scholarly journals MULTI-OBJECTIVE LAND USE OPTIMIZATION THROUGH PARALLEL PARTICLE SWARM ALGORITHM: CASE STUDY BABOLDASHT DISTRICT OF ISFAHAN, IRAN

2016 ◽  
Vol 10 (1) ◽  
pp. 42-49 ◽  
Author(s):  
Alireza Sahebgharani
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Planning ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Multi Objective ◽  
Land Use Optimization ◽  
Land Types ◽  
Better Than

Land use planning seeks to divide land, the most valuable resource in the hands of planners, among different land types. During this process, various conflicting objectives are emerged which land use planners should prepare land use plans satisfying these objectives and deal with a large set of data and variable. For this reason, land use allocation is a multi-objective NP-hard optimization problem which is not solvable by the current exact methods. Therefore, solving land use optimization problem relies on the application of meta-heuristics. In this paper, a novel meta-heuristic named parallel particle swarm is developed to allocate seven land types (residential, commercial, cultural, educational, medical, sportive and green space) to Baboldasht district of Isfahan covered by 200 allocation cells with size 1000 m2 for maximizing compactness, compatibility and suitability objective functions. Afterwards, the outputs of the new developed algorithm are compared to the outputs of genetic algorithm. The results demonstrated that the parallel particle swarm is better than genetic algorithm in terms of both solution quality (1.35%) and algorithm efficiency (63.7%). The results also showed that the outputs achieved by both algorithms are better than the current state of land use distribution. Thus, the method represented in this paper can be used as a useful tool in the hands of urban planners and decision makers, and supports the land use planning process.

scholarly journals MULTI-OBJECTIVE LAND USE OPTIMIZATION THROUGH PARALLEL PARTICLE SWARM ALGORITHM: CASE STUDY BABOLDASHT DISTRICT OF ISFAHAN, IRAN

2016 ◽  
Vol 10 (1) ◽  
pp. 42-49
Author(s):  
Alireza Sahebgharani
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Planning ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Multi Objective ◽  
Land Use Optimization ◽  
Land Types ◽  
Better Than

Land use planning seeks to divide land, the most valuable resource in the hands of planners, among different land types. During this process, various conflicting objectives are emerged which land use planners should prepare land use plans satisfying these objectives and deal with a large set of data and variable. For this reason, land use allocation is a multi-objective NP-hard optimization problem which is not solvable by the current exact methods. Therefore, solving land use optimization problem relies on the application of meta-heuristics. In this paper, a novel meta-heuristic named parallel particle swarm is developed to allocate seven land types (residential, commercial, cultural, educational, medical, sportive and green space) to Baboldasht district of Isfahan covered by 200 allocation cells with size 1000 m2 for maximizing compactness, compatibility and suitability objective functions. Afterwards, the outputs of the new developed algorithm are compared to the outputs of genetic algorithm. The results demonstrated that the parallel particle swarm is better than genetic algorithm in terms of both solution quality (1.35%) and algorithm efficiency (63.7%). The results also showed that the outputs achieved by both algorithms are better than the current state of land use distribution. Thus, the method represented in this paper can be used as a useful tool in the hands of urban planners and decision makers, and supports the land use planning process.

scholarly journals Spatial Multi-Objective Land Use Optimization toward Livability Based on Boundary-Based Genetic Algorithm: A Case Study in Singapore

2020 ◽  
Vol 9 (1) ◽  
pp. 40 ◽  
Author(s):  
Kai Cao ◽  
Muyang Liu ◽  
Shu Wang ◽  
Mengqi Liu ◽  
Wenting Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Central Region ◽  
Optimization Model ◽  
Optimization Problem ◽  
Weighted Sum ◽  
Multi Objective ◽  
Land Use Optimization ◽  
Future Direction

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have been successfully conducted, which can demonstrate the effectiveness of the spatial multi-objective land use optimization model developed in this research as well as the robustness and reliability of computer-generated solutions. In addition, the comparison between the computer-generated solutions and the two real planned scenarios has also clearly demonstrated that our generated solutions are much better in terms of fitness values. Lastly, the limitation and future direction of this research have been discussed.

The Multi-Objective Space Optimal Allocation of Urban Land Based on Spatial Genetic Algorithm

2018 ◽  
Vol 43 (1) ◽  
pp. 21-25 ◽  
Author(s):  
Xinhua Zhu
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Planning ◽  
Optimization Design ◽  
Optimal Allocation ◽  
Urban Land ◽  
Urban Land Use ◽  
Multi Objective ◽  
Planning And Design ◽  
Objective Space

With the constant increasing scale of urban buildings, the contradiction between supply and demand of land use problems is more prominent. Therefore, the multi-objective space optimal allocation of urban land use based on spatial genetic algorithm was proposed in this paper. Firstly, the present situation of the urban land use resources was expounded; in view of the urban land use planning, a spatial genetic algorithm was proposed; then, the urban land was divided into different functional areas, and the land planning and design method was put forward; finally, taking a city's land space planning as an example, the optimal planning and design were carried out to the geological disasters, low hilly land and land overall utilization; by comparing the land use before and after the planning optimization, the advantages of land optimization design were confirmed.

Multivariant Optimization Algorithm for the 0-1 Knapsack Problem

2014 ◽  
Vol 556-562 ◽  
pp. 3514-3518
Author(s):  
Lan Juan Liu ◽  
Bao Lei Li ◽  
Qin Hu Zhang ◽  
Dan Jv Lv ◽  
Xin Ling Shi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Heuristic Algorithm ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Global Search ◽  
Particle Swarm Algorithm ◽  
Data Sets ◽  
Better Than

In this paper, a novel heuristic algorithm named Multivariant Optimization Algorithm (MOA) is presented to solve the 0-1 Knapsack Problem (KP). In MOA, multivariant search groups (locate and global search groups) execute the global exploration and local exploitation iteratively to locate the optimal solution automatically. The presented algorithm has been compared with Genetic Algorithm (GA) and Particle swarm algorithm (PSO) based on five data sets, results show that the optimization of MOA is better than GA and PSO when the dimension of problem is high.

scholarly journals Practical Efficient Regional Land-Use Planning Using Constrained Multi-Objective Genetic Algorithm Optimization

2021 ◽  
Vol 10 (2) ◽  
pp. 100
Author(s):  
Tingting Pan ◽  
Yu Zhang ◽  
Fenzhen Su ◽  
Vincent Lyne ◽  
Fei Cheng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Planning ◽  
Novel Mutation ◽  
Spatial Optimization ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Multi Objective ◽  
Regional Land ◽  
Near Optimality

Practical efficient regional land-use planning requires planners to balance competing uses, regional policies, spatial compatibilities, and priorities across the social, economic, and ecological domains. Genetic algorithm optimization has progressed complex planning, but challenges remain in developing practical alternatives to random initialization, genetic mutations, and to pragmatically balance competing objectives. To meet these practical needs, we developed a Land use Intensity-restricted Multi-objective Spatial Optimization (LIr-MSO) model with more realistic patch size initialization, novel mutation, elite strategies, and objectives balanced via nominalizations and weightings. We tested the model for Dapeng, China where experiments compared comprehensive fitness (across conversion cost, Gross Domestic Product (GDP), ecosystem services value, compactness, and conflict degree) with three contrast experiments, in which changes were separately made in the initialization and mutation. The comprehensive model gave superior fitness compared to the contrast experiments. Iterations progressed rapidly to near-optimality, but final convergence involved much slower parent–offspring mutations. Tradeoffs between conversion cost and compactness were strongest, and conflict degree improved in part as an emergent property of the spatial social connectedness built into our algorithm. Observations of rapid iteration to near-optimality with our model can facilitate interactive simulations, not possible with current models, involving land-use planners and regional managers.

Trajectory optimization for manipulators based on external archives self-searching multi-objective particle swarm optimization

2021 ◽  
pp. 095440622199748
Author(s):  
Youyu Liu ◽  
Xuyou Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
B Splines ◽  
Multi Objective ◽  
Searching Strategy ◽  
Better Than

In order to improve the quality of the non-inferior solutions obtained by multi-objective particle swarm optimization (MOPSO), an improved algorithm called external archives self-searching multi-objective particle swarm optimization (EASS-MOPSO) was proposed and applied to a multi-objective trajectory optimization problem for manipulators. The position curves of joints were constructed by using quartic B-splines; the mathematical models of time, energy and jerk optimization objectives for manipulators were established; and the kinematic constraints of joints were transformed into the constraints of the control vertexes of the B-splines. A self-searching strategy of external archives to make non-inferior solutions have the ability to search the surrounding hyperspace was explored, and a diversity maintaining strategy of the external archives was proposed. The results of several test functions by simulation show that the convergence and diversity of the proposed algorithm are better than those of other 4 selected algorithms; the results of the trajectory optimization problem for manipulators by simulation show that the convergence, diversity and time consumption of the proposed algorithm are significantly better than those of non-dominated sorting genetic algorithm.

scholarly journals Optimization of Land-Use Structure Based on the Trade-Off Between Carbon Emission Targets and Economic Development in Shenzhen, China

2018 ◽  
Vol 11 (1) ◽  
pp. 11 ◽  
Author(s):  
Dang Han ◽  
Ruilin Qiao ◽  
Xiaoming Ma
Keyword(s):  
Land Use ◽  
Carbon Emissions ◽  
Land Use Planning ◽  
Programming Model ◽  
Low Carbon ◽  
Land Use Pattern ◽  
Intergovernmental Panel ◽  
Multi Objective ◽  
Land Use Structure ◽  
Land Types

The approach of choosing an effective low-carbon land-use structure by multi-objective methods is commonly used in land-use planning. A common methodology is to calculate carbon emissions and conduct scenario simulations for the future. However, most Chinese cities have not implemented the methods for monitoring carbon emissions proposed by the Intergovernmental Panel on Climate Change (IPCC), especially Shenzhen, which is one of the fastest-growing cities in China. This study first calculated the carbon emissions for a typical year in Shenzhen under the guidance of the IPCC. Second, nighttime light data were used to spatialize the gross domestic product to obtain the economic benefit coefficients of the various land types. Finally, a multi-objective linear programming model was used to optimize the land-use structure under different scenarios for 2020 and 2025. The results show that (i) energy consumption contributed the most to local carbon emissions in 2016, at 94.75%; (ii) carbon emissions from paddy fields, animals, and humans were the second most dominant source; (iii) the intensity of carbon emissions from different land types in 2016 was variable; and (iv) compared with the natural scenario, an optimized land-use structure could reduce carbon emissions by 5.97% by 2020 and 12.61% by 2025. Under ideal simulation conditions, the simulated land-use pattern could not only meet the requirements of economic and social development, but also could effectively reduce carbon emissions, which is of great value to land managers and decision-makers.

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