Jul 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 15· 0 citations
Abstract
In order to improve the computability and multi-objective coordination ability of the spatial form design of low-carbon campuses, in this study, GIS data processing, parametric generative design and NSGA-II are used. Eight variables such as green space ratio, canopy cover, permeable paving ratio and road network density were hybrid-encoded, while the net life-cycle carbon emissions, outdoor thermal comfort, and walkability were set as the optimization objectives by taking a typical open space at Chengdu East Campus as a study case. The results indicate that 36 non-dominated solutions were obtained from the algorithm. The comprehensive balanced solution resulted in a reduction of the net carbon emissions from 148.6 tCO₂e to 117.9 tCO₂e, an increase of the proportion of thermally comfortable area from 43.2% to 64.5% and an increase of walkability from 0.614 to 0.801. Spatial continuity, canopy supplementation and direct path connectivity allows for the synergic optimization of carbon reduction, environmental improvement and circulation efficiency within a limited site.
: Addressing the complex trade-offs between thermal load reduction, shading efficiency enhancement, and daylighting quality optimization in architectural design, this study constructs a multi-objective optimization strategy model (ODSM) and conducts a long-cycle case analysis. The NSGA-II algorithm searches for Pareto optimal solutions within a seven-dimensional decision space encompassing geometric parameters and material properties, overcoming limitations of traditional static design methods. Empirical analyses across diverse climates — including Shanghai and Anchorage — demonstrate significant energy savings ranging from 35.0% to 39.3%, yielding a climate-adaptive design matrix applicable to both high and low latitudes. For Sungrove University's new student center, the study further incorporates a future climate prediction framework based on an ARMA(2,1) model, simulating hourly temperature evolution from 2026 to 2055 under the RCP 4.5 scenario. Results demonstrate that the proposed optimization scheme — combining circular windows, Low-E glass, and phase change materials — exhibits exceptional robustness under future warming conditions. By 2055, energy consumption increases by only 2.3%, achieving cumulative energy savings of 26,657 kWh. This study provides scientific, quantitative decision-making support for unlocking energy-saving potential in building envelopes throughout their lifecycle.
Rui-Lin Zhou, Sijia Ouyang, Xin-Yu Hua· Proceedings of the 1st Inter...· 0 citations
To address the challenge of synergistically optimizing multiple conflicting objectives, energy efficiency, low carbon emissions, and occupant comfort, in indoor design, this study proposes an improved multi-objective particle swarm optimization (IMOPSO) framework. Tailored to the mixed heterogeneous nature of design variables, the method introduces a probability-driven discrete-variable update mechanism, integrated with adaptive parameter tuning and a dual-archive elitist guidance strategy, enabling automated, iterative coupling with building energy simulation tools. A case study based on a typical office space demonstrates that, compared with NSGA-II, MOEA/D, and standard MOPSO, the Pareto front obtained by IMOPSO achieves better performance in generation distance (0.019) and hypervolume (0.745), with solution-set uniformity improved by approximately 13%. Analysis of optimized solutions reveals that, while maintaining equivalent thermal comfort, operational energy consumption can be reduced by 31.2% and whole-life-cycle carbon emissions by 22.5%. This study provides a quantitative decision-support tool for performance-driven, low-carbon indoor design.
Zhiyi Wang, Lian Wang· Discover Computing· 0 citations
In the face of frequent extreme weather and limited architectural space in modern cities, traditional landscape design methods cannot effectively balance ecological performance and public experience. In this study, a digital twin system is established, and quantitative evaluation models of Ecological Adaptation Degree (EAD) and Interactive Experience Utility (IEU) are constructed respectively. Taking a typical public square as an example, the multi-objective optimization is carried out by integrating GIS (Geographic Information System), sensors, and simulation data. Finally, three typical schemes are obtained, revealing the relationship between resource competition and cost constraint among design variables. The results show that the dynamic balance between ecology and experience can be achieved by calculation optimization, which also provides a set of feasible methods for data-driven urban landscape design.
Jue Xia· International Journal of Agr...· 0 citations
Agriculture plays an exceedingly pivotal role in the future growth and development of a nation, relying heavily on its water and land resources. To achieve maximum agricultural productivity, it is crucial to ensure timely and adequate supply of both land and water for irrigated farming. This necessitates precise planning and execution of water resource management, coupled with the incorporation of modern technologies to optimize the utilization of available resources. A significant task in economic and sustainable agricultural decision-making involves optimizing resource constraints within a given planting period. Considering the socioeconomic conditions, the present study tackles a multi-objective optimization problem to identify an optimal crop plan that simultaneously maximizes profit while minimizing groundwater usage. In single-objective optimization problems, determining the superior solution was straightforward by comparing objective function values, whereas in multi-objective optimization, dominance criteria are employed for determination. The study focuses on the genetic algorithm based NSGA-II and NSGA-III algorithms, comparing their performance and effectiveness through result analysis. Additionally, graphical comparisons are presented to showcase the Pareto fronts. The findings indicate that NSGA-III is a more viable tool for addressing optimal crop planning problems compared to NSGA-II.
problem. ARPN Journal of Engineering and Applied Sciences, 11, 4079-4086.Kuo, S.F., Merkley, G.P., and Liu, C.W. (2000). Decision support for irrigation project planning using a genetic algorithm. Agricultural Water Management, 45(3), 243-266.275Vafaeinejad, A. (2016). Cropping Pattern Optimization by Using of TOPSIS and Genetic Algorithm Based on the Capabilities of GIS. Iranian journal of Ecohydrology, 3(1), 69-82.Van Veldhuizen, D.A., and Lamont, G.B. (2000). On measuring multiobjective evolutionary Lalehzari, R., Boroomand Nasab, S., Moazed, H., and Haghighi, A. (2016). Multiobjective management of water allocation to algorithm performance. In Proceedings of the 2000 Congress on Evolutionary Computation. CEC00, 1, 204-211, IEEE.sustainable irrigation planning and optimal cropping pattern. Journal of Irrigation and Drainage Engineering, 142(1), 05015008.Mansourifar, M., Almassi, M., Borghaee, A.M., and Moghadassi, R. (2013). Optimization crops pattern in variable field ownership. World Applied Sciences Journal, 21(4), 492-497.Marko, O., Pavlović, D., Crnojević, V., and Deb, K. (2019). Optimisation of crop configuration using NSGA-III with categorical genetic operators. In Proceedings of the Genetic and Evolutionary Computation Conference Companion.Márquez, A.L., Baños, R., Gil, C., Montoya, M.G., Manzano‐Agugliaro, F., and Montoya, F.G. (2011). Multi‐objective crop planning using pareto‐based evolutionary algorithms. Agricultural Economics, 42(6), 649-656.Mwiya, R.M., Zhang, Z., Zheng, C., and Wang, C. (2020). Comparison of Approaches for Irrigation Scheduling Using AquaCrop and NSGA-III Models under Climate Uncertainty. Sustainability, 12(18), 7694.Nath, K., Jain, R., Marwaha, S., Roy, H.S. and Arora, A. (2020). Identification of optimal crop plan using nature inspired metaheuristic algorithms. Indian Journal of Agricultural Sciences, 90(8), 1587-92.Olakulehin, O.J., and Omidiora, E.O. (2014). A genetic algorithm approach to maximize crop yields and sustain soil fertility. Net Journal of Agricultural Science, 2(3), 94-103.Oluwole, A.A., Oludayo, O.O., and Josiah, A. (2014). A comparative study of state-of-the-art evolutionary multi-objective algorithms for optimal crop-mix planning. International Journal of Agricultural Science and Technology, 2(1), 1-9.Pal, B.B., Chakraborti, D., and Biswas, P. (2009). A genetic algorithm based hybrid goal programming approach to land allocation problem for optimal cropping plan in agricultural system. In International Conference on Industrial and Information Systems (ICIIS).Raju, K.S., Vasan, A., Gupta, P., Ganesan, K., and Mathur, H. (2012). Multi-objective differential evolution application to irrigation planning. ISH Journal of Hydraulic engineering, 18(1), 54-64.Sadati, S.K., Speelman, S., Sabouhi, M., Gitizadeh, M., and Ghahraman, B. (2014). Optimal irrigation water allocation using a genetic algorithm under various weather conditions. Water, 6(10), 30683084.Srinivas, N., and Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 2(3), 221-248.Sarker, R., and Ray, T. (2009). An improved evolutionary algorithm for solving multi-objective crop planning models. Computers and electronics in agriculture, 68(2), 191-199.Sarma, A.K., Misra, R., and Chandramouli, V. (2006). Application of genetic algorithm to Opsearch, 43(3), 320-329.determine optimal cropping pattern.
problem. ARPN Journal of Engineering and Applied Sciences, 11, 4079-4086.Kuo, S.F., Merkley, G.P., and Liu, C.W. (2000). Decision support for irrigation project planning using a genetic algorithm. Agricultural Water Management, 45(3), 243-266.275Vafaeinejad, A. (2016). Cropping Pattern Optimization by Using of TOPSIS and Genetic Algorithm Based on the Capabilities of GIS. Iranian journal of Ecohydrology, 3(1), 69-82.Van Veldhuizen, D.A., and Lamont, G.B. (2000). On measuring multiobjective evolutionary Lalehzari, R., Boroomand Nasab, S., Moazed, H., and Haghighi, A. (2016). Multiobjective management of water allocation to algorithm performance. In Proceedings of the 2000 Congress on Evolutionary Computation. CEC00, 1, 204-211, IEEE.sustainable irrigation planning and optimal cropping pattern. Journal of Irrigation and Drainage Engineering, 142(1), 05015008.Mansourifar, M., Almassi, M., Borghaee, A.M., and Moghadassi, R. (2013). Optimization crops pattern in variable field ownership. World Applied Sciences Journal, 21(4), 492-497.Marko, O., Pavlović, D., Crnojević, V., and Deb, K. (2019). Optimisation of crop configuration using NSGA-III with categorical genetic operators. In Proceedings of the Genetic and Evolutionary Computation Conference Companion.Márquez, A.L., Baños, R., Gil, C., Montoya, M.G., Manzano‐Agugliaro, F., and Montoya, F.G. (2011). Multi‐objective crop planning using pareto‐based evolutionary algorithms. Agricultural Economics, 42(6), 649-656.Mwiya, R.M., Zhang, Z., Zheng, C., and Wang, C. (2020). Comparison of Approaches for Irrigation Scheduling Using AquaCrop and NSGA-III Models under Climate Uncertainty. Sustainability, 12(18), 7694.Nath, K., Jain, R., Marwaha, S., Roy, H.S. and Arora, A. (2020). Identification of optimal crop plan using nature inspired metaheuristic algorithms. Indian Journal of Agricultural Sciences, 90(8), 1587-92.Olakulehin, O.J., and Omidiora, E.O. (2014). A genetic algorithm approach to maximize crop yields and sustain soil fertility. Net Journal of Agricultural Science, 2(3), 94-103.Oluwole, A.A., Oludayo, O.O., and Josiah, A. (2014). A comparative study of state-of-the-art evolutionary multi-objective algorithms for optimal crop-mix planning. International Journal of Agricultural Science and Technology, 2(1), 1-9.Pal, B.B., Chakraborti, D., and Biswas, P. (2009). A genetic algorithm based hybrid goal programming approach to land allocation problem for optimal cropping plan in agricultural system. In International Conference on Industrial and Information Systems (ICIIS).Raju, K.S., Vasan, A., Gupta, P., Ganesan, K., and Mathur, H. (2012). Multi-objective differential evolution application to irrigation planning. ISH Journal of Hydraulic engineering, 18(1), 54-64.Sadati, S.K., Speelman, S., Sabouhi, M., Gitizadeh, M., and Ghahraman, B. (2014). Optimal irrigation water allocation using a genetic algorithm under various weather conditions. Water, 6(10), 30683084.Srinivas, N., and Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 2(3), 221-248.Sarker, R., and Ray, T. (2009). An improved evolutionary algorithm for solving multi-objective crop planning models. Computers and electronics in agriculture, 68(2), 191-199.Sarma, A.K., Misra, R., and Chandramouli, V. (2006). Application of genetic algorithm to Opsearch, 43(3), 320-329.determine optimal cropping pattern.
Kamalika Nath, Rajni Jain, R. Paul et al.· Journal of the Indian Societ...· 0 citations
: Against the backdrop of intensifying ecological pressures on Earth and the continued advancement of deep-space exploration strategies, establishing a permanent lunar colony capable of accommodating 100,000 people has become a long-term development goal. To address challenges including high transportation costs, stringent resource constraints, and significant environmental impacts, this study constructs a multi-objective discrete optimization framework centered on a coupled “transportation– resource – environme nt” system to determine optimal transportation and resource-supply strategies. First, based on projections of transportation capacity and unit payload in 2050, a closed-loop water-resource model is developed by integrating hierarchical water recycling, in-situ resource utilization (ISRU), electrolytic fuel-production modules, and dynamic safety-stock simulation to estimate net water-transport demand and its associated costs. Next, an environmental model based on life cycle assessment (LCA) is introduced to convert carbon emissions into discounted environmental costs, forming a three-objective optimization model that balances economic cost, construction duration, and environmental impact. Results show that under ideal conditions, annual net water-transport demand ranges from 200,000 to 830,000 tons. When space-elevator transport accounts for 69% of total capacity, the weighted total cost is minimized at USD 919.996 trillion. Inventory simulations confirm year-round water-supply sustainability while significantly reducing carbon emissions.
Jianxin Xie, Shengli Shao, Qianya Yu· Proceedings of the 1st Inter...· 0 citations
Amid the low-carbon transition, university teaching buildings feature high occupancy and energy use, making the synergistic enhancement of their daylighting and thermal environments crucial for “dual carbon” goals. However, traditional “north–south homogenization” designs in cold regions fail to address the spatial heterogeneity of solar radiation and climate resources, intensifying the trade-off between natural daylighting and Heating Energy Use Intensity (Eh) while restricting space performance optimization. Focusing on a typical cold-region teaching building, this study proposes a “parametric modeling–multi-objective optimization–machine learning” integrated framework. Targeting spatial daylight autonomy (sDA), useful daylight illuminance (UDI), and Eh, we compared the homogeneous baseline model with the Pareto-optimal solution set, demarcated key design parameter boundaries, and developed an ensemble-based rapid prediction model. Based on the parametric simulation analysis of this representative case building in a cold region, results indicate that: (1) Compared to the baseline, the overall optimal scheme reduced Eh by 17.43% while increasing UDI and sDA by 12.0% and 10.5%, respectively. (2) The Pareto set strictly converges toward a due-south orientation and a “deep-south, shallow-north” layout (depth ratio: 0.66–0.77); thermal configurations exhibit “enhanced northern insulation and southern heat gain,” confirming heterogeneous design matches cold climates better. (3) The four constructed machine learning models (MLP, LightGBM, XGBoost, and Random Forest) uniformly achieved test recall rates exceeding 99%, enabling highly precise, rapid classification of top-performing design scenarios during early-stage design. This study overcomes climate-matching blindness in traditional design, providing a multi-objective synergistic optimization path balancing low energy and high-quality daylighting with substantial engineering and theoretical value.
Ming Yang, Jieli Sui· Buildings· 0 citations
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