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While the building sector has a significant thermodynamic improvement potential, exergy analysis has been shown to provide new insight for the optimisation of building energy systems. This paper presents an exergy-based multi-objective optimisation tool that aims to assess the impact of a diverse range of retrofit measures with a focus on non-domestic buildings. EnergyPlus was used as a dynamic calculation engine for first law analysis, while a Python add-on was developed to link dynamic exergy analysis and a Genetic Algorithm optimisation process with the aforementioned software. Two UK archetype case studies (an office and a primary school) were used to test the feasibility of the proposed framework. Different measures combinations based on retrofitting the envelope insulation levels and the application of different HVAC configurations were assessed. The objective functions in this study are annual energy use, occupants' thermal comfort, and total building exergy destructions. A large range of optimal solutions was achieved highlighting the framework capabilities. The model achieved improvements of 53% in annual energy use, 51% of exergy destructions and 66% of thermal comfort for the school building, and 50%, 33%, and 80% for the office building. This approach can be extended by using exergoeconomic optimisation. [ABSTRACT FROM AUTHOR]

The paper proposes a bi-level optimisation framework for Electric Vehicle (EV) fleet charging based on a realistic EV fleet model including a transport demand sub-model. The EV fleet is described by an aggregate battery model, which is parameterised by using recorded driving cycle data of a delivery vehicle fleet. The EV fleet model is used within the inner level of the bi-level optimisation framework, where the aggregate charging power is optimised by using the dynamic programming (DP) algorithm. At the superimposed optimisation level, the final State-of-Charge (SoC) values of individual EVs being disconnected from the grid are optimised by using a multi-objective genetic algorithm-based optimisation. In each iteration of the bi-level optimisation algorithm, it is generally needed to recalculate the transport demand sub-model for the new set of final SoC values. In order to simplify this process, the transport demand is modelled by using a computationally efficient response surface method, which is based on naturalistic synthetic driving cycles and agent-based simulations of the EV model. When compared to the single-level charging optimisation approach, which assumes the final SoC values to be equal to 1 (full batteries on departure), the bi-level optimisation provides a degree of optimisation freedom more for more accurate techno-economic analyses of the integrated transport-energy system. The two approaches are compared through a simulation study of the particular delivery vehicle fleet transport-energy system. [ABSTRACT FROM AUTHOR]

In this paper a new stochastic-heuristic methodology for the optimisation of the electrical supply of stand-alone (off-grid) hybrid systems (photovoltaic-wind-diesel with battery storage) is shown. The objective is to minimise the net present cost of the system. The stochastic optimisation is developed by means of Monte Carlo simulation, which takes into account the uncertainties of irradiation, temperature, wind speed and load (correlated Gaussian random variables), using their probability density functions and the variance-covariance matrix. Also the uncertainty of diesel fuel price inflation rate was considered. The heuristic approach uses genetic algorithms to obtain the optimal system (or a solution near the optimal) in a reasonable computation time. This methodology includes an accurate weighted Ah-throughput battery model with several control variables, which can be set in the modern battery controllers or inverter/chargers with State of Charge control. A case study is analysed as an example of the application of this methodology, obtaining the stochastic optimisation an optimal system similar to the one obtained by the deterministic optimisation. It is recommended to perform first the deterministic optimisation (with low computation time), then the search space should be reduced and finally the stochastic optimisation can be obtained in a reasonable computation time. [ABSTRACT FROM AUTHOR]

Certain buildings are required in urban green spaces by Chinese regulations, and their area depends on the type and area of the green space. The sizes of individual buildings and the extent of dispersion among buildings in a group dramatically influences the local ecological environment and landscape. However, there is a lack of effective methods for evaluating the distribution of buildings, and it is difficult to plan and manage the arrangement of buildings in green spaces. Based on geostatistical methods for describing the distribution of geographic objects, this study presents the Index of Distribution (IOD) to describe the distribution pattern of buildings in green spaces. Yuhuatai Park and Qingliangshan Park of Nanjing are chosen as cases to verify the effectiveness of the IOD. This paper also presents a genetic algorithm-based model that can generate the plan of buildings corresponding to a specific IOD. The model provides considerable flexibility in buildings locations. The results of the model can be used as a valuable reference in the planning of buildings in green spaces. [ABSTRACT FROM AUTHOR]

Electrical power quality and stability is an important issue nowadays and technology of Superconducting Magnetic Energy Storage systems, SMES, has brought real power storage capability to power systems. Therefore, optimum SMES design to achieve maximum energy with the least length of tape has been quite a matter of concern. This paper provides an approach to design optimization of solenoid and toroid types of SMES, ensuring maximum possible energy storage. The optimization process, based on Genetic Algorithm, calculates the operating current of superconducting tapes through intersection of a load line with the surface indicating the critical current variation versus the parallel and perpendicular components of magnetic flux density. FLUX3D simulations of SMES have been utilized for energy calculations. Through numerical analysis of obtained data, formulations have been obtained for the optimum dimensions of superconductor coil and maximum stored energy for a given length and cross sectional area of superconductor tape. [ABSTRACT FROM AUTHOR]

A multi-wall structure by sandwiching star-shaped tube between two circulars is proposed and named it CSC-tube in this paper. It is found that the interaction effects between tubes walls play an important role in the improvement of the crashworthiness. The experimental result shows that the mean crushing force of the new design is 25% higher than that of star-shaped tube. The Surrogate model based on radial basis function is employed for the multi-objective optimization problem to maximize the value of the specific energy absorption and minimize the peak value of the crushing force with the design variables—inner, outer and core thickness. The models are optimized by Non-Dominated Sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ) and the compromise solution shows that 8.03% increase in specific energy absorption and 12.73% decrease in the value of peak crushing force can be achieved. [ABSTRACT FROM AUTHOR]

An efficient and optimized field development plan is a crucial and primary aspect in maximizing well productivities, improving the recovery factors of oil and gas fields, and thus, increasing profitability most effectively. In this paper, we apply a metaheuristics algorithm known as the Imperialist Competitive Algorithm (ICA) to determine optimal well location for maximum well productivity. The ICA is an evolutionary algorithm that mimics socio-political imperialist competition. This algorithm uses an initial population that consists of colonies and imperialists that are assigned to several empires. The empires then compete with each other, which cause the weak empires to collapse and the powerful empires to dominate and overtake their colonies. We compared the ICA performance with that of the particle swarm optimization (PSO) as well as the genetic algorithm (GA) in the following four optimization scenarios: 1) a vertical well in a channeled reservoir, 2) a horizontal well in a channeled reservoir, 3) placement of multiple vertical wells, and 4) placement of multiple horizontal wells. In all four scenarios, the ICA achieved a better solution than the PSO and GA in a fixed number of simulation runs. In addition, we conducted sensitivity analyses for three important parameters (revolution ratio, assimilation coefficient, and assimilation angle), and the results of these analyses showed that the recommended ICA default parameters generally led to acceptable performances in our examples. However, to obtain optimum performance, we recommend tuning the three main ICA parameters for specific optimization problems. [ABSTRACT FROM AUTHOR]

A decision forest is an ensemble of decision trees, and it is often built to discover more patterns (i.e. logic rules) and predict/classify class values more accurately than a single decision tree. Existing decision forest algorithms are typically used for building huge numbers of decision trees, involving large memory and computational overhead, in order to achieve high accuracy. Generally, many of the trees do not contribute to improving the ensemble accuracy of a forest. As a result, ensemble pruning algorithms aim to get rid of those trees while generating a subforest in order to achieve higher (or comparable) ensemble accuracy than the original forest. The objectives are two fold: select as small number of trees as possible, and maintain the ensemble accuracy of the subforest as high as possible. An optimal subforest can be found by exhaustive search; however it is not practical for any standard-sized forest as the number of candidate subforests grows exponentially. In order to avoid the computational burden of an exhaustive search, many greedy and genetic algorithm-based subforest selection techniques have been proposed in literature. In this paper, we propose a subforest selection technique that achieves small size as well as high accuracy. We use a genetic algorithm where we carefully select high quality individual trees for the initial population of the genetic algorithm in order to improve the final output of the algorithm. Experiments are conducted on 20 data sets from the UCI Machine Learning Repository to compare the proposed technique with several existing state-of-the-art techniques. The results indicate that the proposed technique can select effective subforests which are significantly smaller than original forests while achieving better (or comparable) accuracy than the original forests. [ABSTRACT FROM AUTHOR]

This paper presents a way of solving the classical area (volume)-to-point heat conduction problem by the means of a simple Genetic Algorithm (GA) in square configuration. After a short description of the numerical method, the optimal solutions proposed for minimizing the peak or mean temperature of a domain are presented. The effects of the conductivity ratio and the filling ratio on the configurations of the conductive tree are also analyzed and discussed. A numerical benchmark is then established to assess the influence of mesh resolution and the reproducibility of the GA optimization. Results show that GA is capable of proposing solutions having almost the same cooling effectiveness for different mesh resolutions or random seed generators. GA is also relevant compared to other optimization techniques presented here. It can be considered as a simple, easy to adapt and robust but computation time consuming method for addressing the general area (volume)-to-point heat conduction problem. [ABSTRACT FROM AUTHOR]

An efficient identification method for modal testing of viscoelastic composite materials is demonstrated in this paper, through the analytical–experimental transfer function method. The procedure for the identification of analytical–experimental transfer functions is carried out using a genetic algorithm (GA) by minimizing the difference between the measured response from tests and the calculated response, which is a function of the modal parameters. The analytical transfer functions provide a sub-structuring process to identify modes, as a function of damped natural frequencies and loss factors of a complex structure and it is insensitive to experimental noise as well as the modal coupling effect. The proposed method is verified with the calculation of the elastic modulus and modal properties of a cantilever steel beam with the FEM and compared with the identification results of the proposed algorithm. The effectiveness of the proposed method is demonstrated by investigating the static and dynamic behavior of epoxy cantilever beam specimens reinforced with silica nanoparticles. Analytical–experimental transfer functions accurately identified the viscoelastic and dynamic response of the studied specimens, while the results indicated that the inclusion of nanosilica particles increased the stiffness of the epoxy network and the damping response of the reinforced specimens is improved. [ABSTRACT FROM AUTHOR]

The integrated timetable and speed profile optimization model has recently attracted more attention because of its good achievements on energy conservation in metro systems. However, most previous studies often ignore the spatial and temporal uncertainties of train mass, and the variabilities of tractive force, braking force and basic running resistance on energy consumption in order to simplify the model formulation and solution algorithm. In this paper, we develop an integrated metro timetable and speed profile optimization model to minimize the total tractive energy consumption, where these real-world operating conditions are explicitly considered in the model formulation and solution algorithm. Firstly, we formulate a two-phase stochastic programming model to determine the timetable and speed profile. Given the speed profile, the first phase determines the timetable by scheduling the arrival and departure times for each station, and the second phase determines the speed profile for each inter-station with the scheduled arrival and departure times. Secondly, we design a simulation-based genetic algorithm procedure incorporated with the optimal train control algorithm to find the optimal solution. Finally, we present a simple example and a real-world example based on the operation data from the Beijing Metro Yizhuang Line in Beijing, China. The results of the real-world example show that, during peak hours, off-peak hours and night hours, the total tractive energy consumptions can be reduced by: (1) 10.66%, 9.94% and 9.13% in comparison with the current timetable and speed profile; and (2) 3.35%, 3.12% and 3.04% in comparison with the deterministic model. [ABSTRACT FROM AUTHOR]

Laser welding process parameters have significant effects on the welding bead profile and quality of the welding joint. This paper proposes an integration method of process parameters optimization using finite element method (FEM), Kriging metamodels and nondominated sorting genetic algorithm II (NSGA-II) in laser welding for stainless steel 316L. The process parameters in this study are laser power (LP), welding speed (WS) and laser focal position (LF). Firstly, a three-dimensional thermal finite element model is developed to obtain the simulated results of bead width (BW) and depth of penetration (DP). Then, Kriging metamodels are constructed to reflect the relationship between input process parameters and output responses. Finally, NSGA-II is used to search for multi-objective Pareto optimal solutions. In addition, the main effects and contribution rates of multiple process parameters on welding bead profile are analyzed. The results of verification experiments indicate that the optimal process parameters are effective and reliable for producing expected welding bead profile. [ABSTRACT FROM AUTHOR]

A new metaheuristic method, the Cuckoo Search (CS) algorithm, based on the life of a bird family is proposed in this paper for optimal design of static synchronous compensator (STATCOM) in a multimachine environment. PV curves are illustrated to determine the best location of STATCOM. The STATCOM parameter tuning problem is converted to an optimization problem which is solved by CS Algorithm. The performance of the proposed CS based STATCOM (CSSTATCOM) is compared with Genetic Algorithm (GA) based STATCOM (GASTATCOM) and open loop STATCOM under various operating conditions and disturbances. The superiority of the proposed technique in damping oscillations and enhancing voltage profiles is confirmed through eigenvalues and time domain simulation results over the GA and open loop one. [ABSTRACT FROM AUTHOR]

As a result of multiple land use types, spatial heterogeneity, and conflicts of interest among multiple participants, multi-site land use allocation becomes a complex and significant optimization issue. We propose an improved Genetic Algorithm (GA) to deal with multi-site land use allocation, in which maximum economic benefit, maximum ecological benefit, maximum suitability, and maximum compactness were formulated as optimal objectives; and residential space demand and some regulatory knowledge were set as constraints. A Goal Programming model with a reference point form was used to manage trade-offs among multiple objectives. In order to improve the efficiency of the common GA applied to multi-site land use allocation, two crossover steps and two mutation operations were designed. This paper presents an application of the improved GA to the Regional District of Central Okanagan in Canada. Results showed that the proposed GA exhibited good robustness and could generate any optimal land use scenario according to stakeholders' preferred objectives, thus having the potential to provide interactive technical support for land use planning. [ABSTRACT FROM AUTHOR]

Finding the optimized trade-off relationship between cost and time, two important objectives of construction projects, helps project managers and their teams select a more suitable schedule for a given project. This trade-off relationship can roughly be estimated using past and cumulative knowledge, but since the early 1970s, researchers have been working on a systematic and mathematical solution to define this relationship more accurately. These researchers have used different optimization techniques such as the genetic algorithm (GA), ant colony, and fuzzy logic to further explore the relationship. In the present paper, the authors have used their previously introduced construction schedule generator algorithm to present graphical relationships between pre-defined objectives of schedule optimizations. The process starts with developing construction schedules from the project's Building Information Model (BIM) as part of the input along with resource data. Then the process continues with optimization of all developed construction schedules according to the two mentioned objectives along with the introduced job-site movement objective, which mathematically helps the sequence of installation be more logical and practical. Finally generation of a 3D space for all the created and calculated construction schedules in the form of a 3D solution cloud point. These 3D construction schedules show solution cloud points and three Pareto Fronts for the given project. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid neuro-evolutive algorithm (NEA) that uses a compact indirect encoding scheme (IES) for representing its genotypes (a set of ten production rules of a Lindenmayer System with memory), moreover has the ability to reuse the genotypes and automatically build modular, hierarchical and recurrent neural networks. A genetic algorithm (GA) evolves a Lindenmayer System (L-System) that is used to design the neural network’s architecture. This basic neural codification confers scalability and search space reduction in relation to other methods. Furthermore, the system uses a parallel genome scan engine that increases both the implicit parallelism and convergence of the GA. The fitness function of the NEA rewards economical artificial neural networks (ANNs) that are easily implemented. The NEA was tested on five real-world classification datasets and three well-known datasets for time series forecasting (TSF). The results are statistically compared against established state-of-the-art algorithms and various forecasting methods (ADANN, ARIMA, UCM, and Forecast Pro). In most cases, our NEA outperformed the other methods, delivering the most accurate classification and time series forecasting with the least computational effort. These superior results are attributed to the improved effectiveness and efficiency of NEA in the decision-making process. The result is an optimized neural network architecture for solving classification problems and simulating dynamical systems. [ABSTRACT FROM AUTHOR]

The optimization of linear consecutive- k -out-of- n (Lin/Con/ k / n ) is to find an optimal component arrangement where n components are assigned to n positions to maximize the system reliability. With the interchangeability of components in practical systems, the optimization of Lin/Con/ k / n systems is becoming widely applied in engineering practice, which is also a typical component assignment problem concerned by many researchers. This paper proposes a Birnbaum importance-based genetic algorithm (BIGA) to search the near global optimal solution for Lin/Con/ k / n systems. First, the operation procedures and corresponding execution methods of BIGA are described in detail. Then, comprehensive simulation experiments are implemented on both small and large systems to evaluate the performance of the BIGA by comparing with the Birnbaum importance-based two-stage approach and Birnbaum importance-based genetic local search algorithm. Thirdly, further experiments are provided to discuss the applicability of BIGA for Lin/Con/ k / n system with different k and n . Finally, the case study on oil transportation system is implemented to demonstrate the application of BIGA in the optimization of Lin/Con/ k / n system. [ABSTRACT FROM AUTHOR]

Path planning is not only the premise of underwater navigation and obstacle avoidance, but also the core and key to improve the operation durability for the bionic robotic fish. In this paper, the path length, smoothness and safety factor are selected as the optimization performance indexes. In order to solve the path planning with ocean currents and moving obstacles, a two-level optimization algorithm is proposed for the bionic robotic fish. In the first level of optimization, it solves the global path planning problem with known environment. In the second level of optimization, based on the optimization results of the first level of optimization, the local optimal path is obtained by the dynamic optimization method in the environment with unknown static and moving obstacles, which can solve the problem of obstacle avoidance for irregular obstacles effectively. The two-level optimization algorithm improves the accuracy of the path planning. Fuzzy adaptive genetic algorithm (FAGA) is given to further improve the optimization performance. The proposed algorithm can improve the optimization performance of the path planning, whose effectiveness is demonstrated by the simulation and experiment results. [Display omitted] • A two-level optimization algorithm is initially developed to improve the optimization accuracy. • The dynamic optimization mechanism is proposed to solve the path planning with unknown ocean currents and obstacles. • Simulations and experiments are presented to demonstrate the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, the focus was to develop an optimization tool by using mathematical layout optimization methods applied to increase the energy capacity or reduce the cost of an offshore wind farm. For this purpose, two wind farm layout optimization (WFLO) models were developed using genetic algorithm (GA): model (a) minimizing the cost of power for variable turbine number, model (b) maximizing the power generation for fixed turbine number. Wind speed and wind direction were assumed constant. Therefore, unlike previous studies, smaller grid sizes were used in the models developed in this study, and better-performing layouts were obtained than in the literature. Continuous approach to WFLO guarantees the best result since it evaluates unlimited number of possibilities. However, it complicates the problem as well as brings high computational costs. Therefore, discrete approach was used in this study, and the performance of using finer grids was investigated. As a result, it was observed that the optimum layout may be improved as the grid size got smaller. Following this, developed models within the scope of this study were applied to a high offshore wind potential area in Turkey, and a preliminary wind farm layout design was proposed for this area. • Two Python codes are developed for fixed and variable turbine numbers. • Using smaller grid sizes showed potential for improvement in efficiency in WFLO. • An optimum wind farm layout is proposed for a high offshore wind potential region in Turkey. [ABSTRACT FROM AUTHOR]

• In the modern environment, the innovations emerging in information technology has driven us to focus on strengthening the security process. • This led to the recent advancement in face recognition technology and special attention is given to the recognition process by applying a biometric system for personal identification. • Face recognition is renowned as one of the efficacious applications of picture study. • The proposed model works in two-step processes: face feature extraction and face pattern matching. • The result has shown that the model is highly efficient, the PCA method has achieved 80.6% of recognition rate and the AFSA has acquired 88.9% accuracy in correct recognition rate. In the modern environment, the innovations emerging in information technology has driven us to focus on strengthening the security process. This led to the recent advancement in face recognition technology and special attention is given to the recognition process by applying a biometric system for personal identification. Face recognition is renowned as one of the efficacious applications of picture study, popularly applied for reliable biometric where security is the important quality attribute to be achieved. In this paper, a highly effective face recognition system has been proposed by incorporating genetic algorithms for better search strategy. The proposed model works in two-step processes: face feature extraction and face pattern matching. The Haralick features and features extracted from face databases using PCA are used for face recognition. The most eminent artificial firefirefly swarm optimization algorithm is employed for better searching and matching of facial features. From the simulation experiments performed on the faces warehoused in the OUR database, the result has shown that the model is highly efficient, the PCA method has achieved 80.6% of recognition rate and the AFSA has acquired 88.9% accuracy in correct recognition rate. [ABSTRACT FROM AUTHOR]

• A CH selection optimized based on GA (NCOGA) for the heterogeneous WSN is proposed. • A comprehensive discussion on related work is presented which highlights the research gaps. • The proposed algorithm is discussed in a step-by-step manner. • Rigorous simulations and analysis are presented. • Results are validated against the most recent state-of-the-art algorithms. In this paper, an optimized cluster head (CH) selection method based on genetic algorithm (NCOGA) is proposed which uses the adaptive crossover and binary tournament selection methods to prolong the lifetime of a heterogeneous wireless sensor network (WSN). The novelty of the proposed algorithms is the integration of multiple parameters for the CH selection in a heterogeneous WSN. NCOGA formulates fitness parameters by integrating multiple parameters like the residual energy, initial energy, distance to the sink, number of neighbors surrounded by a node, load balancing factor, and communicating mode decider (CMD). The parameters for load balancing and CMD are utilized to discover out the best candidate to be selected as a relay CH and for deciding the mode of communication (single or multi-hop) of CH. Further, these parameters are useful in avoiding hot-spot problem in the network. The working of the NCOGA starts based on the criteria "consider only those nodes which have energy higher than the pre-defined threshold energy". This criterion of nodes selection makes the NCOGA more efficient and quickly convergent. Extensive computer simulations are conducted to determine the effectiveness of the NCOGA. Simulation results reveal that the proposed NCOGA outperforms the state-of-the-art optimization algorithms based on GA in terms of several performance metrics, specifically, stability period, residual energy, network lifetime, and throughput. [ABSTRACT FROM AUTHOR]

With the help of deep learning technology, constructing the neural network model between structures and properties of materials is one of important methods to speed up the new materials discovery and design. However, both the training efficiency and accuracy of model play important roles in application of machine learning on materials. At present, the commonly used traditional neural network model algorithm in materials is single, easy to fall into local optimization, and difficult to converge in the training process, and the training model has a low prediction power. In this paper, a novel neural network optimization method based on swarm algorithms is proposed to discover better parameters in developing new materials. In this neural network model, particle swarm and Bayesian optimization are used to improve the crossover, mutation and selection strategies of genetic algorithm. Using advantages of each algorithm to realize the optimal parameters selection of the neural network and network optimization, and to improve the prediction ability of the neural network model. By comparing energies predicted by traditional networks and the proposed network, more stable models with high efficiency and precision are trained. [Display omitted] • The combination of particle swarm optimization algorithm and genetic algorithm. • Swarm intelligence algorithms for optimizing neural networks. • Constructing neural network potential energy for material structure prediction. • Constructing neural network with high training efficiency and prediction accuracy. [ABSTRACT FROM AUTHOR]

Offshore platforms are key structures in energy provision that are subjected to continuous and harsh environmental loadings. A tuned mass damper (TMD) has been proposed as a robust and effective vibration control method for marine structures. However, selecting optimum TMD parameters under certain constraints may become a multimodal optimization problem. Several studies focused on finding the TMD optimum parameters using different metaheuristic optimization algorithms. Nonetheless, it becomes a necessity to find an answer to the question: which algorithm can find the most effective TMD parameters to minimize the structure response? This paper presents the use of a novel metaheuristic optimizer, the Improved Grey Wolf Optimizer (IGWO), for optimum tuning of a TMD aimed at reducing the wave-induced vibration level of a jacket type platform. Moreover, a comprehensive performance analysis of four different metaheuristic algorithms for optimizing a TMD has been investigated. They are IGWO, popular algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA). In order to achieve this purpose, a finite element (FE) model representing a prototype of a jacket-type platform under the influence of irregular waves is numerically solved in COMSOL Multiphysics. Irregular waves are presented using the JONSWAP spectrum and hydrodynamic forces are modeled using Morison's equation. The comparative algorithms are evaluated for the independent minimization of three different objective functions. The target parameters of TMD to be tuned are the mass ratio (μ), the frequency ratio (f opt), and the damping ratio (ζ opt). Finally, the optimized TMD will be applied to the FE model and the resultant response will be investigated. The results demonstrate the superior ability of the IGWO optimizer to optimally tune a TMD attached to marine structures compared with the other algorithms. In addition, IGWO proved its unique ability to steadily converge toward the global minimum value. • Metaheuristic optimization of a TMD to minimize the dynamic response of a jacket platform influenced by irregular waves. • The use of the improved grey wolf optimizer as a novel technique for TMD optimization. • Comprehensive performance analysis of four metaheuristic algorithms (GA, PSO, WOA, and IGWO) for TMD optimization. • The analysis includes three different objective functions to be minimized by the comparative algorithms. • Finite element simulation of applying the optimized TMD to a jacket platform using COMSOL. [ABSTRACT FROM AUTHOR]

Optimization is an art that is best performed by a well-tuned algorithm. Nature – instead of being fully deterministic – is evolutionary, vibrant and resourceful. The nature-inspired algorithms use the best combination and evolution strategy in a given situation. In this work, a new metaheuristic algorithm is developed by using social behavior in human dynasties. The motivation, conceptual framework, mathematical model, pseudocode and working of the algorithm are described in this paper and the adjoining papers. The proposed dynastic optimization algorithm (DOA) has evolved with the wind turbine micrositing (WTM) problem in mind. The proposed DOA has been successfully applied to the traditional WTM and encouraging results have been obtained. It is demonstrated that the proposed approach is equally viable as other existing algorithms, like the Genetic algorithm (GA) and Differential evolution algorithm (DEA). The main advantage of the proposed DOA is that it is simple, unique, fast, unbiased and versatile in comparison with others. The validation of results has been made with respect to a few other mainstream algorithms in the literature, besides statistical sensitivity analysis is also performed. The 95% confidence interval forecasts for the power enhancement and cost reduction by using DOA against GA and DEA are encouraging and guarantee an adequate amount of mean increase in power output and a considerable average cost reduction. • The proposed metaheuristic optimization algorithm is inspired from human dynasties. • Full application is made to the Wind Turbine Micrositing (WTM) problem. • The mathematics, pseudocode, working, advantages and application are discussed. • The DOA reveals superior results as compared to the GA and DEA. • 95% confidence bounds indicate that future forecasts are encouraging. [ABSTRACT FROM AUTHOR]

This research presents an optimization problem associated with scheduling of unrelated parallel machines considering the simultaneous effects of start-time-related deterioration, position-related learning and sequence-related setup times, with the aim to achieve an optimized value for the mean weighted tardiness and power consumption minimization. A workaround is proposed and served as the converter for solving the Mixed Integer Programming (MIP) problem by the continuous-based metaheuristic algorithms. Knowing the NP-hard nature of the problem, three metaheuristic algorithms, namely, Genetic Algorithm (GA), Cat Swarm Optimization (CSO) and Interactive Artificial Bee Colony (IABC), are employed to obtain quality solutions within acceptable computation time. A unique elitism strategy is introduced in the conventional CSO to reduce the computational time of its seeking mode and to provide improved solution, with the new algorithm form called CSO-Elit. Furthermore, a compact solution expression form is proposed for the use of GA, CSO-Elit and IABC to reduce the number of constraints. With the newly proposed model, many essential constraints are automatically built into the solution representation. Accordingly, the efficiency of algorithms is compared with LINGO regarding the quality of solutions, computational time, and overall power consumption using two sets of problems in small and large scales. In solving the small-scale cases, no statistically meaningful difference was observed between LINGO, GA and CSO-Elit, while in large-scale instances CSO-Elit demonstrated higher performance. The contributions of this paper lie in modeling a complex parallel machines problem considering power consumption minimization and proposing a new form of CSO algorithm with elitism strategy to reduce the computational time. • A novel problem has been identified and explored in this paper. • The model includes mathematical modeling and optimization of an unrelated parallel machine scheduling problem. • Several restrictions which are practical and industrially relevant are considered in this paper. • A new metaheuristic approach by adopting an elitism strategy into a metaheuristic algorithm is proposed. [ABSTRACT FROM AUTHOR]

Determining the optimal sizing of a solar power tower system (SPTS) with a thermal energy storage system is subject to finding the optimum values of design parameters including the solar multiple (SM), design direct normal irradiance (DNI) and thermal storage hours. These design parameters are determined for each station separately and have remarkable effects on the thermo-economic performance of the system. This paper aims to demonstrate how artificial intelligence (AI) techniques may play an important role in addressing the above-mentioned need and help determine the optimum design parameters for different stations. For this purpose, we developed a thermo-economic model of a 100 MW SPTS with a molten salt storage system for five stations (two stations in India, and one each in Bangladesh, Pakistan, and Afghanistan). A method-based AI is utilized in this paper to ascertain the design parameters of the system. Additionally, a novel hybrid method based on adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching-learning-based optimization algorithm (ANFIS-GATLBO) is employed. The input parameters are latitude, longitude, design point DNI and SM, while the annual energy produced, levelized cost of energy and capacity factor are the target variables. The results of the study show that although the annual energy produced by SPTS rises by increasing the SM and decreasing design point DNI, optimum design parameters should be determined by the economic factors. In addition, it was found that the ANFIS-GATLBO method used in this study successfully predicted the targets with a correlation coefficient close to 1. • A 100 MW solar power tower system (SPTS) for 5 different stations is designed and evaluated. • Design parameters (solar multiple (SM), design direct normal irradiance (DNI) and thermal storage hours) are discussed. • An intelligent method based on ANFIS optimized with combination of GA and TLBO is developed to simulate the behavior of SPTS. • To find the optimum design parameters of SPTS, the economic criteria should be taken into account. [ABSTRACT FROM AUTHOR]

In this paper, the influence of the slot parameters of the slotted fins on the flow and heat transfer performance and the exergy destruction are studied by numerical simulation, and the optimal parameters corresponding to the best overall performance and the minimum exergy destruction are obtained by the method of CFD coupled with optimization algorithm, respectively. In current work, the slot length and width are taken as parameters (slot length 1.2–2.1 mm, slot width 0.1–1.0 mm), 100 different cases are studied under the same boundary conditions, and the results are analyzed and optimized by the neural network and genetic algorithm. The overall performance is taken as the evaluation of the heat transfer performance and the power consumption, while exergy destruction caused by heat transfer and fluid flow are taken as the estimate of the loss of available energy and mechanical work. The results show that the overall performance is best when the slot length and width are 1.38 mm and 0.27 mm respectively, and the overall performance is 13.1% higher than that of flat fin. While the exergy destruction caused by heat transfer and fluid flow are minimized synergistically when the slot length and width are 1.37 mm and 0.12 mm, and the irreversible loss caused by heat transfer is reduced by 19.13%, at the cost of an increase of 40.6% in the irreversible loss caused by fluid flow. The difference between the parameters corresponding to best overall performance and exergy destruction minimization means the irreversible loss of heat transfer and fluid flow process is not minimum when the overall performance is best, and indicates the exergy destruction minimization can be a principle to evaluate the heat transfer and flow process. The result in this paper is of great significant to the energy utilization and the improvement of the thermal quality in waste heat recovery. • Optimization is realized by combination of CFD coupled with optimization algorithm. • Results are analyzed and optimized by the neural network and genetic algorithm. • Exergy destruction caused by heat transfer and fluid flow are taken as the estimate of irreversibility. • Exergy destruction minimization can be a principle to evaluate the heat transfer and flow process. [ABSTRACT FROM AUTHOR]

In this paper, an efficient methodology to obtain Best Theory Diagrams (BTDs) for composite and sandwich plates is presented. A BTD is a curve that provides the minimum number of unknown variables in a kinematic theory for the desired accuracy. The present work combines genetic algorithms (GAs) and neural networks (NNs) to construct BTDs efficiently, faster than using GAs alone. A structural finite element model of a plate is derived using the principle of virtual displacements. Arbitrary plate models are considered in a compact manner using Carrera Unified Formulation. As reported in previous papers by the authors, a multiobjective optimization technique using a GA is applied to build BTDs for a given structural problem. The plate models stresses and displacements are compared to those of a reference solution, and a plate model performance is quantified in terms of the number of unknown variables, the mean error and standard deviation of the stresses and displacements. As a novelty, a NN is trained to reproduce the mean error and standard deviation of the stresses and displacements for any plate model refined from a reference plate model. In this way, the computational time required to build BTDs using the finite element method is optimized. BTDs for different boundary conditions not previously investigated are reported in this paper. The results of the present method are compared to those obtained via GA using the finite element solution. The BTDs build using a NN are comparable to those obtained by a regular finite element analysis. Refined plate models with appropriate predictive capabilities and measured computational cost are presented. The results show that the NN reduces the computational time to build BTDs drastically. [ABSTRACT FROM AUTHOR]

An intrusion detection system (IDS) is considered critical for detecting threats, intrusions, and unauthorized access. IDS monitors massive network traffic that includes irrelevant and extravagant features that profoundly impact the system's efficiency and slow down the classification process for accurate decisions. Its effectiveness is tested over the various techniques that comprise an enormous volume of data and heavy network traffic. Many approaches, such as machine learning algorithms, data mining, swarm intelligence, and artificial neural networks (ANN), have been implemented for adequate and improved IDSs. This paper recommends a novel feature selection method using a genetic algorithm (GA) that determines the optimal feature subsets from the NSL-KDD dataset. Further, hybrid classification has been performed using logistic regression (LR) and decision tree (DT) to achieve a better detection rate (DR) and accuracy (ACC). This research applied and compared several meta-heuristic algorithms' performance to optimize the selected optimal features. The experimental results show that the grey wolf optimization (GWO) algorithm gives the best accuracy of 99.44% and DR of 99.36% with the reduction of features (=20) out of (=41). The results of the proposed work are compared with the existing feature selection methods to verify improved performance. [Display omitted] • Novel feature selection method using a genetic algorithm (GA) that determines the optimal feature subsets. • Hybrid classification to achieve a better detection rate (DR) and accuracy (ACC). • Compared several meta-heuristic algorithms' performance to optimize the selected optimal features. • The grey wolf optimization (GWO) algorithm gives the best accuracy of 99.44% and DR of 99.36% with the reduction of features (=20) out of (=41). • Optimization for improvement of IDS. [ABSTRACT FROM AUTHOR]

• Porous sponge composite sound absorption structure with embedded non-regular scatterers; • Topology optimization design of low-frequency broadband structures; • Combination of genetic algorithms for global optimization; • Functional analysis of the scatterer structure. The low-frequency sound-absorption performance of porous acoustic materials is poor, while the combination of porous materials and scatterer-based structures can improve the low-frequency absorption coefficient of porous materials. This paper investigates the use of a topology optimization with a genetic algorithm (TOGA) to automatically design the optimal structure for better broadband low-frequency sound-absorption performance. This study is designed to optimize the distribution of scatterers inside a 5-cm-thick melamine sponge, and the optimization results in an average absorption coefficient of 0.8 in the frequency range of 200–1600 Hz. The effects of different structures, different locations, different material parameters, different acoustic incident angles and simplified structures on the sound-absorption coefficients are investigated. The simulation calculation results and experimental results verify the correctness of the results. This composite acoustic material and the method provide more design ideas for the design of future acoustic materials. [ABSTRACT FROM AUTHOR]

The need to determine the optimum well placement which causes maximum cumulative oil production has dramatically raised in recent years. Hence, there is a need for a reliable and accurate tool to diagnose the possible solutions in a cost- and time-effective manner. Two of the most commonly used algorithms in this area are Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). These two algorithms, the similar used methodologies for finding the optimum well location, suffer from local optimization issues. These algorithms are slow, and their found points is not accurate. Therefore, in this study, a new meta-heuristic optimization algorithm known as the Black Widow Optimization (BWO) is employed to optimize the location of five production wells. The results are compared to those of PSO and GA. To this end, the cumulative oil production is intended as the objective function, and a simplified model of an actual reservoir is simulated. Afterward, the well placement is optimized. The results revealed a higher improvement of the objective function for the BWO algorithm compared to PSO and GA. The new algorithm was faster and resulted in a better optimum point. Concisely, the BWO algorithm reduced the associated time complexities up to 9.19% and 18.29% compared to GA and PSO, respectively. The PUNQ-S3 reservoir model was also employed to validate the results. • The well location is optimized, using Black Widow Optimization (BWO). • To validate the results, BOW performance is compared with particle swarm optimization and genetic algorithm. • BWO algorithm found the global optimum within less computational time (9.19% faster than GA, and 18.29% faster than PSO). • BWO resulted in a better optimum point in comparison to GA and PSO. • This study can be continued by repeating the methodology of this paper in bigger fields with more wells. [ABSTRACT FROM AUTHOR]

• Systematically integrating deep neural network and genetic algorithm for truss optimization. • Improving exploration and exploitation capabilities by deep neural networks. • Exploiting data generated during optimization • Reducing difficulties in the deep neural network generation. The truss optimization problem has been extensively investigated, and the optimized trusses have been widely used in various fields. Truss optimization is a challenging optimization involving many difficulties, such as discrete variables and non-convex problems. In order to solve these problems, various metaheuristic optimization methods have been proposed. Due to the stochastic feature of these methods, it is always computationally intensive, and the optimization results may vary greatly in different optimization runs. In this paper, a hybrid intelligent genetic algorithm (HIGA) is proposed to improve the effectiveness and efficiency of truss optimization problems. This method systematically integrates deep neural network (DNN) and genetic algorithm (GA) in the optimization process. A two-step training procedure is proposed where the data generated during the optimization process is exploited to update DNN. Next, based on the generated DNN model, an optimization prediction procedure is proposed to seek more optimized trusses in an efficient manner. Through the investigation of three classical truss problems (size optimization, shape optimization and size-shape integrated optimization), the effectiveness of the proposed method is validated. In addition, the influence of different settings on the optimization performance and efficiency is investigated to demonstrate the applicability and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

The development and validation of a 2D analytical wind turbine wake model based on Jensen’s wake model using Gaussian function is presented in this paper. The velocity deficit predicted by the newly-developed Jensen–Gaussian wake model is compared with wind tunnel experimental measured data in literatures and results show that, the velocity deficit predicted by the model fits well with the measured data at different downwind distances of X = 2.5 D , X = 5 D , X = 7.5 D and X = 10 D . Considering the turbulence inside the turbine wake, a new turbulence model is developed and based on this, the Jensen–Gaussian wake model was improved and validated. The 2D Jensen–Gaussian wake model is then applied in the wind turbine layout optimizing process within a wind farm based on the multiple populations genetic algorithm (MPGA). The performance of this newly 2D model in the optimization process is validated and compared with the results presented in some typical studies on the turbine layout optimization. The comparison is performed for ‘constant wind speed of 12 m/s with variable wind directions’. Using the 2D Jensen–Gaussian wake model instead of Jensen’s wake model in the MPGA turbine layout optimization program, both the total power generation and wind farm efficiency decreased. The wind farm efficiency drop to 77.83%, 78.47% and 81.84% from 96.83%, 96.34% and 96.23% for 38, 39 and 40 wind turbines, respectively which is in accordance with the literatures on the power losses caused by wake effect in large wind farm. The development and application of the 2D Jensen–Gaussian wake model means more theory significance and practical values in wind energy utilization. [ABSTRACT FROM AUTHOR]

In this paper a population based evolutionary optimization methodology called firefly algorithm (FFA) is applied for the optimization of system coefficients of the infinite impulse response (IIR) system identification problem. FFA is inspired by the flash pattern and characteristics of fireflies. In FFA technique, behaviour of flashing firefly towards its competent mate is structured. In this algorithm attractiveness depends on brightness of light and a bright firefly feels more attraction for the brighter one. For this optimization problem, brightness varies inversely proportional to the error fitness value, so the position of the brightest firefly gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic movement equation results in balancing of exploration and exploitation of search space. The proposed FFA based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using FFA over GA, PSO and DE in terms of convergence speed, identifying plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters. [ABSTRACT FROM AUTHOR]

The estimation of software effort is an essential and crucial activity for the software development life cycle. In recent years, many researchers and software industries have given significant attention on the estimation of software effort. In industry, effort is used for planning, budgeting and development time calculation. Therefore a realistic effort estimation is required. Many researchers have proposed various models for software effort estimation, such as statistical models, algorithmic models, machine learning based models and nature inspired models in the past. In this research paper, a simplified genetic algorithm based model is proposed. A simplified genetic algorithm is used for optimizing the parameters of the basic COCOMO model. The proposed approach is applied on NASA software project dataset. Experimental results show better realistic estimation over the basic COCOMO. [ABSTRACT FROM AUTHOR]

The effect of fin design parameters on the performance of plate-fin heat exchanges was investigated in the paper, in which an improved algorithm combing a Kriging response surface and multi-objective genetic algorithm was used. An ideal gas was adopted as the working fluid and ε -NTU method was utilized to determine the heat transfer and pressure drop. The fin height h , fin space s , fin thickness t and interrupted length l of serrated fin and channel inlet Reynolds number are firstly optimized, while the j factor, f factor and JF factor are optimization goals. The results show that when the inlet channel flow is in the laminar flow ( Re < 1000), it is beneficial to trade off the j factor and f factor. Furthermore, the total heat flow rate, total annual cost and number of entropy production units of plate-fin heat exchanges are optimized with the specified mass flow rate under given space by multi-objective optimization. Results obtained from the first two objectives and three objectives show that the fin design parameters are very similar except that the latter interrupted length is smaller slightly. A comparison between the results obtained by the previous approaches and the proposed algorithm shows that under the same effectiveness, the annual cost of the proposed algorithm is about 10% lower than the previous ones and it is faster to be converged. Therefore, this study demonstrated that the proposed algorithm is able to optimize the fin design parameter of serrated fin and the obtained results are beneficial to guide the design of plate-fin heat exchanges. [ABSTRACT FROM AUTHOR]

Engineering systems are designed to carry the load. The performance of the system largely depends on how much load it carries. On the other hand, the failure rate of the system is strongly affected by its load. Besides internal failures, such as fatigue and aging process, systems may also fail due to external impacts such as nature disasters and terrorism. In this paper, we integrate the effect of loading and protection of external impacts on multi-state systems with performance sharing mechanism. The objective of this research is to determine how to balance the load and protection on system elements. An availability evaluation algorithm of the proposed system is suggested and the corresponding optimization problem is solved utilizing genetic algorithms. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to optimize noise emission level associated with two types of speed reducers for different speeds of a vehicle (20, 40, 60 km/h) by Genetic Algorithm and Artificial Neural Network. The optimization shows that the maximum level of noise is sensitive to speed reducer dimensions. It is reduced by 24 dB(A) by changing the width from 0.6 m to 0.3 m for the height 0.04 m whereas, it is reduced by 32 dB(A) by changing from the height 0.055 m and the width 0.9 m to the height 0.04 m and the width 0.3 m. [ABSTRACT FROM AUTHOR]

Simulation-optimization (Sim-Opt) is a widely used optimization technique that enables the use of simulation model so as naturally describe system complexity and stochastics. A key barrier to its broader adoption is the high computational cost associated with simulation that often limits its practicability. In this paper, we propose the use of GPU parallel computing, to enhance the computational efficiency of Sim-Opt. The main objective of this work is to develop a systematic framework that can be used to construct an efficient hybrid CPU-GPU program. The optimization of a process monitoring model using a Genetic Algorithm is used as a case study to illustrate the proposed approach. Our results show an over 100× acceleration of computation time by the developed hybrid program in comparison to a traditional CPU-based approach. [ABSTRACT FROM AUTHOR]

Recently, as the controllability of complex networks attracts much attention, how to optimize networks’ controllability has become a common and urgent problem. In this paper, we develop an efficient genetic algorithm oriented optimization tool to optimize the controllability of arbitrary networks consisting of both state nodes and control nodes under Popov–Belevitch–Hautus rank condition. The experimental results on a number of benchmark networks show the effectiveness of this method and the evolution of network topology is captured. Furthermore, we explore how network structure affects its controllability and find that the sparser a network is, the more control nodes are needed to control it and the larger the differences between node degrees, the more control nodes are needed to achieve the full control. Our framework provides an alternative to controllability optimization and can be applied to arbitrary networks without any limitations. [ABSTRACT FROM AUTHOR]

Owing to the intermittent solar irradiance from cloud cover in the diurnal period and unavailability at night time, the practical design of a solar system requires energy backup storage for an uninterrupted supply or for off-grid operation. However, for highly efficient CPV (concentrated photovoltaic) system, the literature is lacking for energy management and optimization algorithm and tool for standalone operation. In this paper, a system with CPV and electrolyser is presented where beam irradiance of sunlight is harnessed to convert the instantaneously generated electricity into useful Hydrogen/Oxygen gas, where they can be stored and re-used for downstream applications such as the fuel cells, etc. The multi-variable design and multi-objective optimization strategies are proposed and presented for a standalone operation of the CPV-Hydrogen system as well as their system performances, particularly electrical rating of CPV based upon the real weather data of Singapore. [ABSTRACT FROM AUTHOR]

In this paper, a novel foam-filled ellipse tube (FET) is proposed and compared with other hollow and foam-filled tubes with different cross-sections under multiple loading angles, which include square, circle and rectangle. First, finite element analyses of these tubes reveal that the FET tube has the best crashworthiness under multiple loading angles. Second, design of experiments (DOE) was used to analyze the parameters that radial rate f , thickness of wall t and foam density ρ f . Third, the Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to optimize the FET tube, in which the optimal parameter variation is sought for maximizing specific energy absorption (SEA) and minimizing peak crush force (PCF) under multiple loading angles. The optimized FET tube exhibits better crashworthiness than the origin FET tube and other tubes with different cross-section, indicating that the FET tube can be a potential energy absorber especially under oblique impact loading. [ABSTRACT FROM AUTHOR]

Magnetic sensors are largely used in several engineering areas. Among them, magnetic sensors based on the Giant Magnetoimpedance (GMI) effect are a new family of magnetic sensing devices that have a huge potential for applications involving measurements of ultra-weak magnetic fields. The sensitivity of magnetometers is directly associated with the sensitivity of their sensing elements. The GMI effect is characterized by a large variation of the impedance (magnitude and phase) of a ferromagnetic sample, when subjected to a magnetic field. Recent studies have shown that phase-based GMI magnetometers have the potential to increase the sensitivity by about 100 times. The sensitivity of GMI samples depends on several parameters, such as sample length, external magnetic field, DC level and frequency of the excitation current. However, this dependency is yet to be sufficiently well-modeled in quantitative terms. So, the search for the set of parameters that optimizes the samples sensitivity is usually empirical and very time consuming. This paper deals with this problem by proposing a new neuro-genetic system aimed at maximizing the impedance phase sensitivity of GMI samples. A Multi-Layer Perceptron (MLP) Neural Network is used to model the impedance phase and a Genetic Algorithm uses the information provided by the neural network to determine which set of parameters maximizes the impedance phase sensitivity. The results obtained with a data set composed of four different GMI sample lengths demonstrate that the neuro-genetic system is able to correctly and automatically determine the set of conditioning parameters responsible for maximizing their phase sensitivities. [ABSTRACT FROM AUTHOR]

Caging aims at constraining objects like a bird cage. It does not need force closure and contacts, and therefore is robust to perception noises. This paper considers these merits of caging and proposes an efficient approach to find the optimized finger positions for robust grasping under perception noises by measuring the quality of caging. It initially uses two quality models (the quality model of translational constraint and the quality model of rotational constraint) to combinatorically represent the quality of caging and finds the optimized finger positions (finger positions that have large robustness to perception noises) by maximizing the margins to caging breaking. Meanwhile, it employs Genetic Algorithm (GA) to accelerate the combinatorial optimization of the two quality models. The encoding rules, the initial procedure, and the stopping criterion of the GA are designed carefully towards the quality models to improve its computational efficiency. Experiments with various object shapes show that with the help of multi-model optimization and GA acceleration, the proposed approach is not only robustness to perception noises but also efficient in computation. [ABSTRACT FROM AUTHOR]

With the rapid development of high-speed rail (HSR) systems, the security and safety of these huge systems are becoming the primary concerns for passengers. HSR infrastructure plays an important role in HSR systems, making the maintenance of security and safety of the HSR infrastructure especially important. Meanwhile, sensor network technologies allow the realization of real-time and all-weather monitoring of HSR infrastructure. This paper analyzes the application requirements and characteristics of infrastructure health monitoring sensor network (IHMSN) through construction of a three-layer IHMSN which is composed of end devices, repeater points, and access points. The physical topology optimization goal of IHMSN is to set the optimal number of network nodes (namely, minimum cost) as well as the best physical connections. Given types and amount of the end devices, a multiple knapsack model is established which converts the physical topology optimization problems into multiple knapsack problems. Based on the different needs of practical application, three different cases (basis case, adding devices case and weight-based case) are proposed, and the corresponding models are built. Some artificial intelligence algorithms and a traditional dynamic programming algorithm are presented to solve the problems. In addition, a general algorithmic finite state machine is proposed to describe the solving process. After comparing these algorithms in execution time, memory, and optimal results, the genetic algorithm and particle swarm optimization algorithm stand out when used to solve the basic case as well as the extended cases. The numerical results show that these proposed models and algorithms can effectively solve the physical topology optimization problem of IHMSN for HSR systems. Moreover, these methods can effectively reduce network costs and provide a theoretical basis for network communication link optimization. [ABSTRACT FROM AUTHOR]

Maintenance planning is a critical issue for all heavy industrial sectors such as aeronautics, automobile factories and power plants. The study herein is aimed towards improvement of safety systems reliability in a nuclear power plant. Optimization of maintenance and surveillance test activities is one of the best strategies to improve the reliability of the related safety systems in this kind of plants. Maintenance programs can be formulated in terms of a multi-objective optimization where unavailability, cost and Exposure Time (ET) act as decision criteria and surveillance tests, Allowed Outage Time (AOT) and Preventive Maintenance (PM) intervals act as decision variables. In this paper, Genetic Algorithm (GA) is used to find the best series of answers in the form of Pareto front curve. Sensitivity Index (SI) is applied as a decision making tool to extract the most optimized and promising solution. The unavailability, cost and ET functions, for the most optimal solutions, were reduced by 86%, 58% and 30%, respectively. [ABSTRACT FROM AUTHOR]

In flotation process, the operating conditions under which the concentrate grade is high are the same conditions that lead to a lower recovery and selectivity. Thus, in the face of contradictory, but inherent characteristics of flotation phenomenon, it is necessary to apply appropriate techniques of multi-objective optimization in order to meet specific needs. In the present paper the Firefly Algorithm, associated with the Pareto dominance criterion and with an anti-stagnation operator, is used to determine the best operating conditions of the column flotation of an igneous apatite ore. The aim is to find the conditions that lead to a maximum P 2 O 5 grade and recovery maximization, with adequate selectivity. The results indicated that the proposed approach was able to obtain the operating conditions which led to the best performances for this flotation process. [ABSTRACT FROM AUTHOR]

Elastic recovery is one of the major elements determining sealing performance of the metal seal. Therefore, it is required to increase the elastic recovery for long time operation in extreme environments i.e. high temperature, radiation dose. This paper presents how much elastic recovery can be improved by optimizing the design of the metal O-ring seal without any aids such as an Inconel spring. The shape optimization has been performed based on the result from the topology optimization of the previous study. In order to determine its shape for high elasticity, the cubic B-spline was used and the real coded genetic algorithm (RCGA) was adopted as an optimization algorithm. This study was accompanied with the elasto-plastic analysis to calculate major parameters for performance estimation. Lastly, the elastic recovery and the stored elastic energy rate on the final optimal shape were compared to that of the typical metal O-ring seal. [ABSTRACT FROM AUTHOR]

The power losses reduction is one of the main targets for any electrical energy distribution company. This paper studies the applicability of a control system based on a Genetic Algorithm (GA) on a portion of the actual Italian electric distribution network located in Rome and surroundings, managed by the ACEA Distribuzione S.p.A. The joint optimization of both power factor correction (PFC) and distributed feeder reconfiguration (DFR) is faced. The PFC is performed tuning the phases of the distributed generators (DGs) and the output voltage of the Thyristor Voltage Regulator (TVR). The DFR is performed by opening and closing the available breakers according to a graph based algorithm that is able to find all the possible radial configurations of the network. The joint PFC and the DFR optimization problem are faced by solving a suitable optimization problem, defining the fitness function that drives the GA. In order to have the opportunity to study a realistic future scenario, the actual network has been modified by introducing a few extra distributed generators. Aiming to validate the applicability of the proposed algorithm to an operative scenario, two different tests have been performed. The first one, referred to as time-unconstrained optimization, represents an ideal scenario where there are no constraints on time available for optimization. The second one, referred to as time-constrained optimization, represents a real scenario where the optimization must be completed within a time slot of one hour. Both tests have been performed by feeding the developed simulation tool with real data concerning dissipated and generated active and reactive power values. The comparison between results obtained in the two tests campaigns furnishes the opportunity to evaluate the effectiveness of the proposed control algorithm in real time, relying on the computational performances of an entry-level workstation. The obtained results encourage the use of derivative free methods in a real-time control scenario, showing that the performances achieved by the time-constrained optimization procedures are very close in terms of objective function values to the ones obtained by the time-unconstrained procedure. [ABSTRACT FROM AUTHOR]