximize the value of natural resources without affecting ecological protection has become a key issue that affects the sustainable development of China. Many studies have confirmed that constructing a reasonable landscape (land resource) spatial pattern by optimizing the landscape (land resource) allocation from a spatial perspective is effective for mitigating conflicts between economic development and ecological protection, and to achieve the goal of sustainable development [6,[9][10][11][12][13][14][15][16].…”

“…Compared with other heuristic algorithms, the main advantages of PSO algorithms are the flexibility and simplicity of its operators, improved space search capability and adaptability, and rapid convergence rate. Thus, PSO algorithms have been widely applied to solve the land-use spatial allocation optimization problem [9,40,43], and they are effective tools for solving complex spatial layout optimization problems for landscape patterns [44]. However, these heuristic-based models mainly focus on spatial pattern optimization whereas they ignore the quantitative structures and beneficial optimization of land use.…”

“…Compared with other traditional evolutionary algorithms, PSO has a superior capacity for searching the space as well as adaptability, in addition to a rapid convergence rate [25,43,47,55]. Furthermore, the successful applications of PSO to various problems have demonstrated its potential, such as the spatial optimization of land-use allocation [6,9,25,40,43,47], facilities location selection [56], optimal allocation of earthquake emergency shelters [57], model parameter optimization [58], feature selection in classification [59], and other problems [60,61]. Many studies have demonstrated that PSO is highly robust and it can obtain more different routes through the problem hyperspace than other evolutionary algorithms [62].…”