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Improving the control strategy of an heating, ventilation, and air-conditioning (HVAC) system can result in substantial energy saving. In this paper, we formulate the whole HVAC system to a bi-level optimization problem to minimize the energy consumption of the HVAC system and maximize the satisfaction of indoor human comfort. The hierarchical evol...

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## Citations

... In other words, BLP is a nested optimization problem. BLP has many applications in theoretical research and real-life applications such as traffic planning [33], eco-traffic signal system [28], HVAC system [56], transportation network design, design of local area networks [4,8], production planning [4], etc. ...

In literature, Clustered Shortest-Path Tree Problem (CluSPT) is an NP-hard problem. Previous studies often search for an optimal solution in relatively large space. To enhance the performance of the search process, two approaches are proposed: the first approach seeks for solutions as a set of edges. From the original graph, we generate a new graph whose vertex set's cardinality is much smaller than that of the original one. Consequently, an effective Evolutionary Algorithm (EA) is proposed for solving CluSPT. The second approach looks for vertex-based solutions. The search space of the CluSPT is transformed into 2 nested search spaces (NSS). With every candidate in the high-level optimization, the search engine in the lower level will find a corresponding candidate to combine with it to create the best solution for CluSPT. Accordingly, Nested Local Search EA (N-LSEA) is introduced to search for the optimal solution on the NSS. When solving this model in lower level by N-LSEA, variety of similar tasks are handled. Thus, Multifactorial Evolutionary Algorithm applied in order to enhance the implicit genetic transfer across these optimizations. Proposed algorithms are conducted on a series of datasets and the obtained results demonstrate superior efficiency in comparison to previous scientific works.

... In other words, BLP is a nested optimization problem. BLP has 165 many applications in theoretical research and real-life applications such as traffic planning [33], eco-traffic signal system [28], HVAC system [56], transportation network design, design of local area networks [4,8], production planning [4], etc. ...

In literature, Clustered Shortest-Path Tree Problem (CluSPT) is an NP-hard problem. Previous studies focus on approximation algorithms which search for an optimal solution in relatively large space. Thus, these algorithms consume a large amount of computational resources while the quality of obtained results is lower than expected. In order to enhance the performance of the search process, this paper proposes two different approaches which are inspired by two perspectives of analyzing the CluSPT. The first approach intuition is to narrow down the search space by reducing the original graph into a multi-graph with fewer nodes while maintaining the ability to find the optimal solution. The problem is then solved by a proposed evolutionary algorithm. This approach performs well on those datasets having small number of edges between clusters. However, the increase in the size of the datasets would cause the excessive redundant edges in multi-graph that pressurize searching for potential solutions. The second approach overcomes this limitation by breaking down the multi-graph into a set of simple graphs. Every graph in this set is corresponding to a mutually exclusive search space. From this point of view, the problem could be modeled into a bi-level optimization problem in which the search space includes two nested search spaces. Accordingly, the Nested Local Search Evolutionary Algorithm (N-LSEA) is introduced to search for the optimal solution of glscluspt, the upper level uses a simple Local Search algorithm while the lower level uses the Genetic Algorithm. Due to the neighboring characteristics of the local search step in the upper level, the lower level reduced graphs share the common traits among each others. Thus, the Multi-tasking Local Search Evolutionary Algorithm (M-LSEA) is proposed to take advantages of these underlying commonalities by exploiting the implicit transfer across similar tasks of multi-tasking schemes. The improvement in experimental results over N-LSEA via this multi-tasking scheme inspires the future works to apply glsm-lsea in graph-based problems, especially for those could be modeled into bi-level optimization.

... In [13], the authors developed a hierarchical design optimization model for facilitating large-scale and simulation-based design tasks in architecture. Additional works on hierarchical control methods [14], [15], [16], and bi-level optimization based on hierarchical evolutionary algorithm [17] were proposed to find the good-quality control and optimization strategy. ...

In literature, Clustered Shortest-Path Tree Problem (CluSPT) is an NP-hard problem. Previous studies focus on approximation algorithms which search for an optimal solution in relatively large space. Thus, these algorithms consume a large amount of computational resources while the quality of obtained results is lower than expected. In order to enhance the performance of the search process, this paper proposes two different approaches which are inspired by two perspectives of analyzing the CluSPT. The first approach intuition is to narrow down the search space by reducing the original graph into a multi-graph with fewer nodes while maintaining the ability to find the optimal solution. The problem is then solved by a proposed evolutionary algorithm. This approach performs well on those datasets having small number of edges between clusters. However, the increase in the size of the datasets would cause the excessive redundant edges in multi-graph that pressurize searching for potential solutions. The second approach overcomes this limitation by breaking down the multi-graph into a set of simple graphs. Every graph in this set is corresponding to a mutually exclusive search space. From this point of view, the problem could be modeled into a bi-level optimization problem in which the search space includes two nested search spaces. Accordingly, the Nested Local Search Evolutionary Algorithm (N-LSEA) is introduced to search for the optimal solution of glscluspt, the upper level uses a simple Local Search algorithm while the lower level uses the Genetic Algorithm. Due to the neighboring characteristics of the local search step in the upper level, the lower level reduced graphs share the common traits among each others. Thus, the Multi-tasking Local Search Evolutionary Algorithm (MLSEA) is proposed to take advantages of these underlying commonalities by exploiting the implicit transfer across similar tasks of multi-tasking schemes. The improvement in experimental results over N-LSEA via this multi-tasking scheme inspires the future works to apply M-LSEA in graph-based problems, especially for those could be modeled into bi-level optimization.

Building, heating, ventilation, and air-conditioning (HVAC) consumes nearly 48% of the entire building energy of the world. Improvement in the control strategy of an HVAC system can result in substantial energy savings, which is the motivation behind this research. In this study, a decentralized method is proposed to search for a good-enough control strategy to reduce the energy consumption of a typical HVAC system consisting of cooling towers, chillers, pumps, and air handling units (AHUs). The method is then compared to a centralized method. In addition to energy consumption, indoor environmental factors such as temperature and humidity are considered in the new method to meet the requirements of human comfort. We introduce experimental conditions for feasibility and the crude energy index in the decentralized method to reduce the computational time. The improved HVAC system was able to save 34% (per 12 h) on energy consumption and the average computational time was reduced to 1247.5 s, which proves the efficiency and effectiveness of the decentralized method and the performance of the proposed control strategy.

It can result in substantial energy saving in heating, ventilation, and air-conditioning (HVAC) system by improving the control strategy of heating, ventilation, and air-conditioning system. However, it is challenging to obtain the optimal control strategy of an HVAC system due to its model’s complexity. In this paper, a regression model is proposed for the wet-bulb temperature which is a key variable in cooling tower and fan coil unit. The proposed model avoids the iterative computing process of obtaining the value of the wet-bulb temperature and reduces the complexity of an HVAC system’s model. Numerical results show that the proposed model takes less than 7% computing time to get the value of wet-bulb temperature, and the relative deviations are less than 0.4%, compared to the original model.