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The back EMF-based sensorless control of PMSMs is widely applied to the blowers and hermetic compressors of heating, ventilation and air-conditioning (HVAC) systems. For the sensorless control of PMSMs, a smooth transition from the I–f startup to the closed loop speed control with a rotor speed estimator is required. This paper proposes a smooth transition algorithm that can minimize torque and speed fluctuations under all load conditions. At the starting of a transition, the dq current based on the reference rotor position for I–f control is switched to the dq current with the estimated rotor position. The operating point of the dq current moves from I–f control to MTPA (maximum torque per ampere) operation, while the dq current follows a constant torque curve. Experimental results show that the torque and speed fluctuations under the proposed transition algorithm were significantly reduced under all load conditions. [ABSTRACT FROM AUTHOR]

The decision making of a suitable heating, ventilating and air conditioning system's set-point temperature is an energy and environmental challenge in our society. In the present paper, a general framework to define such temperature based on a dynamic adaptive comfort algorithm is proposed. Due to the fact that the thermal comfort of the occupants of a building has different ranges of acceptability, this method is applied to learn such comfort temperature with respect to the running mean temperature and therefore to decide the suitable range of indoor temperature. It is demonstrated that this solution allows to dynamically build an adaptive comfort algorithm, an algorithm based on the human being's thermal adaptability, without applying the traditional theory. The proposed methodology based on the K-Nearest-Neighbour algorithm was tested and compared with data from an experimental thermal comfort field study carried out in a mixed mode building in the south-western area of Spain and with the Support Vector Machine method. The results show that K-Nearest-Neighbour algorithm represents the pattern of thermal comfort data better than the traditional solution and that it is a suitable method to learn the thermal comfort area of a building and to define the set-point temperature for a heating, ventilating and air-conditioning system. [ABSTRACT FROM AUTHOR]

To solve data imbalance and insufficient prior knowledge problems in using quick access recorder （QAR）for health management and abnormal detection of large passenger aircraft air⁃conditioning system, this paper carries out an algorithm based on improved fuzzy C⁃means （FCM） to evaluate components degradation status. This algorithm is to calculate the distance between fault state and normal state. Based on the physical characteristics of large passenger aircraft’s air⁃conditioning system, the distance function of FCM algorithm is improved, and the difference of the left and the right packs is adopted as the evaluation standard. The overfitting problem of current methods is effectively solved, and the improved FCM algorithm is more sensitive to the degradation of the components in the early stage. It can also effectively reflect the intermediate process of performance degradation. This study provids strong technical support for airlines in arranging maintenance plan and reducing operation cost. [ABSTRACT FROM AUTHOR]

• FDD tools are increasingly adopted in commercial buildings. • Using FDD tools to correct hunting faults can mitigate issues cost-effectively. • We developed and implemented automated control hunting correction in FDD tools. • The algorithm was tested in nine VAV boxes and successfully eliminated hunting faults. • We discussed challenges and future research areas for scaling the technology up. Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology. [ABSTRACT FROM AUTHOR]

In the last decades, short-term load forecasting(STLF) has been the object of particular attention in the power systems field. STLF has been applied almost exclusively to the generation sector, based on variables, which are transversal to most models. Among the most significant variables we can find load, expressed as active power (MW), as well as exogenous variables, such as weather and economy-related ones; although the latter are applied in larger forecasting horizons than STLF. In this paper, the application of STLF to the distribution sector is suggested including inductive reactive power as a forecasting endogenous variable. The inclusion of this additional variable is mainly due to the evidence that correlations between load and weather variables are tenuous, due to the mild climate of the actual case-study system and the consequent feeble penetration of electrical heating ventilation and air conditioning loads. Artificial neural networks (ANN) have been chosen as the forecasting methodology, with standard feed forward back propagation algorithm, because it is a largely used method with generally considered satisfactory results. Usually the input vector to ANN applied to load forecasting is defined in a discretionary way, mainly based on experience, on engineering judgement criteria and on concern about the ANN dimension, always taking into consideration the apparent (or actually evaluated) correlations within the available data. The approach referred in the paper includes pre-processing the data in order to influence the composition of the input vector in such a way as to reduce the margin of discretion in its definition. A relative entropy analysis has been performed to the time series of each variable. The paper also includes an illustrative case study. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The limited drive range (The maximum distance that an EV can travel.) of Electric Vehicles (EVs) is one of the major challenges that EV manufacturers are attempting to overcome. To this end, a simple, accurate, and efficient energy consumption model is needed to develop real-time eco-driving and eco-routing systems that can enhance the energy efficiency of EVs and thus extend their travel range. Although numerous publications have focused on the modeling of EV energy consumption levels, these studies are limited to measuring energy consumption of an EV’s control algorithm, macro-project evaluations, or simplified well-to-wheels analyses. Consequently, this paper addresses this need by developing a simple EV energy model that computes an EV’s instantaneous energy consumption using second-by-second vehicle speed, acceleration and roadway grade data as input variables. In doing so, the model estimates the instantaneous braking energy regeneration. The proposed model can be easily implemented in the following applications: in-vehicle, Smartphone eco-driving, eco-routing and transportation simulation software to quantify the network-wide energy consumption levels for a fleet of EVs. One of the main advantages of EVs is their ability to recover energy while braking using a regenerative braking system. State-of-the-art vehicle energy consumption models consider an average constant regenerative braking energy efficiency or regenerative braking factors that are mainly dependent on the vehicle’s average speed. In an attempt to enhance EV energy consumption models, the proposed model computes the regenerative braking efficiency using the instantaneous vehicle operational variables. The proposed model accurately estimates the energy consumption, producing an average error of 5.9% relative to empirical data. The results also demonstrate that EVs can recover a higher amount of energy in an urban driving environment when compared to high speed highway driving using the proposed model. Moreover, the study also compared different electric vehicles and quantified the impact of auxiliary systems, including the air conditioning and heating systems, on vehicle energy consumption levels using the proposed energy model. The study demonstrated that the use of the heating and air conditioning system could significantly reduce the EV efficiency and travel range. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid algorithm to solve the optimal energy dispatch of an ice storage air-conditioning system. Based on a real air-conditioning system, the data, including the return temperature of chilled water, the supply temperature of chilled water, the return temperature of ice storage water, and the supply temperature of ice storage water, are measured. The least-squares regression (LSR) is used to obtain the input-output (I/O) curve for the cooling load and power consumption of chillers and ice storage tank. The objective is to minimize overall cost in a daily schedule while satisfying all constraints, including cooling loading under the time-of-use (TOU) rate. Based on the Radial Basis Function Network (RBFN) and Ant Colony Optimization, an Ant-Based Radial Basis Function Network (ARBFN) is constructed in the searching process. Simulation results indicate that reasonable solutions provide a practical and flexible framework allowing the economic dispatch of ice storage air-conditioning systems, and offering greater energy efficiency in dispatching chillers. [ABSTRACT FROM AUTHOR]

The installation of smart meters has provided an opportunity to better analyze residential energy consumption and energy-related behaviors. Air-conditioning (A/C) use can be determined through non-intrusive load monitoring, which separates A/C cooling energy consumption from whole-house energy data. In this paper, a disaggregation technique is described and executed on 1-min smart meter data from 88 houses in Austin, TX, USA, from July 2012 through June 2013. Nineteen houses were sub-metered to validate the accuracy of the disaggregation technique. The R 2 value between the predicted and actual A/C energy use for the 19 houses was 0.90. The algorithm was then applied to all houses. On average, daily energy use from A/C increased by 25 ± 11 kWh between a mild temperature day of 15.5 °C (60 °F) and a hotter day of 31.5 °C (89 °F), with an 11 kWh increase just during peak hours (14:00–20:00). Average time operated, number of cycles, and A/C fraction of energy were found to increase linearly with outdoor temperature up to 25 °C (77 °F); a plateau was detected at higher temperatures. The accuracy of A/C disaggregation on 5-min data was found to be comparable to 1-min data. However, 15-min data did not yield accurate results due to insufficient granularity. [ABSTRACT FROM AUTHOR]

• Bringing the asset management of transformer under the purview of building manager from utility. • Derived mathematical model of demand response from transformer thermal model. • Method to estimate transformer temperature time constant from measurements and algorithm for improving the life of transformer by regulating HVAC and AHUs. • Smart grid architecture for demand responsive HVAC. • Real time simulation of impact of demand response using the actual building consumption data from an academic campus. Overloading and ageing effects contribute to 12-15% of distribution transformer failures in India. This research is motivated in extending life of transformer by using demand response as an operational strategy where the demand response events are issued by predicting overheating of transformer before it occurs. In this paper heating ventilation and air conditioning is considered as a flexible load for responding to demand response events. A demo site is created at 11 KV/440 V substation with 1.6 MVA transformer at Indian Institute of Technology Hyderabad (IITH) campus and the proposed architecture and algorithm is implemented. This substation supplies power to chiller plant and three academic buildings with heating ventilation and air conditioning (HVAC). Analysis for improving the transformer lifetime is carried out for 5 consecutive days during the period (25–29)th Feb 2020 using real time field data. It is observed that during this period 11 demand response events occurred with a total demand response of 919 kWH. From the obtained results it is observed that there is a saving of 15 hoursof transformer life within a period of 5 days (120 hours) of transformer operation. The key contribution in this paper is to bring together three concepts transformer lifetime estimation, HVAC flexibility and demand response for improving the transformer lifetime. [ABSTRACT FROM AUTHOR]

Conventional heating, ventilating, and air conditioning (HVAC) systems have traditionally used the temperature and the humidity ratio as the quantitative indices of comfort in a room. Recently, the carbon dioxide (CO2) concentration has also been recognized as having an important contribution to room comfort. This paper presents the modeling of an augmented HVAC system including CO2 concentration, and its control strategies. Because the proposed augmented HVAC system is multi-input multi-output (MIMO) and has no relative degree problem, the dynamic extension algorithm can be employed; then, a feedback linearization technique is applied. A linear-quadratic regulator (LQR) is designed to optimize control performance and to stabilize the proposed HVAC system. Simulation results are provided to validate the proposed system model, as well as its linearized control system. [ABSTRACT FROM AUTHOR]

In order to save energy and improve the control of indoor environments, researchers have developed hundreds of computer algorithms that can automatically and continuously analyze the conditions of HVAC systems. However, the complex information requirements of these algorithms inhibit deploying them in real-world facilities. This paper proposes an integrated performance analysis framework that automatically collects, merges, and provides the information required by them. The information items that are used to represent the information needs are categorized, a domain-specific query language that can formally represent the query statements is formalized, and a library of mechanisms that can automatically reason about and retrieve the needed information is developed. In order to validate the performance of the query language and mechanisms, a prototype was also developed, which includes a graphic user interface that helps users to define the queries, and the reasoning mechanisms that process the queries was implemented. [ABSTRACT FROM AUTHOR]

Highlights: [•] The optimal control gains were obtained from the linear matrix inequalities with mixed H 2 and H ∞ control algorithm. [•] The proposed method guaranteed convergence, stability, and provided a way for disturbance rejection and energy savings. [•] The coupling effects occurred among the compressor speed, opening degrees, evaporator temperatures and superheats. [•] The system identification and thermal dynamics equations could obtain the inner and outer loop transfer functions. [•] All simulations in this paper were in discrete time domain. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we outline a framework and the algorithms that will enable a home controller to accomplish occupancy-moderated multi-zone, multi-inhabitant space-conditioning under a demand-driven pricing scheme for electricity. We investigated the effects that influencing factors such as the number of zones and inhabitants, randomness of occupancy patterns and thermal mass of the residence can have on the efficacy of occupancy-moderated zonal space-conditioning (OZS) under different scenarios. Simulations driven by real data and highly realistic proxy data reveal that OZS can achieve improvements averaging 23% over the case without under certain settings. Our investigations also revealed that a house with heavier thermal mass stands to gain as much as 15% from OZS with pre-conditioning than a lighter one. We further offer a few pointers on reducing the cost of space-conditioning using OZS under a demand-driven pricing scheme to the average homeowner based on our findings. [Copyright &y& Elsevier]

Abstract: Modeling the performance characteristics of thermal systems has been a research interest for many decades with moisture transfer systems experiencing a resurgence over the last decade, especially in heating, ventilating, and air conditioning (HVAC) applications. In this study, a neural network (NN) model is developed to predict the heat and moisture transfer performances (i.e., the sensible and latent effectivenesses) of a novel HVAC energy exchanger called the Run-Around Membrane Energy Exchanger (RAMEE) which is able to transfer both heat and moisture between exhaust and supply air streams. The training data set for the NN model covers a wide range of design and operating parameters and is produced using an experimentally validated finite difference (FD) model. Two separate NNs (one for sensible and one for latent energy transfer) each with five inputs and one output, are selected to represent the RAMEE. The results from NN models are numerically and experimentally validated. The root mean squared error (RMSE) between the FD and NN models are 0.05°C and 2×10−5 kg v /kg a , indicating satisfactory agreement for energy exchange calculations. The paper reports the weights and biases to make the results of this study reproducible. These NN models are very fast and easy to use therefore, they might be used for design and for estimating the annual energy savings in different buildings which use the RAMEE in their HVAC system. Additionally, the NN models can be used with optimization algorithms to maximize energy savings and minimize life-cycle costs for a given system. [Copyright &y& Elsevier]

Environmental control strategy via computerized implementation is one of the most efficient approaches to integrate new advanced knowledge in research of human thermal comfort to a mechanical air-conditioning unit. Recently, a new conceptual development in designing air-conditioning systems has indicated that the indoor comfort temperature strongly depends upon changes of the outdoor air temperature rather than to be a conventional fixed temperature set-point. The explanation is due to occupants' adaptability of thermal comfort to a dynamic environment in terms of their clothing and/or activities while the outdoor temperature can be explicitly used as an ultimate indicator of such changes to empirical function of the indoor comfort temperature. In this paper, the first prototype embedded system is developed to emulate such an adaptive algorithm to numerically determine an indoor comfort temperature for a real-time control in an air-conditioning system. From a theoretical point of view, an adaptive comfort model together with grey prediction model is presented for exploring a practical application of a comfort temperature-based control for a single air-conditioned space, so as to show the viability of the proposed methodology by simulated results. The field studies by interview survey of satisfaction on thermal comfort within an air-conditioned reading room of a library confirm the viability of the proposed real-time computerized implementation of adaptive indoor comfort temperature via the embedded system for a conventional air-conditioning unit in practical uses. [ABSTRACT FROM AUTHOR]

Abstract: A robust fault detection and diagnosis (FDD) strategy using a hybrid approach is presented for pressure-independent variable air volume (VAV) terminals in this paper. The residual-based cumulative sum (CUSUM) control charts are utilized to detect faults in VAV terminals. The residuals between the temperature error and its predication are generated using autoregressive time-series models. The standard CUSUM control charts are used to monitor the residuals which are statistically independent. If the CUSUM value exceeds the chart limits, it means the occurrence of fault or abnormity in the corresponding VAV terminal. The residual-based CUSUM control chart can improve the accuracy of fault detection through eliminating the effects of serial correlation on the performance of control charts. Also, the residual-based CUSUM control chart can enhance the robustness and reliability of fault detection through reducing the impacts of normal transient changes. A rule-based fault classifier consisting of expert rules and fault isolation algorithms is developed to isolate 15 fault sources. The FDD strategy was online tested and validated using in real time data collected from real VAV air-conditioning systems. [Copyright &y& Elsevier]

Abstract: Parallel flow condenser (PFC), which is widely used in automobile air conditioning (AAC) industries, was modeled and optimized in this paper. A sample of designed and manufactured condenser of this type was modeled and tested. In the proposed physical model the condenser is divided into three regions of superheat, saturated (two-phase) and subcooled. The modeling results validated by comparison with the experimental data. In optimization section, the condenser heat transfer rate was maximized while its pressure drop was minimized applying genetic algorithm multi-objective optimization technique. A set of Pareto optimal solutions as well as the final optimal design point were presented for our case study. The optimum design parameters resulted in heat transfer rate increase for 7.1% and decrease in pressure drop for 96% in comparison with the corresponding manufactured operating parameters. [Copyright &y& Elsevier]

Abstract: Indoor space cooling represents a large potential for solar energy use due to the relative coincidence between energy availability and cooling demand. Solar-assisted air conditioning (AC) applications emerged with the development of high efficiency solar collectors. Energy storage (hot or cold) must be implemented for solar-assisted AC applications when cooling demand is present during intervals without available solar energy and also for cooling capacity optimisation (“peak shaving”). The present paper analyses a solar-assisted ejector cooling system with cold storage. Simulations were carried out over one year considering climatic data for a hot location (Béchar, Algeria) and the performance of the system was assessed for a set of design conditions. Effects of cold storage upon comfort conditions and energy demand were evaluated. Maximum room temperature and overall interval of time during which the room temperature exceeded the set-point value were the parameters used to quantify system performance. It was found that cold storage improved comfort conditions compared to a system without storage. For some design conditions it was found that increasing the cold storage capacity did not result in improved comfort conditions. The control algorithm of the system was identified as the cause of this behaviour. [Copyright &y& Elsevier]

Abstract: A direct-expansion (DX) variable-air-volume (VAV) air-conditioning (A/C) system consists of a VAV air-distribution sub-system and a DX refrigeration plant. This paper reports in detail on a novel capacity controller developed for the DX VAV A/C system to regulate its compressor speed and hence its cooling capacity. The capacity controller consisted of both a numerical calculation algorithm (NCA), which was fundamentally based on the principle of energy balance using a number of real-time measured system’s operating parameters, and a dead-band for decoupling the control actions from both the capacity controller and a conventional PI feedback controller for regulating the opening of an electronic expansion valve (EEV) in the refrigeration plant. To study the feasibility of the capacity controller, an experimental rig for the DX VAV A/C system having two conditioned spaces was built and experimental tests were carried out. The test results showed that using the capacity controller, the cooling capacity of the system’s refrigeration plant can be accurately and continuously regulated and the supply air temperature well maintained at its desired value. The desirable independent zoning-control for space air temperatures can be successfully achieved by the DX VAV A/C system and the control performance for air temperatures in the conditioned space was highly satisfactory. [Copyright &y& Elsevier]

Abstract: The refrigerant mass flow distribution in a two phase fluid network of multi-connected air conditioning units (MAU) is generally uniform in the available literature based on the assumption that the fluid resistance of the pipes connecting the evaporators is ignored. However, such a fluid network of multiple evaporators and connecting pipes is actually a complex two phase direct return system, which leads to refrigerant flow distributary disequilibrium, for which few investigations have been conducted. In this paper, with pipe and local fluid resistance considered, fluid network theory was employed to develop a MAU fluid network model as a series of fluid circuits of components including single phase and two phase pipes, two phase electronic expansive valve (EEV), evaporator and pipes in parallel or series. A particular iterating control algorism was designed to distribute the refrigerant mass flow in proportion to the corresponding fluid resistance. For a given refrigerant fluid network with five indoor units, it is concluded by the model that the closer an indoor unit is to the centre of the fluid network, the less its mass flow deviates from its nominal value, and the units at the greatest distance from the centre of the fluid network are the ones with maximum disequilibrium. Furthermore, for a fluid network with multiple indoor units, an IDDR was proposed to evaluate the refrigerant distributary disequilibrium. [Copyright &y& Elsevier]

This paper describes the development and evaluation of a simple algorithm for night ventilation precooling that can be integrated at low cost within a controller for packaged air conditioners that employ economizers, such as rooftop units. In addition to reducing operating costs, the algorithm ensures that indoor comfort is maintained. The basic night ventilation precooling algorithm was developed based upon a simple optimization and heuristics derived from simulation results. The cost savings potential of the algorithm was evaluated using simulations for a range of buildings and climates throughout California. The buildings were representative of small commercial buildings that employ packaged air-conditioning equipment with integrated economizers. The annual savings in compressor energy for night ventilation ranged from about 0 to 53% depending on the location and building type. The savings in total air-conditioning electrical energy usage are much smaller than the compressor savings due to an increase in fan energy. The annual electrical energy savings varied between about 0 and 8%. The electrical demand cost savings associated with night ventilation varied between about 0 and 28%, whereas the total annual electrical cost savings ranged from about 0 to 17%. The greatest percent savings occurred in cooler coastal climates having smaller total loads but significant opportunities for night ventilation precooling. [ABSTRACT FROM AUTHOR]

• A decentralized control method for multi-split air conditioning system is proposed. • Up to 8.6% energy savings are achieved with the proposed control strategy. • The EOTF-based PI controller has better robustness than model predictive controller. Multi-evaporator air conditioning system has found increasing applications in buildings. Energy-efficient control for this kind of system presents significant challenge because of strong coupling effects and various operation modes. Existing control methods are most likely to encounter uncontrollable problems and lack robustness under uncertain operation conditions. This paper proposes an energy-efficient decentralized control method with enhanced robustness for multi-evaporator air conditioning system. The proposed method is featured by a self-optimizing control strategy and a decentralized proportional-integral (PI) control algorithm based on the effective open-loop transfer function (EOTF). Different from most of previous studies that regulated compressor speed to control the evaporating pressure by using a supervisory optimizer, the proposed self-optimizing control strategy regulates compressor speed to maintain constant suction superheat and achieves energy-efficient operation in a simple and robust manner. Meanwhile, the EOTF-based decentralized PI algorithm overcomes the weakness of decentralized controllers in dealing with coupling effects, while retaining robustness against model errors and component faults. Control performance of the proposed method is tested using a validated system dynamic model. Through extensive controllability tests, it is found that the proposed control method significantly improves temperature control performance of the conventional strategy. Up to 8.6% energy savings are achieved with the proposed control strategy. Moreover, the proposed EOTF-based PI controller demonstrates enhanced robustness in comparison with an advanced model predictive controller. [ABSTRACT FROM AUTHOR]

• 1-D CNN is an innovative approach utilized for fault diagnosis in VRF system. • 1-D CNN models achieves good accuracy and is capable of fault diagnosis. • Enhanced the method to an ensemble model by using parallel 1-D CNNs. • The ensemble model's performance is improved and archived state-of-the-art accuracy. Variable refrigerant flow (VRF) systems are widely-adopted air conditioning systems. When system faults occur in VRF systems, the efficiency of VRF system will drop drastically. This paper presents a single 1-D CNN model and an ensemble model with parallel 1-D CNNs for diagnosing VRF system refrigerant charge faults under heating condition. From the cleaned experiment data of a commercial VRF system, 15 features are selected as the input for the proposed model with ReliefF algorithm. After training, the diagnosis accuracy of the single 1-D CNN model and ensemble 1-D CNN models is evaluated and compared with that of BPNN model and DT model. The result shows that both single 1-D CNN and ensemble 1-D CNN model can diagnose VRF system refrigerant charge fault effectively. The fault detection is also achieved in proposed models. The average diagnosis accuracy of 9-level refrigerant charge faults of the ensemble 1-D CNN model is up to 97.4%, surpassing that of BPNN model, SVM model, DT mode and DBN model. 1-D CNN based model is utilized for VRF system fault diagnosis for the first time, which lays a foundation for the expansion of the related researches. [ABSTRACT FROM AUTHOR]

In Taiwan, over 45% of the energy in common buildings is used for the air-conditioning system. In particular, the chiller plant consumes about 70% of the energy in air-conditioning system. The electric energy consumption of air-condition system in a clean room of semiconductor factory is about 5–10 times of that in a common building. Consequently, the optimal chiller loading in energy saving of building is a vital issue. This paper develops a new algorithm to solve optimal chiller loading (OCL) problems. The proposed two-stage differential evolution algorithm integrated the advantages of exploration (global search) in the modified binary differential evolution (MBDE) algorithm and exploitation (local search) in the real-valued differential evolution (DE) algorithm for finding the optimal solution of OCL problems. In order to show the performance of the proposed algorithm, comparison with other optimization methods has been done and analyzed. The result shows that the proposed algorithm can obtain similar or better solution in comparison to previous studies. It is a promising approach for the OCL problem. [ABSTRACT FROM AUTHOR]