Production-living-ecological space (PLES) constitutes territorial space, and how to scientifically optimize PLES has become the core issue of territorial spatial planning in China. This paper constructs a spatial classification system for PLES based on merge classification. Taking Longli County, Guizhou Province, China, as an example, this paper studies the spatial patterns in 2015 and 2019, the driving factors of the changes in the spatial patterns, and the interrelationships of production space (PS), living space (LS) and ecological space (ES) and proposes a new scheme for dominant functional zoning. The results show that: (1) The high-scoring areas of PS and LS in Longli County are mainly located near the center of each town, with obvious consistency in the spatial distribution. The high-scoring areas of ES are located in the suburbs far from the towns, conflicting with PS and LS; (2) In the five-year period, PS and LS in Longli County continuously expanded. Specifically, LS expanded the most from the perspective of the rate of change, and ES shrunk continuously; (3) Socioeconomic factors are the dominant factor affecting the changes in PLES, among which the distance to town has the greatest influence; (4) Based on the correlation coefficient, PS and LS have a significant positive correlation, but they have a significant negative correlation with ES. In terms of spatial relationships, PS and LS mainly have synergistic relationships, but their relationships with ES mainly involve tradeoffs; (5) In the spatial functional areas of PLES in Longli County, the single dominant functional area is the main area, among which the ecological-dominant functional area is the largest. The results of this study provide a reference for territorial spatial planning and sustainable regional development.

Protecting ecological security has become the backbone of social and economic development since declines in ecological quality due to an increase in human dominance over the natural environment. The establishment of ecological networks is an effective, comprehensive spatial regulation means to ensure regional ecological security. Panzhou city, as a case study, is a typical karst county and has been confronted with the pressure of ecological degradation in recent decades. In this study, an integrated approach combining ecological quality (EQ), ecosystem function importance (EFI), and morphological spatial pattern analysis (MSPA) was developed to determine the ecological sources. Ecological corridors, ecological pinch areas, and ecological barriers were extracted using circuit theory to identify the restored and conserved priority areas of ecological security patterns. The results showed that (1) the remote sensing ecological index (RSEI) and EFI exhibited typical geographical distributions, with the highest values concentrated in the northern and southern parts of the study area and the lowest values scattered in the middle part; (2) 26 patches with forestland, grassland, and waterbodies as the main land cover types were selected as the ecological sources; (3) 63 ecological corridors, composed of 45 key ecological corridors and 18 inactive ecological corridors, were extracted, accounting for 203.12 km and 163.31 km, respectively; (4) 82.76 km² of pinch areas and 320.29 km² of barriers were identified, both of which were distributed on key ecological corridors and played different roles in ecological security; and (5) 4 types of ecological security zones were established according to ecological sources, corridors, pinch areas, and barriers. This integrated approach provides a scientific method for the identification and implementation of ecological networks that can contribute to protecting regional ecological security. Our findings can serve as applicable and reasonable guidance to land administrators and policy-makers for adopting suitable territorial spatial planning, urban planning, green cities, etc.

Airport gate assignment is a critical issue in airport operations management. However, limited airport parking spaces and rising fuel costs have caused serious issues with gate assignment. In this paper, an effective multiobjective optimisation model for gate assignment is proposed, with the optimisation objectives of minimising real-time flight conflicts, maximising the boarding bridge rate, and minimising aircraft taxiing fuel consumption. An improved tunicate swarm algorithm based on cosine mutation and adaptive grouping (CG-TSA) is proposed to solve the airport gate assignment problem. First, the Halton sequence is used to initialise the agent positions to improve the initial traversal and allocation efficiency of the algorithm. Second, the population as a whole is adaptively divided into dominant and inferior groups based on fitness values. To improve the searchability of the TSA for the dominant group, an arithmetic optimisation strategy based on ideas related to the arithmetic optimisation algorithm (AOA) is proposed. For the inferior group, the global optimal solution is used to guide the update to improve the convergence speed of the algorithm. Finally, the cosine mutation strategy is introduced to perturb the optimal solution and prevent the target from falling into the local extrema as a way to efficiently and reasonably allocate airport gates. The CG-TSA is validated using benchmark test functions, Wilcoxon rank-sum detection, and CEC2017 complex test functions and the results show that the improved algorithm has good optimality-seeking ability and shows high robustness in the multiobjective optimisation problem of airport gate assignment.

A wireless sensor network (WSN) is a distributed network system composed of a great many sensor nodes that rely on self-organization. The random deployment of WSNs in city planning often leads to the problem of low coverage of monitoring areas. In the construction of smart cities in particular, a large number of sensor nodes need to be deployed to maintain the reception, processing, and transmission of data throughout the city. However, the uneven distribution of nodes can cause a lot of wasted resources. To solve this problem, this paper proposes a WSN coverage optimization model based on an improved marine predator algorithm (IMPA). The algorithm introduces a dynamic inertia weight adjustment strategy in the global exploration and local exploitation stages of the standard marine predator algorithm to balance the exploration and exploitation capabilities of the algorithm and improve its solution accuracy. At the same time, the improved algorithm uses a multi-elite random leading strategy to enhance the information exchange rate between population individuals and improve the algorithm’s ability to jump out of the local optimum. The optimization experiment results of 11 benchmark test functions and part of the CEC2014 test functions show that the optimization performance of the improved algorithm is significantly better than the standard marine predator algorithm and other algorithms in the literature. Finally, the improved algorithm is applied to the WSN coverage optimization problem. The simulation results demonstrate that the IMPA has a better coverage rate than other metaheuristic algorithms and other improved algorithms in the literature for solving the WSN coverage optimization problem.

Healthy ecosystems are crucial for sustainable regional development. The lack of spatial distribution patterns and driving factors of ecosystem health limited ecosystem management and urban planning. Understanding the spatiotemporal variation characteristics of ecosystem health and its driving factors can contribute to ecosystem management. Based on the “vigor–organization–resilience” (VOR) framework, this paper focuses on increasing ESs and forming an improved “vigor–organization–resilience–ecosystem services (VORS)” framework to evaluate the ecosystem health of Guizhou Province in 2010 and 2020. At the same time, we used the geographic detector model to investigate the driving factors of ecosystem health in the region. The results revealed the following: (1) The areas of forest land accounted for more than 52%. Simultaneously, farmland and forest land decreased, while construction land increased from 2010 to 2020. Construction land was mainly converted from forest land, grassland and farmland. (2) The level of ecosystem health in Guizhou Province spatially increased from northwest to southeast, with the central part exhibiting the lowest health level. The ecosystem health index (EHI) was mainly moderate, accounting for 78.32% and 83.80% in 2010 and 2020, respectively. (3) Among the 11 selected driving factors, the gross domestic product (GDP), general public budget revenue, annual average temperature, average annual precipitation, and night light index significantly affected ecosystem health. Our research refines ecosystem health research and the results will contribute to effective and precise decision-making in ecosystem management and the implementation of land use policies.

Photovoltaic technology, which converts the sun’s light energy directly into electricity, can be used to make photovoltaic cells. The use of photovoltaic cells is centered on the idea of a low-carbon economy and green environmental protection, which effectively addresses the pollution problem in smart cities. Accurate identification of photovoltaic cell parameters is critical for battery life cycle and energy utilization. To accurately identify the single diode model (SDM), dual diode model (DDM), and three diode model (TDM) parameters of solar photovoltaic cells, and an improved honey badger algorithm (IHBA) is proposed in this paper. In the early stages of iteration, the IHBA uses the spiral exploration mechanism to improve the population’s global exploration ability. Furthermore, a density update factor that varies according to the quasi-cosine law is introduced to speed up the algorithm’s convergence speed and prevent the algorithm from falling into the local optimal value. Simultaneously, the pinhole imaging strategy is utilized to disturb the present optimal position to improve the algorithm’s optimization accuracy. The experimental comparison results of 18 benchmark test functions, Wilcoxon rank sum statistical test, and 30 CEC2014 test functions reveal that an IHBA shows remarkable performance in convergence speed, optimization accuracy, and robustness. Finally, the IHBA is used to identify the parameters of three kinds of commercial silicon R.T.C French solar photovoltaic cell models with a 57 mm diameter. In comparison to other algorithms, the IHBA can minimize the root mean square error (RMSE) between the measured current and estimated current at the fastest speed, demonstrating the practicality and superiority of the IHBA in tackling this problem.