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This paper constructs four integrated power generation systems using liquefied natural gas (LNG) cold energy and geothermal energy. These systems contain a geothermal single flash cycle, LNG expansion process and organic Rankine cycle (ORC). Different mathematical models are established to simulate the systems under steady-state conditions respectively. The influences of key working parameters on energy efficiency, exergy efficiency and levelized cost of energy (LCOE) are analyzed, and the comprehensive performances are estimated. The results show that increasing separator pressure, LNG turbine and Rankine turbine inlet pressures could increase the energy and exergy efficiencies. Also, a higher LNG turbine outlet pressure leads to a higher LCOE. To achieve the best system performance, single- and multi-objective genetic algorithm (GA) based optimizations are performed respectively. By single-objective optimization, GSF-TSORC and GSF-TPORC can reach the highest energy and exergy efficiencies of 34.80% and 52.61% respectively, and GSFC has the lowest total cost rate of 0.326 M$·year−1. After using multi-objective optimization, the energy and exergy efficiencies of GSF-TPORC are 29.56% and 51.29% respectively, which are superior to the other three systems, from the thermodynamic point of view. From an economic perspective, GSF-ORC has the lowest LCOE of 0.028 $·kWh−1, implying that it is the best system. [ABSTRACT FROM AUTHOR]

• A wavefront coding annular folded lens is presented in this paper which can achieve athermalization within the 100 °C range. • The relationship between obscuration radio and phase plate parameter is derived, which can be seen from the mathematical model that increased obscuration radio reduces PSF consistency. • A synthesized PSF is constructed using the genetic algorithm. High-quality image restoration over a wide temperature range with a single filter is enabled by the synthesized PSF. The annular folded lens (AFL) achieves miniaturization and integration of optical systems by single-element design. When over a wide temperature range, the AFL cannot be athermalized with one substrate material. In order to reduce the impact of temperature on imaging quality of the AFL, wavefront coding (WFC) technology is introduced. The WFC-AFL design principle is studied, the relationship between the obstruction ratio and the phase plate parameter is deduced, and the model of the obstruction ratio and the point spread function (PSF) consistency is established. In the digital decoding section, the image restoration effect of the PSF under different weights is studied, and the model of synthesized PSF is constructed using the genetic algorithm (GA). According to theory analysis, the infrared WFC-AFL with IRG26 is designed in the long-wave infrared 8 ∼ 12 μ m. The focal length is 50 mm, the total length is 18 mm, the F-number is 1 and the full field of view is 11° Image restoration with a single filter is achieved by using the synthetic PSF. For the restored images at -40 °C and 60 °C, the average SSIM values are improved by 0.0397 and 0.0141 respectively, the average PSNR values are improved by 1.9540db and 1.1077db respectively compared with common methods. The design provides a new idea for the athermalization of integrated and miniaturized optical systems. [ABSTRACT FROM AUTHOR]

[Display omitted] • A Simplified anaerobic co-digestion model is used for multi-objective optimization purpose. • A set methane yield is achieved while reducing total ammonia and maximizing profitability. • Genetic constrained algorithm ensures feasible solutions and rapid computations. • The optimal feed recipes ensure stable operations and enhance profits. Recently, numerous experimental studies have been undertaken to understand the interactions between different feedstocks in anaerobic digestion. They have unveiled the potential of blending substrates in the process. Nevertheless, these experiments are time-intensive, prompting the exploration of various optimization approaches. Notably, genetic algorithms have gained interest due to their population-based structures allowing them to efficiently yield multiple Pareto-optimal solutions in a single run. This study uses a simplified static anaerobic co-digestion model as the fitness function for a multi-objective optimization. The optimization aims to achieve a methane production set-point while reducing the output ammonia nitrogen and increasing the recipe' profitability. Thus, the study employs genetic algorithms to identify Pareto fronts and constraints confined the solution space within feasible boundaries. It also underscores the influence of economic considerations on the viable solution space. Ultimately, the optimal feed recipe not only ensures stable operations within the digester but also enhances associated profits. [ABSTRACT FROM AUTHOR]

This paper presents a systematic methodology to optimise the geometry of a three-body hinge-barge wave energy converter, to maximise the energy extraction of the device in given sea states and in site-specific wave climates. To that end, a 5-degree-of-freedom mathematical model is proposed to describe the system dynamics in two-dimensional space and a two-layer optimisation is designed to find the optimal design and control variables. The inner-layer optimisation is used to find the optimal control parameters of the power take-off system and the outer-layer optimisation, which uses a genetic algorithm, is employed to find the optimal design parameters of barge lengths and of optimal ballast positioning. In the case study, this methodology is applied to a 1:20 scale prototype of the McCabe Wave Pump device. Numerical results indicate that the optimal dimensions of the device, under given sea states, can be found efficiently and accurately, and that there appears to be no obvious benefit in the use of three barges, over a two-barge system. • Geometric optimisation of a 3-barge hinge barge device (no comparable work to date). •Combined optimisation of geometry AND optimal positions for ballast masses. •2-level optimisation, updating the control for each geometry/ballast specification. •The analysis points to an optimal 2-barge configuration. [ABSTRACT FROM AUTHOR]

• A systematic approach has been developed to design multilayer standby mechanisms. • A superstructure is proposed for standby mechanisms under varying loads. • A generalized event tree is presented to enumerate all loss-causing scenarios. • A Matlab code has been developed for optimization via genetic algorithm. • Optimal standby configurations and maintenance policies can be obtained. In modern chemical plants, there is a class of continuous processes operated under varying loads. In these processes, the standby mechanisms are needed to ensure that the downstream demand is always satisfied during the operations. Although a few related studies have been reported in literature, a comprehensive analysis of multilayer standby mechanisms in processes under varying loads has not been carried out. In this paper, a generalized mathematical model is developed to automatically generate the optimal standby mechanisms for any given processes by minimizing the total expected lifecycle expenditure. A Matlab code has been developed to perform these optimization tasks via genetic algorithm. The feasibility and effectiveness of the proposed model are demonstrated with the case study concerning a fan system for providing instrument air in a typical chemical plant. From the optimization results, one can determine the optimum configurations of the standby mechanisms, which include: (1) the number of protection layers, (2) the number of both online and spare sensors in the measurement channels, (3) the corresponding voting-gate logic in each channel, (4) the inspection intervals of switch and (5) the number of spares for switch. [ABSTRACT FROM AUTHOR]

In recent decades, research on supply chain management (SCM) has enabled companies to improve their environmental, social, and economic performance. This paper presents an industrial application of logistics that can be classified as an inventory-route problem. The problem consists of assigning orders to the available warehouses. The orders are composed of items that must be loaded within a week. The warehouses provide an inventory of the number of items available for each day of the week, so the objective is to minimize the total transportation costs and the costs of producing extra stock to satisfy the weekly demand. To solve this problem a formal mathematical model is proposed. Then a hybrid approach that involves two metaheuristics: a greedy randomized adaptive search procedure (GRASP) and a genetic algorithm (GA) is proposed. Additionally, a meta-learning tuning method is incorporated into our hybridized approach, which yields better results but with a longer computation time. Thus, the trade-off of using it is analyzed. An extensive evaluation was carried out over realistic instances provided by an industrial partner. The proposed technique was evaluated and compared with several complete and incomplete solvers from the state of the art (CP Optimizer, Yuck, OR-Tools, etc.). The results showed that our hybrid metaheuristic outperformed the behavior of these well-known solvers, mainly in large-scale instances (2000 orders per week). This hybrid algorithm provides the company with a powerful tool to solve its supply chain management problem, delivering significant economic benefits every week. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) are economic, have good maneuverability, and can perform some complex and high-risk tasks such as maritime supervision, leading to reduced costs and casualties. In this study, we investigate the application of USVs in maritime patrols, considering the endurance constraints of the USVs and the time constraints. Additionally, we establishes a mathematical model for optimizing the patrol path of multiple USVs under the endurance and time constraints. Considering the complexity of the model, the logistic map is introduced into the immune algorithm (IA) to improve the optimization. Then according to the relevant constraints, the corresponding coding and decoding methods are designed. Finally, the mutation and crossover operators of the genetic algorithm (GA) are combined with the designed IA to improve the diversity of antibodies in the iterative process, thereby improving the optimization ability of the algorithm. In the final numerical simulation experiment verification, the improved IA with GA (IA–GA) is compared with the traditional IA, GA, PSO-GA (Particle Swarm and Genetic hybrid algorithm) and IA–GA-greedy (IA–GA with greedy attribute) through simulations and case analysis. The results show that the improved IA can find a better solution than other algorithms in a reasonable time, which proves the superiority of the proposed algorithm. • The endurance of the USVs are taken into consideration. • A novel encoding method based on endurance and task time constraints is proposed. • Logistic map and genetic algorithm are incorporated into the immune algorithm. [ABSTRACT FROM AUTHOR]

• We treated evolvable dynamical systems from the viewpoint of information transmission. • Evolved dynamical systems were classified into three categories: passive transmission, excitable, and oscillatory systems. • We observed that some types of activity of optimally evolved dynamical systems showed neuron-like activity. • We confirmed these phenomena in various varieties of the network structure. We attempted to create a mathematical model for neuronal differentiation. The present study was performed within the framework of self-organization with constraints by looking for an optimized informational unit. We treated networks of individual dynamical system units with an external input, which was provided by coupled one-dimensional maps with possible forms of unidirectionally feed-forward network, random network, small-world network, and fully-connected network. We used a genetic algorithm to maximize the information transmission for each type of network. Optimized maps were obtained depending on the coupling strength and network structure. These maps can be classified into three types: passive, excitable, and oscillatory. In particular, the excitable and oscillatory types of dynamical systems possess characteristics that are quite similar to those of neurons, whereas the passive and oscillatory types of dynamical system may represent glial cells. [ABSTRACT FROM AUTHOR]

• The optimization of 10 important design parameters of a GCPV plant is introduced. • The IRR objective function includes both technical and economic aspects. • Genetic algorithm is suitable and effective to find the global optimum. • Optimal configurations are presented for six locations worldwide. • The effects of decreasing PV module costs on optimal plant design are analysed. The increasing penetration of photovoltaic (PV) technology calls for the development of an effective method for optimization of grid-connected photovoltaic power plants. This paper presents a simultaneous optimization method of ten important design parameters of a PV plant, including the module power, inverter sizing, support structure dimensions, cable losses, module orientation and row spacing. A mathematical PV performance model taking into account the important effects and losses and an economic cost model were developed and presented in detail. The objective function is the internal rate of return and the optimization is performed by a genetic algorithm. The results show that the proposed models and method are capable to optimize the grid-connected PV plant and provide reliable results after a 6–7 min calculation time. The method was demonstrated in detail for a Hungarian location, including the losses and cost structure of the optimal plant configuration. The optimization was also performed for 5 additional sites around the world to assess the effect of location and meteorology. The impact of the decreasing PV module prices on the optimal design is calculated to identify the expected future trends in PV plant design. The presented optimization method can be utilized to facilitate the optimal design of commercial PV plants and for research purposes. [ABSTRACT FROM AUTHOR]

This paper presents a multi-satellite scheduling problem for very large area observation with given specific constraints, derived from satellite capacity and customer requests. It is assumed that the profit was proportional to the coverage of acquired area, and the objective is therefore to maximize the total profits of generated observation schedule. To address the satellite scheduling problem, we first demonstrate a detailed problem description and then transform the problem into set covering problem within several criteria and constraints. Based on that, a mathematical model is established. In order to solve the multi-satellite scheduling problem for large area observation, a new solving framework is proposed. The framework is composed of three phases. In the discretizing phase, an area discretization method is adopted to establish the evaluation system. In the target decomposing phase, area target is decomposed into strips and corresponding visible time windows are calculated. In the scheduling phase, with crossover, mutation and feasibility operators, a genetic algorithm is introduced to generate an optimal observation schedule. Through extensive computational experiments on realistically generated problem with Chinese satellite platform, the effectiveness and reliability of the proposed solving framework are verified. • Multi-satellite scheduling problem for very large area observation is introduced. • The proposed problem is transformed and modeled as a set covering problem with constraints. • A new three-phase solving method is developed, including discretizing, decomposing and scheduling. [ABSTRACT FROM AUTHOR]

Plane wave theory has been used in predicting the acoustical performance of one-chamber straight mufflers. But, the acoustical effect of the mufflers at higher frequencies that are beyond the threshold of the cut-off frequency has been neglected. In addition, there is a need for a more compact muffler design for use inside a space-constrained working area. In order to improve the acoustical performance of straight mufflers, a space-constrained multi-chamber muffler using a more accurate predicting model in conjunction with an optimizer is proposed. In this paper, an eigen function that uses a three dimensional wave propagation to deduce the acoustical field as a four-pole matrix form is applied. An overall four-pole system matrix that multiplies individual matrices is adopted in evaluating the Transmission Loss (TL) for the hybrid mufflers. A circular and three-chamber straight muffler is introduced and optimized for various targeted tones using both Genetic Algorithm (GA) and Simulated Annealing (SA) Methods in conjunction with five objective functions (case A–case E). Before the optimization process is carried out, an accuracy check of the mathematical models for the circular one-chamber muffler is carried out. In order to verify the reliability of the GA and SA methods, a single-objective optimization of the three-chamber straight muffler at a targeted tone of 2000 Hz using GA method and SA method has been executed. Results reveal that the TL can be maximized at the targeted frequency when using either the GA method or the SA method. In addition, the noise reduction will increase if the number of chambers increases. Moreover, the acoustical performance of the mufflers will be reversely proportional to the diameter of the inlet/outlet tubes. Consequently, results reveal that the TL of the circular hybrid mufflers at targeted frequencies can be simultaneously improved by using the fourth and fifth strategies of objective functions in which the inverse of the summation summing up the individual target frequency's deviation square between the targeted transmission loss and the predicted transmission loss has been built. [ABSTRACT FROM AUTHOR]

• The improved Stribeck model is used to establish the non-linear friction model of a electro-hydraulic system. • The improved genetic algorithm is used to identify four unknown parameters in the friction model. • A dynamic friction feedforward compensation method is designed based on the velocity observer and the structural invariance principle. • Effectiveness of the friction model and the friction feedforward compensation method is validated by experiment. It is necessary to identify and compensate the non-linear friction to improve the performances of the electro-hydraulic system of robotic excavators. The mathematical model of the electro-hydraulic system is established first, and the non-linear friction are described by the improved Stribeck model. The actual friction force is calculated by measuring the pressures of the two chambers of the hydraulic cylinder at different constant velocities. The fitness, crossover and mutation probabilities of the genetic algorithm are improved to overcome the shortcomings of stagnation and premature convergence. Four unknown parameters in the friction model are identified and verified by the improved genetic algorithm. According to the displacement signal from the sensor, a velocity observer is used to estimate the velocity signal in real time. Based on the principle of structural invariance, a dynamic friction feedforward compensation method is designed. On this basis, the experiments of friction compensation (FC) under three working conditions of sinusoidal trajectory, low and high velocities tracking were carried out for the arm electro-hydraulic system. The results show that the proposed Stribeck model and friction compensation control method effectively eliminates the low velocity crawling and amplitude flattening phenomena, and improves the control performances of the system. [ABSTRACT FROM AUTHOR]

• Bike repositioning problem with service requirements for rental and return requests was studied. • A mathematical model with the goal of minimizing the travel and unsatisfied costs was developed. • An efficient two-phase heuristic approach based on linear programming was proposed. • The proposed approach performs better than CPLEX optimizer and a genetic algorithm-based method. • The effects of various system parameters on the system were also investigated. An approach to overcome the bike imbalance problem is to transfer excess bikes to branches with bike shortages. This study develops a constrained mathematical model to deal with a multi-vehicle bike-repositioning problem, and aims to minimize the sum of transportation and unmet demand costs over a planning horizon through bike-transfer strategies under a minimum service requirement. A two-phase heuristic based on linear programming was proposed to solve the problem and produce compromising solutions. In the first phase, the paper developed a linear programming model to quickly develop decisions related to bike inventory, unloading, and loading for all stations for each time slot. In the second phase, this paper proposed an iterative approach through two parameter sensitive mathematical models to sequentially reduce the problem scale to develop decisions related to bike transfers. Computational results show that the proposed approach is superior to a CPLEX optimizer and a hybrid heuristic based on a genetic algorithm. The proposed approach was used to analyze the bicycle system in Taiwan. The impacts of various system parameters on the system were also investigated. [ABSTRACT FROM AUTHOR]

• Halal food and Halal logistics regulation are reflected into quantitative elements. • The effect from Haram facilities to the Halal food logistics network is considered. • Mathematical model is developed to minimize the total network development cost. • Devised hybrid genetic algorithm can find an efficient solution within short time. • Numerical experiment and sensitivity tests are designed and executed. This paper investigates the optimal location and allocation of the Halal food logistics network, which include farms, butcheries and food plants that follow strict Islamic food regulations. A mathematical model is developed by reflecting the chain effect from Haram facilities and the solution procedures, which use CPLEX and hybrid genetic algorithm, are suggested to derive an optimal design for Halal food logistics network. The numerical experiment shows that the overall Halal food logistics network is well developed according to Halal regulations. It is observed that the chain effect from Haram facilities is more intensified when the permitted distance is extended. [ABSTRACT FROM AUTHOR]

• The article proposes a new mathematical model of the inductive displacement sensor. • The model uses composite functions and a multi-objective optimization algorithm to design the coil structure parameters of the sensor. • The model aims to improve the sensor's linearity and sensitivity. • The new design method is verified by numerical simulation and physical experiments. This paper proposes a novel mathematical model that has high accuracy based on a multi-objective optimization algorithm for the inductive displacement sensors, which is popular in industrial production due to their simple design and reliable performance. The proposed model uses composite functions to establish the structural parameters of the coil winding. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve a series of non-dominated problems related to the coil structure parameters with the goal of achieving superior sensor performance. Density clustering sorting is used to select the desired non-dominated solutions. The designed sensor has a nonlinearity of 0.16%, and numerical simulations and physical experiments confirm the effectiveness of the new design method. [ABSTRACT FROM AUTHOR]

This research proposed a multi-objective optimization approach that combines Non-dominated Sorting Genetic Algorithms (NSGA) Ⅲ and support vector machine (SVM) to reduce diesel engine emissions while enhancing economic performance and calibration efficiency. In order to obtain accurate experimental data on diesel engines, a space-filling design method was proposed based on the prediction modeling of diesel engine performance. The SVM prediction model for diesel engine performance was established. A genetic algorithm (GA) was introduced to optimize the SVM model's penalty factor and radial basis parameters, thereby improving its prediction accuracy. The multi-objective optimization approach optimized the braking specific fuel consumption (BSFC), NO x , and CO. The results show that: the GA-SVM diesel engine performance prediction model has excellent prediction performance and generalization ability for BSFC, NO x , and CO, with R2 values of 0.981, 0.979, and 0.968, respectively. GA-SVM was used to evaluate the fitness of the NSGA-III optimal set. This not only ensures optimization accuracy but also improves working efficiency. After optimization, the BSFC of the diesel engine was reduced by 1.67%, NO x emission was reduced by 27.01%, CO emission was reduced by 19.15%, and noticeable optimization results were obtained. This work has important reference value for the automatic calibration of diesel engine control parameters, improving the economy and emission of diesel engines. • A design method based on diesel engine performance is proposed. • Diesel engine performance is accurately predicted by GA-SVM. • The fuel MAPs of engine were optimized based on NSGA-Ⅲ and GA-SVM. • The reduced rates of BSFC, NO x , and CO reach 1.67%, 27.01%, 19.15%. [ABSTRACT FROM AUTHOR]

The hub location is a major problem in the systems with high dependence on the transportation of goods, information, and passengers between the constituent parts. The present study is aimed to introduce a multi-commodity multi-model hierarchical hub location problem with uncertain demand. Thus, it is estimated by intuitionistic fuzzy variables. The objective function in the present problem is to minimize the total transportation costs in the network to determine the optimal hub location, allocate non-hub nodes to the hubs and the ground hubs to the air hubs, and identify the type of vehicles required in each route. The model has a hierarchical structure consisting of a three-echelon network (i.e., central hubs, non-central hubs, and demand nodes) connected in the star-tour-star form. A four-index mathematical model was presented and solved by GAMS optimization software. The results indicated that the objective function (cost) has a descending trend with increasing the number of hubs which can be assigned to the decline in the transportation costs. The costs increased by a decrease in the maximum delivery time which could be due to the selection method of the transportation mode. In the multi-product case, the costs declined by increasing the number of hubs as these hubs decremented the network complexity. Regarding the NP-Hard nature of the hub problems, a genetic meta-heuristic algorithm was proposed in the MATLAB software to solve the large-scale problem. According to the results, this algorithm offered close-optimal solutions. Noteworthy, this study employed known AP datasets. [ABSTRACT FROM AUTHOR]

Optimal selection of safety measures (SMs) is a challenging task for safety managers due to its importance, complexity, and incapability of traditional approaches in considering all the aspects of SMs optimal selection. Sophisticated mathematical models can be used to overcome the limitations of traditional approaches. However, setting the objective functions while considering their priorities as well as possible synergistic effects of the SMs on the hazards are still among the main concerns in the development and application of mathematical models. The present study is aimed at developing a methodology to optimize the SMs selection while addressing the aforementioned challenges and considering both the budget and the risks. To do so, first the Pareto set of the solutions is obtained by NSGA-II technique - a multi-objective genetic algorithm technique - where a lexicographic model is used to select the optimal solution from the Pareto set based on the priority of the objective functions. A pessimistic strategy is used to account for the synergistic effects and the overlaps between the selected SMs. Two mathematical models are developed to represent different policies in optimal SMs selection in a gas wellhead and surface facility. The results show a notable difference between the two policies, indicating the importance of setting proper objective functions in multi-objective optimization problems. The results also show that the methodology is able to effectively satisfy different safety management policies and constraints with no need for much extra information except the cost and impact of SMs on the hazards' risk. [ABSTRACT FROM AUTHOR]

Variable Selection in large datasets is a commonly procedure in multivariate calibration, which is a field of study from chemometrics. Selecting the most informative variables becomes an important step to build mathematical models through statistical techniques in order to predict some property of interest from an analyzed sample. Recombination-based search methods such as Genetic Algorithms (GAs) have been widely used as variable selection techniques to solve several optimization problems. However, previous works from literature have emphasized the schemata disruption problem caused by genetic operators. Therefore, this paper proposes two versions of an epistasis-based implementation (EbFSA) as a novel approach for variable selection in multivariate calibration problems, where each version is deterministic and performs a different strategy. The use of epistasis concepts becomes important to assess the genes (variables) interdependence. Based on our experimental results, we are able to claim EbFSA can select the most informative variables and overcome some state-of-the-art algorithms. • A novel approach for variable selection in multivariate calibration problems is presented. • We present two versions of an Epistasis-based implementation (EbFSA). • A comparison with traditional algorithms is performed. • We obtained advantages by reducing the number of variable as well as the prediction error. • We experimentally demonstrated that EbFSA can be a good choice for the variable selection problem. [ABSTRACT FROM AUTHOR]

Abstract In this study, we extend the traveling repairman problem with profits (TRPP) by considering the use of multiple vehicles, which occurs commonly in practice. We call the resulting problem the multiple traveling repairman problem with profits (MTRPP). In MTRPP, a fleet of identical vehicles is dispatched to visit a subset of nodes in order to collect time-dependent profits. To solve MTRPP, we present a highly effective memetic algorithm, which combines (i) a randomized greedy construction method for initial solution generation, (ii) a variable neighborhood search for local refinement, and (iii) a dedicated route-based crossover operator for solution recombination. Computational experiments performed on a large set of instances reveal the efficiency of the algorithm. Additionally, we demonstrate the relevance of our algorithm for the classic TRPP. The reported computational results serve as a benchmark for future researchers who wish to investigate the problem. Highlights • The multiple traveling repairman problem with profits (MTRPP) is introduced. • A mathematical model of MTRPP is formulated. • A memetic algorithm (MA) is proposed for solving MTRPP. • Computational study shows that the proposed MA effectively solves MTRPP. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Error backpropagation algorithm is used to built general mathematical model. • Computational model is established based on general mathematical model. • Computational model shows good reliability and predictive ability. • Relations between reaction parameters and performance are studied. • Optimum reaction parameters are obtained by using genetic algorithm. Abstract To optimise methanol steam reforming performance of cylindrical microreactor for hydrogen production, an error backpropagation algorithm was used to build a mathematical model for reaction performance of different microreactors for hydrogen production. Additionally, a genetic algorithm (GA) was utilised to process the computational model to obtain the optimum reaction parameters. The reliability of optimum reaction parameters of cylindrical microreactor for hydrogen production was verified by experiments. Firstly, take platemicroreactor as an example, the porosity of porous copper fiber sintered sheet (PCFSS), reaction temperature of methanol steam reforming for hydrogen production, injection velocity of the methanol and water mixture, and catalyst loading of PCFSS were considered as input data, whereas methanol conversion was used as output data. The computational model for specific testing system was gained by utilising input and output data from specific testing system to train the mathematical model for different microreactors, combining with matrix laboratory (MATLAB) neural network toolbox and designed MATLAB program. The E max of 5% for plate microreactor and E max of 3.2% for cylindrical microreactor verified the good predictive ability and reliability of the computational model for plate and cylindrical microreactor, indicating the reliability and universal applicability of the mathematical model for different microreactors. Secondly, the effects and mechanisms of PPI, reaction temperature, injection velocity, and catalyst loading on methanol conversion were studied, relying on the computational model. Finally, the optimum reaction parameters were acquired using GA, MATLAB neural network toolbox and designed MATLAB program. The validity of the optimum reaction parameters of cylindrical microreactor for hydrogen production was confirmed by experiments. This study provides a feasible method for methanol steam reforming performance optimisation for hydrogen production. [ABSTRACT FROM AUTHOR]

Abstract Recent studies propose that different dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TPs). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the development of a time series segmentation algorithm for paleoclimate data in order to find segments sharing common statistical patterns. The proposed algorithm uses a clustering-based approach for evaluating the solutions and six statistical features, most of which have been previously considered in the detection of early warning signals in paleoclimate TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series, together with a method to compare the segmentations. The final segments provided by the algorithm are used to construct a prediction model, whose promising results show the importance of segmentation for improving the understanding of a time series. [ABSTRACT FROM AUTHOR]

Abstract Building performance simulation (BPS) enables users to predict the demand reductions achieved by energy conservation measures (ECMs). Identifying an optimal set of ECMs in combination is complex due to interaction effects. This creates a combinatorial problem where every ECM combination needs be simulated to identify the optimum with certainty. To avoid the computational burden of running separate simulations for each ECM combination, approximate approaches for predicting the joint effects of ECMs based on single ECM simulations have been proposed in literature: linear-addition and log-addition of savings. These reduced-order approaches are very rapid compared to BPS, but their accuracy is not well characterized. This paper compares ECM energy savings estimated by BPS with the linear and log-additive approaches and a new reduced-order approach: log-additive decomposition. An existing library of energy models and ECMs (Hamilton, et al., 2014) representing Philadelphia medium-sized office buildings is utilized to compare the performance of each approach not only to each other but also to BPS. Overall, log-additive decomposition performs well (prediction error ∼10%) followed by log-addition (prediction error ∼20% to 30%), which outperform linear addition (prediction error often exceeding 50%). Compared to BPS, the computational cost of each is 0.018%, 0.014%, and 0.004% respectively. [ABSTRACT FROM AUTHOR]

Highlights • The extra loss caused by uncertainty is measured. • A MIP model under uncertainty is formulated considering a lot of uncertain scenarios. • A GA-based framework combined with three-stage algorithm is proposed. Abstract In a container terminal, the arriving times and handling volumes of the vessels are uncertain. The arriving times of the external trucks and the number of containers which are needed to be brought into or retrieved from a container terminal by external trucks within a period are also uncertain. Yard crane (YC) scheduling is under uncertainty. This paper addresses a YC scheduling problem with uncertainty of the task groups' arriving times and handling volumes. We do not only optimize the efficiency of YC operations, but also optimize the extra loss caused by uncertainty for reducing risk of adjusting schedule as the result of the task groups' arriving times and handling volumes deviating from their plan. A mathematical model is proposed for optimizing the total delay to the estimated ending time of all task groups without uncertainty and the extra loss under all uncertain scenarios. Furthermore, a GA-based framework combined with three-stage algorithm is proposed to solve the problem. Finally, the proposed mathematical model and approach are validated by numerical experiments. [ABSTRACT FROM AUTHOR]

Abstract Flowshop production is adopted as the major type of production of reinforced precast concrete components and it has higher requirements on shop floor schedules than other types, especially that from rescheduling. However, up to now, very few approach for the optimization of the shop floor rescheduling has been proposed in spite of its vital importance. This research proposes an approach for optimizing shop floor rescheduling of multiple production lines for flowshop production of reinforced precast concrete components. The approach comprehensively utilizes the over-assigned time, which is the difference value between the assigned production time and the estimated one of a production step for a precast component to deal with production emergencies. Meanwhile, it keeps the adjustment of schedules at minimum to avoid massive material re-dispatch. First of all, the optimization objectives and constraints of optimized shop floor rescheduling of multiple production lines for flowshop precast production are analyzed and a mathematic model is thus formulated. Then, the solver of the model is established by using genetic algorithm. Finally, the approach is validated by case studies. It is concluded that the approach contributes to the effective and efficient optimized rescheduling of multiple production lines for flowshop precast production. Highlights • A Rescheduling Model of Multiple production lines for flowshop precast production (RM-MFP) was established. • An optimized rescheduling approach was then proposed based on the RM-MEP by using Genetic Algorithm (GA). • The over-assigned time of multiple production steps can be used as a whole to cope with the emergencies. • the approach, the complexity and workload of material re-dispatching can be minimized during rescheduling. • A number of optimization constraints were identified for rescheduling of multiple production lines for precast production. [ABSTRACT FROM AUTHOR]

In order to meet the requirement of frequent start-stop of the units, it is significant to optimize the operation scheme of the unit. A novel optimized mathematical model of a steam turbine was proposed at a previous paper of this study’s authors. In this study the supported vector machine (SVM) stress model of a 320 MW steam turbine was established based on 13 groups of start-up schemes. According to the novel optimized mathematical model and SVM stress model of the turbine, Genetic algorithm-particle swarm optimization was employed to search for the best values of the steam temperature rise rate. The optimization schedule can shorten the start-up time of 170 min in the premise of meeting the stress requirement. It is necessary to testify the feasibility of the optimized schedule, the fatigue, creep, and creep-fatigue life of the steam turbine rotor were analyzed by continuous damage mechanics (CDM) model. The results shown that the life assessment of the rotor from the nonlinear CDM is accordance with the bilinear failure criterion of ASME Section III, and the critical creep-fatigue damage of the rotor is 0.2083, and the life loss of the rotor after working 30 years under the optimized start-up schedule is 19.25%. [ABSTRACT FROM AUTHOR]

Abstract In this paper, system identification by using real-coded genetic algorithm (RGA) with a reduced-order robust observer (RRO) for an induction motor (IM) is successfully proposed and realized. Because the rotor flux linkages of an IM are not easily measurable, the RRO is proposed to observe them. Different state errors including the measurable and observed states are used in the fitness functions, which are optimized by the RGA to find the unknown system's parameters. From numerical simulations and experimental results, unknown parameters of an IM are successfully identified by the RGA with state-error fitness functions. The contributions of this paper are: (i) the RRO is combined in the mathematical model of the IM to observe the un-measureable rotor flux linkages; (ii) The observer gain of the reduced-order observer is given as the unknown parameters, which is selected by the RGA approach to satisfy the linear matrix inequality (LMI) conditions; (iii) Three fitness functions are successfully proposed, compared and optimized by using the RGA. It is found that the one with more real state errors has the best identify performance than the other two; (iv) From numerical and experimental results, it can be concluded that the more system's states are measurable and used in the fitness function, the more system's parameters are accurately identified. [ABSTRACT FROM AUTHOR]

The heliostat field of solar power tower (SPT) system occupies a large proportion of both the total investment and total energy losses of a plant. However, the optimization design of a heliostat field is a challenging work, because there are too many parameters to be optimized. In this paper, a new high-dimensional genetic algorithm toolbox (HDGA) is developed in Visual Studio Community 2015 for the heliostat field design, in which a new crossover strategy is employed for the high-dimensional optimization. The algorithm is verified by both mathematical models and engineering cases, the results show that HDGA is more effective for the high-dimensional problem, and its convergence speed is much faster than that of the genetic algorithm toolbox developed by the University of Sheffield (Sheffield GA). The new algorithm is explained in detail and the optimal field layout is presented. With the new algorithm, a heliostat field referencing to the Gemasolar plant is optimized in this paper. The results show that the optical performance of the heliostat field is improved significantly than that of the un-optimized case, and the optical efficiency of 63.7% is reachable at the design point. At the same time, the annual insolation weighted efficiency is 56.9%. [ABSTRACT FROM AUTHOR]

Surrogate modeling, or metamodeling, is an efficient way of alleviating the high computational cost and complexity for iterative function evaluation in design optimization. Accuracy is significantly important because optimization algorithms rely heavily on the function response calculated by surrogate model and the optimum solution is directly affected by the quality of surrogate model. In this study, an optimized trend kriging model is proposed to improve the accuracy of the existing kriging models. Within the framework of the proposed model, regression analysis is carried out to approximate the unknown trend of the true function and to determine the order of the universal kriging model, which has a fixed form with a mean structure dependent on the order of model. In addition, the selection of an optimal basis function is conducted to separate the useful basis function terms from the full set of the basis function. The optimal subset of the basis function is selected with the global optimization algorithm; which can accurately represent the trend of true response surface. The mean structure of proposed model has been optimized to maximize the accuracy of kriging model depending on the trend of true function. Two- and three-dimensional analytic functions and a practical engineering problem are chosen to validate the proposed model. The results showed that the OTKG model yield the most accurate responses regardless of the number of initial sample points, and can conversed into well-trained model with few additional sample points. [ABSTRACT FROM AUTHOR]

The procedure of selecting an optimum number and best distribution of ground control information is important in order to reach accurate and robust registration results. This paper proposes a new general procedure based on Genetic Algorithm (GA) which is applicable for all kinds of features (point, line, and areal features). However, linear features due to their unique characteristics are of interest in this investigation. This method is called Optimum number of Well-Distributed ground control Information Selection (OWDIS) procedure. Using this method, a population of binary chromosomes is randomly initialized. The ones indicate the presence of a pair of conjugate lines as a GCL and zeros specify the absence. The chromosome length is considered equal to the number of all conjugate lines. For each chromosome, the unknown parameters of a proper mathematical model can be calculated using the selected GCLs (ones in each chromosome). Then, a limited number of Check Points (CPs) are used to evaluate the Root Mean Square Error (RMSE) of each chromosome as its fitness value. The procedure continues until reaching a stopping criterion. The number and position of ones in the best chromosome indicate the selected GCLs among all conjugate lines. To evaluate the proposed method, a GeoEye and an Ikonos Images are used over different areas of Iran. Comparing the obtained results by the proposed method in a traditional RFM with conventional methods that use all conjugate lines as GCLs shows five times the accuracy improvement (pixel level accuracy) as well as the strength of the proposed method. To prevent an over-parametrization error in a traditional RFM due to the selection of a high number of improper correlated terms, an optimized line-based RFM is also proposed. The results show the superiority of the combination of the proposed OWDIS method with an optimized line-based RFM in terms of increasing the accuracy to better than 0.7 pixel, reliability, and reducing systematic errors. These results also demonstrate the high potential of linear features as reliable control features to reach sub-pixel accuracy in registration applications. [ABSTRACT FROM AUTHOR]

Soft sensors have been widely used in industrial processes over the past two decades because they use easy-to-measure process variables to predict difficult-to-measure ones. Some success has been achieved by the dominant traditional methods of modeling soft sensors based on statistics, such as principal components analysis (PCA) and partial least square (PLS), but such sensors usually become inaccurate and inefficient when processing strong nonlinear data. In this paper, a new soft sensor modeling approach is proposed based on a deep learning network. First, stacked auto-encoders (SAEs) are employed to extract high-level feature representations of the input data. In the process of training each layer of a SAE, the Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) is adopted to optimize the weights parameters. Then, a support vector regression (SVR) is added to predict the target value on the basis of the features obtained from the SAE. To improve the model performance, Genetic Algorithm (GA) is used to obtain the optimal parameters of the SVR. To evaluate the proposed method, a soft sensor model for estimating the rotor deformation of air preheaters in a thermal power plant boiler is studied. The experimental results demonstrate that the soft sensor based on the SAE-SVR algorithm is more effective than the existing methods are. [ABSTRACT FROM AUTHOR]

In this paper, an approach for placement of voltage regulators (VRs) in traditional distribution systems by considering a local controller model is presented. The main aims of this paper are controlling the voltage level in its permitted range and decreasing the costs imposed to the distribution system companies, such as costs that stem from power losses, VRs’ investment and maintenance. Genetic algorithm (GA) has been used as a tool to determine the number, location and rated power of VRs. Since in traditional distribution systems, tap position determination of VRs is achieved by local controllers, local controller model is established to determine tap operations. A 70-bus distribution system is considered to prove the value of the presented approach. Effectiveness of the proposed approach and ineffectiveness and infeasibility of conventional approaches are presented in numerical studies. The presented approach allowed to eliminate voltage violation in all load conditions and a reduction of power losses of about 6% for the maximum load level. [ABSTRACT FROM AUTHOR]

In this paper, we propose an original double flexible job-shop scheduling problem (DFJSP), in which both workers and machines are flexible. Because of the characteristics of double flexibility, DFJSP conforms to practical production better than the flexible job-shop scheduling problem (FJSP). In addition, a multi-objective optimization mathematic model according to the DFJSP is proposed, which is concerned with the processing time indicator that is usually optimized by most existing studies; green production indicators, namely, factors regarding environmental protection; and human factor indicators, which are actual indispensable elements that exist in the production system. Furthermore, ten benchmarks of DFJSP are presented and solved using a newly proposed hybrid genetic algorithm (NHGA). With the proposed NHGA, a new well-designed three-layer chromosome encoding method and some effective crossover and mutation operators are developed. To obtain the best combination of key parameters in NHGA, the Taguchi design of experiment method is used for their evaluation. Finally, comparisons between NHGA and NSGA-II show that the proposed NHGA has advantages in terms of the solving accuracy and efficiency of the DFJSP, particularly at a large scale. It would be beneficial to apply our proposed model to the multi-objective optimization of scheduling problems, especially those considering human factor and green production-related indicators. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to examine the state-of-the-art research efforts linked with the development of fatigue life estimation models. The main objective is to identify new concepts for fatigue life estimation other than the classical models and their hybrids. Various techniques to estimate fatigue life have been identified, such as critical plane deviation, 5D deviatoric space enclosed surface, modified Wholer curve. However, the most notable one to be found is the application of evolutionary optimization algorithms for, e.g., genetic algorithms, artificial neural networking, and differential ant-stigmergy algorithms. Initially, a brief history of fatigue life estimation and modeling is presented. In subsequent sections, some familiar classical models are discussed, and then various innovative approaches to fatigue life prediction are reviewed. The survey is fairly detailed, and best efforts have been made to the net in as many new methodologies as possible. The review is organized to offer insight on how past research efforts have provided the groundwork for subsequent studies. [ABSTRACT FROM AUTHOR]

In this study, a new mobile p -hub location problem in a dynamic environment is proposed, where there are mobile facilities inside hub nodes that can be transferred to other nodes in the next period. Mobile facilities have a mobility feature and can be transferred to other nodes in order to meet demand. Using such facilities will save extra hub establishment and closing costs in networks. This approach can be used in some real-world applications with rapidly changing demand, such as mobile post offices or emergency medical service centers, because designing immobile hub networks may be less efficient. In addition, designing dynamic hub networks entails establishing and closing costs in different periods. The model also considers a mobility infrastructure of hub facilities. The numerical examples confirm that a mobile hub network is more efficient than an immobile hub network in a dynamic environment. The effect of different parameters on the model is analyzed to consider its applicability conditions. A genetic algorithm, along with tuned parameters and a simulated annealing algorithm, are proposed to solve the model in large instances. Proposing of a model considering mobility feature in the hub location networks, proving its efficiency and finally proposing a proper solution algorithm are main contributions of this study. The model and solutions algorithms were analyzed by more numerical instances using Australia post (AP) dataset. [ABSTRACT FROM AUTHOR]

Asymmetric acoustic wave propagation is important for control and manipulation of the acoustic wave signals in various devices. However, previous approach to find optimal asymmetric acoustic transmission (AAT) is through repeatedly adjusting the geometrical parameters, thus causing time-consuming. Here we propose a study on the multi-objective optimization of the AAT, aiming to achieve the widest working frequency range ( fr ) and the highest transmittance peak ( η ) with regard to the design variables. For this purpose, the Radial Basis Function (RBF) neural work and finite element (FE) method are applied to obtain the valuable data in the procedure. Furthermore, local sensitivity analysis of design parameters on the fr and η are analyzed. Ultimately, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adapted for getting the Pareto-optimal solutions. The optimization results show great improvement for the overall performance of the AAT, which could be potentially significant in designing various sound devices. [ABSTRACT FROM AUTHOR]

This paper proposes a bi-level model to solve the timetable design problem for an urban rail line. The upper level model aims at determining the headways between trains to minimize total passenger cost, which includes not only the usual perceived travel time cost, but also penalties during travel. With the headways given by the upper level model, passengers’ arrival times at their origin stops are determined by the lower level model, in which the cost-minimizing behavior of each passenger is taken into account. To make the model more realistic, explicit capacity constraints of individual trains are considered. With these constraints, passengers cannot board a full train, but wait in queues for the next coming train. A two-stage genetic algorithm incorporating the method of successive averages is introduced to solve the bi-level model. Two hypothetical examples and a real world case are employed to evaluate the effectiveness of the proposed bi-level model and algorithm. Results show that the bi-level model performs well in reducing total passenger cost, especially in reducing waiting time cost and penalties. And the section loading-rates of trains in the optimized timetable are more balanced than the even-headway timetable. The sensitivity analyses show that passenger’s desired arrival time interval at destination and crowding penalty factor have a high influence on the optimal solution. And with the dispersing of passengers' desired arrival time intervals or the increase of crowding penalty factor, the section loading-rates of trains become more balanced. [ABSTRACT FROM AUTHOR]

In this paper, a new approach is presented to find the optimum catalyst loading distribution along the flow field. The optimization is performed by integrating a computational fluid dynamic (CFD) model and genetic algorithm optimization method. The CFD model is two-dimensional, steady state and two-phase. Multiphase mixture model (M 2 ) is used to model two-phase transport in porous media of a Polymer Electrolyte Membrane Fuel Cell (PEMFC). Numerical domain includes channel, gas diffusion layer (GDL) and catalyst layer (CL) in the cathode side. In the next step, current density is assumed to be proportional with catalyst loading. Catalyst loading is considered as polynomial functions with unknown coefficients. Genetic algorithm optimization method is applied to find the unknown coefficients and as a result the optimum catalyst loading function along the flow field. The results indicate that catalyst loading distribution has a significant effect on the fuel cell performance and it is seen that in the optimum case, maximum PEMFC power density is increased by about 14%. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective optimal operation of meshed AC/DC power grids including multi-terminal voltage-source-converter-based high-voltage direct current (VSC-MTDC) systems. The proposed approach is modeled as a corrective security-constrained optimal power flow (CSC-OPF) problem, with the minimization of both the operation cost and power loss as the objectives. Moreover, it provides a cost-effective solution to assist in decision-making, and improves the system security during operation. The N − 1 contingency security criterion is enforced for both AC and DC transmission networks, and corrective control is used to eliminate or alleviate post-contingency security violations. The corrective control actions used in this paper include not only secure operation control actions, but also economical post-contingency corrective control of the multi-terminal VSC-HVDC. To increase the computation speed, a contingency screening technique is applied to CSC-OPF by efficiently selecting the most severe case of the N − 1 contingency, as obtained using a voltage security index (VSI). The proposed approach uses the non-dominated sorting genetic algorithm (NSGA-II) to find multi-objective OPF solutions by checking the post-contingency state feasibility while taking into account post-contingency corrective actions. Simulation results confirm the validity and effectiveness of this approach. [ABSTRACT FROM AUTHOR]

In this study based on the Soave-Redlich-Kwong equation of state (SRK EOS), a new viscosity model for light hydrocarbons, CO 2 and N 2 has been proposed. The proposed model can be applied in both liquid and gas phases and in high and low ranges of pressure and temperature. The optimal values of the parameters in the proposed model are determined by using the Genetic algorithm and generalized in term of acentric factor. The performance of the proposed model has been compared with the performance of Peng-Robinson EOS-based viscosity model of Guo et al. (2001), the Peng-Robinson EOS-based viscosity model of Fan and Wang (2006) and the Lohrenz-Bray-Clark (LBC) method. In order to have a fair comparison, the binary interaction coefficients in the mixing rules are set to zero. The results of calculations for seventeen pure components (6449 data), thirty eight binary mixtures (2772 data), ten simple and twenty complex mixtures (74 data) show the superiority of the proposed model compared to the other viscosity models. [ABSTRACT FROM AUTHOR]

Multi-degree splines are piecewise polynomial functions having sections of different degrees. For these splines, we discuss the construction of a B-spline basis by means of integral recurrence relations, extending the class of multi-degree splines that can be derived by existing approaches. We then propose a new alternative method for constructing and evaluating the B-spline basis, based on the use of so-called transition functions. Using the transition functions we develop general algorithms for knot-insertion, degree elevation and conversion to Bézier form, essential tools for applications in geometric modeling. We present numerical examples and briefly discuss how the same idea can be used in order to construct geometrically continuous multi-degree splines. [ABSTRACT FROM AUTHOR]

Algal model based multi-objective optimization using elitist non-dominated sorting genetic algorithm with inheritance was carried out for batch cultivation of Dunaliella tertiolecta using NPK-fertilizer. Optimization problems involving two- and three-objective functions were solved simultaneously. The objective functions are: maximization of algae-biomass and lipid productivity with minimization of cultivation time and cost. Time variant light intensity and temperature including NPK-fertilizer, NaCl and NaHCO 3 loadings are the important decision variables. Algal model involving Monod/Andrews adsorption kinetics and Droop model with internal nutrient cell quota was used for optimization studies. Sets of non-dominated (equally good) Pareto optimal solutions were obtained for the problems studied. It was observed that time variant optimal light intensity and temperature trajectories, including optimum NPK fertilizer, NaCl and NaHCO 3 concentration has significant influence to improve biomass and lipid productivity under minimum cultivation time and cost. Proposed optimization studies may be helpful to implement the control strategy in scale-up operation. [ABSTRACT FROM AUTHOR]

This study investigates the combined Rankine power and the absorption cooling cycles. The working fluid used in this cycle is the binary liquid mixture of water and ammonia. It produces both refrigeration and power simultaneously via a single heat source. Parametric analysis has been adopted to evaluate the thermodynamic parameters effects on the operation of the combined cycle where the Engineering Equation Solver (EES) is utilized. The obtained results show that environmental temperatures, heat source, refrigeration, inlet pressure, and temperature, and the density of the ammonia-water dilution have major effects on the exergy efficiency, the refrigeration output, and the net power of the system. In order to obtain the maximum exergy and thermal efficiencies, the optimization of the combined cycle has been performed via the genetic algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. [ABSTRACT FROM AUTHOR]

In recent years, due to environmental concerns, remanufacturing of products is practiced in different companies and closed loop supply chain network in these companies is significant to optimize. Therefore, current study is aimed to determine an optimal closed loop supply chain network, which is composed of multiple producers, remanufacturers, intermediate centers and customer centers. Furthermore, uncertainty in the demand and uncertainty in the quantity of returned products is considered simultaneously in the network to make it significantly useful in the uncertain environment. A novel genetic artificial bee colony (GABC) algorithm is introduced with a new food source representation for the current problem. The proposed GABC algorithm considered neighbor food sources for local search and used crossover and mutation operations of genetic algorithm to enhance the exploration ability of the proposed algorithm. Taguchi method is employed to compute the optimal parameters of GABC for two different size test problems taken from literature and a Case problem which are modified according to the current research problem. The performance of presented GABC algorithm is tested by comparing the results of considered test problems with the results obtained from the original artificial bee colony (ABC) algorithm and genetic algorithm (GA). Moreover, to test the robustness of the proposed GABC algorithm, different scenarios of small and large size problems based on the quantity of demand and variations in demand are made to perform the experiments. Results indicate that proposed GABC outperforms standard ABC and GA in different scenarios to give smaller value of the total cost of network and gives more robust results to give smaller variations in the total cost of network due to uncertain variations in the demand, as compared to original ABC and GA.d [ABSTRACT FROM AUTHOR]

In order to save more energy and quicken the load change speed of air-cooled steam condenser units, the closed-loop optimized control on the fan speed is proposed and its realization is worth making intensive study. In this regard, the study presents the static and dynamic models of air-cooled steam condenser, and the characteristics of turbine power output affected by fan speed. Then, the structure of closed-loop control on fan speed is designed. Furthermore, two optimized methods on condenser pressure based on fan speed control are separately discussed: the optimum condenser pressure is solved by the genetic algorithm to save more energy, and the condenser pressure regulation is combined with traditional boiler-turbine coordinated control to accelerate the load response. Case study in our paper proves that the fan speed optimization can significantly improve the unit load-following capability, and furthermore unit coal consumption has been significantly reduced when the unit operating in a stable load condition. [ABSTRACT FROM AUTHOR]

A robust encryption algorithm using DNA and chaotic logistic maps was recently proposed, which can resist exhaustive attack, statistical attack and differential attack. The security of this encryption algorithm depends on the initial conditions of 1D and 2D logistic chaotic maps, which include the front and latter pixel sum of plain image. However, this scheme is not secure enough because the front half and latter half pixel sum can be known to the attacker. According to this security weakness the encryption algorithm is completely cracked by a novel chosen-plaintext attack scheme. Experiments and analysis verify our approach. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for data hiding in frequency domain with the use of genetic algorithm. At first, cover images are mapped to a proper frequency domain using the concepts of adaptive wavelet transform and genetic algorithm. In the obtained space, using a model based on Kieu and Chang, encrypted information will be embedded in the frequency coefficients that represent edges of the image in spatial domain. So the cover image will change the least and have the most compatibility with human visual system. Simulation results show that our proposed method outperforms recently published works in terms of PSNR and PSPNR factors. [ABSTRACT FROM AUTHOR]

The moving average strategy is a technical indicator that can generate trading signals to assist investment. While the trading signals tell the traders timing to buy or sell, the moving average cannot tell the trading volume, which is a crucial factor for investment. This paper proposes a fuzzy moving average strategy, in which the fuzzy logic rule is used to determine the strength of trading signals, i.e., the trading volume. To compose one fuzzy logic rule, we use four types of moving averages, the length of the moving average period, the fuzzy extent, and the recommend value. Ten fuzzy logic rules form a fuzzy set, which generates a rating level that decides the trading volume. In this process, we apply genetic algorithms to identify an optimal fuzzy logic rule set and utilize crude oil futures prices from the New York Mercantile Exchange (NYMEX) as the experiment data. Each experiment is repeated for 20 times. The results show that firstly the fuzzy moving average strategy can obtain a more stable rate of return than the moving average strategies. Secondly, holding amounts series is highly sensitive to price series. Thirdly, simple moving average methods are more efficient. Lastly, the fuzzy extents of extremely low, high, and very high are more popular. These results are helpful in investment decisions. [ABSTRACT FROM AUTHOR]