Optimum operational conditions of a rotary regenerator using genetic algorithm

The optimum operational conditions of an air-to-air rotary regenerator (also called air preheater or heat wheel) for air conditioning applications which was designed and manufactured in Energy Systems Improvement Laboratory (ESIL) has been investigated in this paper. The performance of such a rotary regenerator was modeled and the numerical values of modeling output were verified with the experimental data obtained from the equipment testing. In the next step, the optimum operational conditions of the rotary regenerator were obtained using genetic algorithm optimization technique subject to a list of constraints. The objective function in the optimization technique was the thermal effectiveness, while the design parameters (decision variables) were volumetric flow rates of cold and hot air streams, matrix rotational speed, and the exchanger frontal area (heat transfer surface area). The apparatus was tested under the optimized operating conditions and the results were compared with the results obtained numerically applying genetic algorithm optimization. The experimental value for the effectiveness showed an acceptable closeness (2.07%) with the corresponding value obtained from the system modeling and optimization. The economic analysis of energy savings by the designed and manufactured regenerator showed a pay back period about 3 years.     

Development of a shredding genetic algorithm for structural reliability

Classical genetic algorithms (GA) have been previously proposed for structural reliability analysis but have not been widely adopted because of their inefficiencies. To improve the efficiency of the search process, this paper develops a modified algorithm, referred to as the shredding genetic algorithm (SGA). SGA follows the practice adopted in modern breeding technology where healthy animals are cultivated by interfering with the natural selection process and filtering out pubs with undesirable characteristics using the principle of elitism. By simulating this filtration process, SGA focuses the search around the most important genes thereby improving GA’s efficiency.Following the process used during the human genome project, the filtration criteria are established after shredding each chromosome into strings of genes each of which is associated with a fitness factor. The strings’ fitness factors are assembled into a fitness index matrix that is updated generation by generation as more information about the fitness of chromosomes is gathered. The chromosomes created during the crossover steps are filtered to eliminate those containing strings that do not satisfy a probabilistic filtration standard.Similarly to classical reliability analysis techniques, SGA identifies dominant structural failure modes and also gives detailed information about which random variables are primary contributors to the formation of these failure modes. Furthermore, SGA provides information about linkages that may exist between the random variables that control the safety of structural systems. These linkages allow the identification of sub-mechanisms or partial failures. Such useful information would eventually lead to better control of the safety of structural systems and improve the reliability of designs. Examples are provided to demonstrate the application of the proposed SGA method and its efficiency.     

Augmented heuristic algorithm for multi-skilled resource scheduling

Conventional project scheduling is restricted to single-skilled resource assumption where each worker is assumed to have only one skill. This, in effect, contradicts real-world practice where workers may possess multiple skills and, on several occasions, are assigned to perform tasks for which they are not specialized. Past research has shown a simple process of heuristic approach for multi-skilled resource scheduling where a project is planned under the assumption that each resource can have more than one skill and resource substitution is allowed. Nevertheless, the approach has presented resource substitution step where an activity with higher priority can claim any resource regardless of its concurrent activities' resource requirements. Furthermore, the approach is subjected to all-or-nothing resource assignment concept where an activity cannot start and resources are not needed for that activity at all unless the required resources of that activity can be completely fulfilled. This research presents an alternative heuristic approach for multi-skilled resource scheduling in an attempt to improve the resource substitution approach. Augmented resource substitution rule and resource-driven task duration are presented to increase starting opportunity of activities on earlier time. Case studies are presented to illustrate the improved result of shorter project duration.     

A new approach for project scheduling using fuzzy dependency structure matrix

As an alternative solution, the Dependency Structure Matrix (DSM) is a useful tool in project scheduling when approaching information dependency issues between activities. However, the current DSM approach faces the dilemma that the overlap of activities cannot be precisely estimated in the planning stage of a project, and the solution calls for a robust methodology for managing schedules within uncertain conditions of information dependency. The aim of our research is to propose an approach that utilizes fuzzy set theory to solve the problem within an uncertain environment. As an extension of traditional DSM-based scheduling, we describe the overlap and duration of activities as fuzzy numbers and put forth a systematic algorithm to calculate the time variables of activities and project duration thereof. An example is also provided to demonstrate the effectiveness of the algorithm.     

Fuzzy logic control for a parallel hybrid hydraulic excavator using genetic algorithm

In this paper, fuzzy logic control is put forward in a parallel hybrid hydraulic excavator for the purposes of better energy distribution and higher fuel economy. A mathematical model of parallel hybrid hydraulic excavator is presented in detail, and the parameters of components and overall system are listed and analyzed. The fuzzy logic controller is then designed to cope with energy distribution and management. To achieve better equivalent fuel consumption, genetic algorithm is implemented to fine-tune the membership functions. The control effects are compared between different control strategies, e.g. rule-based control and fine-tuned fuzzy logic control. The results indicate that hybrids with the proposed strategy can improve fuel economy for the excavator without sacrificing any system performance.     

Precedence relationship options for the resource levelling problem using a genetic algorithm

The resource levelling problem (RLP) arises in project scheduling where the intention is to reduce the fluctuation of the resource demand. The solutions of the RLP are achieved through the rearrangement of the project activities under a constraint of preselected precedence relationships. To relieve the constraint, a new concept of the RLP with relationship options is initiated. An activity of the project network can have one or more alternative types of relationship with other activities as appropriate. An alternative relationship provides more float time and allows new possibilities for the arrangement of efficient patterns of the project resource demand. The scheduling problem model was formulated by using mathematical equations on spreadsheet software and solved by using the genetic algorithm based optimization. The prototype was tested in two different project instances. The test results demonstrated that this new model could calculate and arrange the project schedules for all selected alternative types of relationships. The model with relationship options provided consistent results for efficient resource utilization schedules, which were better than the one without relationship options. These schedules could decrease the resource demand fluctuation and the maximum resource demand level. This new model of the RLP with relationship options not only provides the additional flexibility to level the resource demands but also determines suitable types of relationships for the project activities.     

Using a genetic algorithm to develop a pile design method

A genetic algorithm (GA) was used in this study to develop a standard penetration test (SPT)-based design method for the axial capacity of driven piles. A total of 72 pile load tests was collected from literature and divided into two groups based on their measurements. The first group had the load-transfer distribution measurements for extracting both the unit side and tip resistances. These unit resistances were correlated by the GA with soil measurements and pile properties to develop the design method. The second group, where only the total capacity measurements were available, were used to validate the new design method and compare its performance with three existing SPT-based design methods. The new GA-derived design method considers nonlinear relationships with the effective stress and pile length and provides an unbiased prediction with a low coefficient of variation (COV) of 40.0 %, while the three existing methods overestimate the capacity by a factor of 1.62 to 1.65 with a high COV of 40.3 % to 52.8 %, which could result in an under design of pile foundations. This study shows that the GA was able to obtain complex relationships with great accuracy and the new design method can be applied to new cases reasonably well.     

Tracing sediment loss from eroding farm tracks using a geochemical fingerprinting procedure combining local and genetic algorithm optimisation

Eroding farm tracks represent important spatially distributed features in many agricultural landscapes and there is concern over their role in catchment sediment problems. It is, however, important to place eroding farm tracks in the context of catchment sediment sources more generally, especially since the former afford potential for targeted sediment mitigation. A sediment source tracing procedure was therefore used to assess the importance of eroding farm track surfaces as a contemporary primary suspended sediment source relative to inputs from pasture or cultivated topsoils and channel banks/subsurface sources, in the upper River Piddle catchment (~ 100 km²), in southern England. The study provided a timely opportunity to assess the performance of both local and global (genetic algorithm; GA) optimisation techniques in the sediment geochemistry mass balance modelling used to apportion sources. Over the duration of the study, average median source contributions for individual time-integrated suspended sediment samples collected from three sub-catchments ranged between 1 ± 1 and 19 ± 3% for farm track surfaces, 31 ± 3 and 55 ± 2% for pasture topsoils, 1 ± 1 and 19 ± 1% for cultivated topsoils and 23 ± 2 and 49 ± 1% for channel banks/subsurface sources. Comparison of the local and GA optimisation techniques demonstrated that GA with random initial values improved the minimisation of the objective functions compared to local searching by 0.01-0.04% of 5000 repeat Monte Carlo iterations. GA informed by the outputs of the local optimisation as initial values improved corresponding performance by 0.05-0.20%. These findings increased confidence in the outputs from the local optimisation mass balance modelling, but fingerprint property datasets should be treated on an individual basis. Future sediment source tracing studies should always endeavour to combine local and global search tools to avoid the risk of using localised solutions for source apportionment estimates.     

Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm

The response of rocks to stress can be highly non-linear, so sometimes it is difficult to establish a suitable constitutive model using traditional mechanics methods. It is appropriate, therefore, to consider modeling methods developed in other fields in order to provide adequate models for rock behavior, and this particularly applies to the time-dependent behavior of rock. Accordingly, a new system identification method, based on a hybrid genetic programming with the improved particle swarm optimization (PSO) algorithm, for the simultaneous establishment of a visco-elastic rock material model structure and the related parameters is proposed. The method searches for the optimal model, not among several known models as in previous methods proposed in the literatures, but in the whole model space made up of elastic and viscous elementary components. Genetic programming is used for exploring the model's structure and the modified PSO is used to identify parameters (coefficients) in the provisional model. The evolution of the provisional models (individuals) is driven by the fitness based on the residual sum of squares of the behavior predicted by the model and the actual behavior of the rock given by a set of mechanical experiments. Using this proposed algorithm, visco-elastic models for the celadon argillaceous rock and fuchsia argillaceous rock in the Goupitan hydroelectric power station, China, are identified. The results show that the algorithm is feasible for rock mechanics use and has a useful ability in finding potential models. The algorithm enables the identification of models and parameters simultaneously and provides a new method for studying the mechanical characteristics of visco-elastic rocks.     

Optimization of laccase mediated biodegradation of 2,4-dichlorophenol using genetic algorithm

The present investigation focuses on the development of an effective strategy to determine the optimum environmental conditions leading to the maximum rate of biodegradation of 2,4-DCP by coupling response surface methodology (RSM) with a developed genetic algorithm (GA) thereby ensuring minimum contact time. RSM is utilized to create an efficient analytical model for biodegradation of 2,4-DCP in terms of environmental parameters: pH, temperature, enzyme activity and time of incubation. For this purpose, a number of degradation experiments based on statistical three-level Box Behnken design methods were carried out. An effective response surface (RS) model is developed by carrying out experiments designed using the Box Behnken method. The RS model thus developed is further interfaced with the GA to optimize the degradation conditions for optimum degradation with minimum contact time. The GA increases the biodegradation conditions to >99% within a time period of 8 h within the given range of experimental conditions. The conditions obtained from GA were verified experimentally.     

Multi-Objective Optimization of aluminum hollow tubes for vehicle crash energy absorption using a genetic algorithm and neural networks

A numerical study of the crushing of thin-walled circular aluminum tubes has been carried out to investigate their behaviors under axial impact loading. These kinds of tubes are usually used in automobile and train structures to absorb the impact energy. A Multi-Objective Optimization of circular aluminum tubes undergoing axial compressive loading for vehicle crash energy absorption is performed for five crushing parameters using the weighted summation method. To improve the accuracy of the optimization process, artificial neural networks are used to reproduce the behavior of the crushing parameters in crush dynamics conditions. An explicit finite element method (FEM) is used to model and analyzed the behavior. A series of aluminum cylindrical tubes are simulated under axial impact condition for the experimental validation of the numerical solutions. A finite element code, capable of evaluating parameters crush, is prepared of which the outputs are used for training and testing the developed neural networks. In order to find the optimal solution, a genetic algorithm is implemented. With the purpose of illustrating optimum dimensional ratios, numerical results are presented for thin-walled circular aluminum AA6060-T5 and AA6060-T4 tubes. Multi-Objective Optimization of circular aluminum tubes has been performed in the basis of different priorities to create the ability for designer to select the optimum dimension ratio. Also, crush parameters of two aluminum alloys has been compared.     

Optimizing project selection and scheduling problems with time-dependent resource constraints

This study presents an optimization model using constraint programming (CP) for project selection and scheduling problems with time-dependent resource constraints. A generic model is proposed to maximize the total profit of selected projects for construction and R&D departments given scheduling problems with various resource constraints during specified time intervals, including consumed and renewable resource limitations. Due to different periodical procurement strategies and annual budget concerns, this research considers various practical limitations for scheduling and allocating resources, such as budget limitations and resource constraints. For additional practicality, the optimization model integrates a project selection mechanism, scheduling precedence, and relationships between projects. To illustrate the model capabilities for solving project selection and scheduling problems, the current study presents two scenarios for maximizing profit, including fifteen candidate projects with time-dependent resource constraints. Analysis results demonstrate that the proposed model allows planners to determine an optimal portfolio with specified resource constraints according to various time intervals, and benefits decision-making for project selection and scheduling.     

用遗传算法优化钢截面探讨

介绍了建立钢结构截面优化设计数学模型，从个体编码、初始群体适应度函数的构造、复制、变异、群体重组、终止条件的几方面对遗传算法步骤进行了分析，最终实现对结构的优化设计。     

Finite element model updating on small-scale bridge model using the hybrid genetic algorithm

Finite element (FE) model-based dynamic analysis has been widely used to predict the dynamic characteristics of civil structures. FE model updating method based on the hybrid genetic algorithm, by combining genetic algorithm and the modified Nelder–Mead's simplex method, is presented to improve bridge structures' FE model. An objective function is formulated as a linear combination of fitness functions on natural frequencies, mode shapes and static deflections using measurements and analytical results to update both stiffness and mass simultaneously. A commercial FE analysis tool, which can utilise previously developed element library and solution algorithms, is adopted for applications on diversified and complex structures. The validity of the proposed method is verified by using a simply supported bridge model with three I-shaped girders. FE models such as grid, beam-shell and shell model are considered to modify initial FE models on the experimental structure. Experimental results suggest that the proposed method can be applied efficiently to various FE models and is feasible and effective when this method is applied to identify FE modelling errors.     

Evaluation of maximum allowable capacity of distributed generations connected to a distribution grid by dual genetic algorithm

This paper proposes a dual genetic algorithm based approach to evaluate the maximum allowable capacity of distributed generations (DGs) connected to a distribution grid. The uncertainties in the existing deterministic approaches for evaluating the steady-state voltage deviation due to distributed generation are discussed as well. Nowadays, deterministic approaches are widely adopted by those who propose the interconnection of DGs. However, the existing deterministic approaches overlook some operation conditions that may give rise to an incorrect result and lead to a wrong decision in practical applications. In this paper, various factors affecting steady-state voltage deviation are discussed first. Then, a maximum allowable DG capacity evaluation approach based on the dual genetic algorithm is proposed. Finally, the uncertainties of the existing deterministic approaches are discussed. It is intended as reference for utility engineers processing DG interconnection applications.     

Building energy system optimizations with utilization of waste heat from cogenerations by means of genetic algorithm

Distributed energy systems based on cogeneration offer significant potential to save energy since they effectively utilize waste heat from power generators. However, unless there is an appropriate combination of machinery and operations, the planned performance cannot be achieved. Thus, it is quite difficult to determine the optimal combination of machinery and operations. For this, an optimal design approach is needed. In this study, a new optimal design method for building energy systems is proposed. There are an enormous variety of combinations with regard to energy supply and demand. This method designs the most efficient energy system by optimizing the operation of available systems with consideration for the optimal capacity of machinery in the systems. Optimization algorithms known as “genetic algorithms” (GAs) with the capacity to deal with non-linear optimization problems have been adopted in this optimization analysis. In this study, a single-building energy system is evaluated. The result shows that the proposed method is sufficiently capable of optimizing the design, and has the potential to be applied to very complex energy systems with appropriate improvements.     

Optimal design of hysteretic dampers connecting adjacent structures using multi-objective genetic algorithm and stochastic linearization method

An optimal design method is proposed for nonlinear hysteretic dampers that enhance the seismic performance of two adjacent structures. The proposed method employs nonlinear random vibration analyses by use of a stochastic linearization method in order to efficiently estimate the stochastic responses of coupled buildings without performing numerous nonlinear time-history analyses. The main objectives of the optimal design are not only to reduce the seismic responses but also to minimize the total cost of the damper system. To deal with such conflicting objectives, a multi-objective genetic algorithm is adopted. This approach systematically obtains a set of Pareto optimal solutions that are non-inferior or non-superior to each other. The process for choosing a reasonable design from the optimal surface of Pareto solutions is also discussed. As an example of a nonlinear hysteretic damping device, this study considers passive-type magneto-rheological dampers with fixed input voltages. The optimal voltages and numbers of installed dampers are simultaneously determined. The robustness of the optimal design against uncertain characteristics of ground motions is examined through extensive nonlinear random vibration analyses.     

HVAC system optimization with CO2 concentration control using genetic algorithms

This study describes the use of genetic algorithms (GAs) for operating standard HVAC systems (HVAC—heating, ventilation and air conditioning) in order to optimize performance, primarily with regard to power saving. Genetic algorithms were introduced as an instrument for solving optimization problems. Analytic optimization procedures are widely used in other fields of engineering, but they are difficult to operate within HVAC systems, because the range of the research is usually too broad, the problems are not linear but rather discontinuous, and they mostly have complex limitations. This is why for this type of system genetic algorithms are used, since they have the qualities of robustness and efficiency that are crucial for finding the optimal solution. A simulation is conducted in order to demonstrate how much power can be saved by using the suggested method of CO₂ concentration control in a standard HVAC system. In addition to Matlab Simulink, the suggested method is verified with Energy software.     

基于试验模态参数的索-拱铁路桥的改进有限元模型

介绍基于模态参数的索-拱铁路桥的校准数值模型。基于扩大的频域分解法,采用只输出技术,通过扰动测试来确定自振频率,桥梁振动的整体和局部模态对应的振型及阻尼系数。采用一种遗传算法对数值模型进行改进,该算法可以在数值模型的15个参数中得到最优解。为了与模态匹配,采用了一种基于模态应变能计算的新技术。考虑到不同的初始种群,大量参数的稳定性证明了数值模型在优化范围内所采用优化算法的鲁棒性。通过一个混凝土的变形模量的特征试验和铁路运输下的动力试验,对改进的数值模型进行了验证。结果显示数值模拟结果与试验结果吻合较好。