Search

Generative Adversarial Networks (GANs) have been proposed as a method to generate multiple replicas from an original version combining a Discriminator and a Generator. The main applications of GANs have been the casual generation of audio and video content. GANs, as a neural method that generates populations of individuals, have emulated genetic algorithms based on biologically inspired operators such as mutation, crossover and selection. This article presents the Deep Learning Generative Adversarial Random Neural Network (RNN) with the same features and functionality as a GAN. Furthermore, the presented algorithm is proposed for an application, the Digital Creative, that generates tradeable replicas in a Data Marketplace, such as 1D functions or audio, 2D and 3D images and video content. The RNN Generator creates individuals mapped from a latent space while the GAN Discriminator evaluates them based on the true data distribution. The performance of the Deep Learning Generative Adversarial RNN has been assessed against several input vectors with different dimensions, in addition to 1D functions and 2D images. The presented results are successful: the learning objective of the RNN Generator creates tradeable replicas at low error, whereas the RNN Discriminator learning target identifies unfit individuals. [ABSTRACT FROM AUTHOR]

• Solve a machine configuration problem of hybrid flow-shop manufacturing network reliability maximization. • Develop the absorbing Markov chain to evaluate the network reliability. • Apply a genetic algorithm to determine the optimal machine configuration. • Demonstrate the applicability and efficiency of the proposed approach by three manufacturing cases. In this study, we adopt a strategy of machine configuration to construct a reliable hybrid flow-shop manufacturing system (HFSMS) with reworking and scrapping actions, in which the reworking and scrapping actions are due to the yield rates of the configured machines. The machine configuration determines the machine suppliers and the number of machines for each production stage in the HFSMS, which may affect the stability of the HFSMS. The machines configured to a production stage in parallel are provided by the same supplier. Accordingly, each production stage has multiple states, following a probability distribution. The HFSMS is a typical stochastic-flow network under machine configuration, and network reliability indicates the probability that order demand d can be fulfilled by the HFSMS and used as a decision reference. Our study integrates genetic algorithm (GA) and absorbing Markov chain (AMC) to solve the reliability-oriented machine configuration problem of HFSMS subject to a configuration budget, where the GA is utilized to find the optimal machine configuration with maximum network reliability and the AMC model constructed based on the yield rates is used for network reliability evaluation. For validating the applicability and computational efficiency of the proposed AMC and GA-based approach, a simple manufacturing system and two practical manufacturing systems are used to compare the proposed approach with four popular meta-heuristic algorithms. The experimental results show that the proposed approach can find the exact optimal solution and has better computational efficiency than the other four meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

The ultrafiltration/diafiltration (UF/DE) process is a crucial step in the canola protein isolation process from rapeseed meal. The process involves using a multi-stage membrane system to separate components of the mixture. As diafiltration dilutes the feed stream in the ultrafiltration system, a large amount of diafiltration water is required. Reducing the diafiltrate for sustainability reasons can be done by carefully selecting process variables or using recycle streams. However, finding the optimum process variables can be a meticulous process if performed experimentally or via trial and error. In this study, we performed an optimization using a mechanistic model integrated with a genetic algorithm to aid in finding an optimum combination of process variables. The mechanistic ultrafiltration model was derived by taking into account transport phenomena within the filtration system. Parameters were characterized experimentally in term of viscosity coefficient, membrane resistance, cake porosity, aggregate diameter equivalent, and material compressibility factors. Using the mechanistic model-based optimization in combination with actual experimental values, the performance of a fourstage UF/DF system could already be improved despite fixing the configuration, albeit at the cost of a reduction in purity. Further improvement could be achieved by using recycle streams. The optimized system achieved a diafiltrate reduction of up to 79%, an increase of purity of up to 31%, and an increase of dry matter content of up to 18%, while maintaining the product purity of the reference set-up. [ABSTRACT FROM AUTHOR]

• Dendrite morphological neuron learning is based on deterministic linkage trees. • Linkage trees overcome k -means' non-reproducibility in learning dendrites' locations. • A micro genetic algorithm speeds up the automatic hyperparameter tuning of models. • A balanced fitness function favors solutions with a low error rate and few dendrites. • The proposed approach beats its counterparts related to accuracy and model complexity. The current learning approach for smooth dendrite morphological neurons (DMNs) determines dendrite parameters using k -means clustering, which is non-reproducible due to its stochastic nature, risking falling into local minima. To overcome this issue, we introduce a DMN learning approach based on a deterministic hierarchical clustering method, which builds a linkage tree for each class of patterns. In addition, a micro genetic algorithm automatically tunes the cut-off points in the linkage trees hierarchy to create suitable clusters of dendrites. The classification experiments consider 40 real-world datasets. The proposed approach outperforms three DMN models in classification performance and is quite competitive with a hybrid morphological-linear perceptron, multilayer perceptron, random forest, and support vector machine. Therefore, the proposed method is a suitable alternative for pattern classification applications. [ABSTRACT FROM AUTHOR]

The quadrotor represents a class of highly nonlinear dynamic systems and its controllability features are challenging. Hence, it serves as an ideal unmanned aerial vehicle platform to validate many artificial intelligence-based research investigations. Nonetheless, most of the offline tuning approaches devote efforts to find near optimal control gains to regulate individually the decoupled motion directions. This work adopts a multi-objective self-adjusting search mechanism to actuate the motions of a quadrotor via deciding the control gains of the interacting loops simultaneously. This algorithm employs a first order low pass filter transfer function as an accepting approach for the tunning mechanism. The proposed approach is compared with a Genetic Algorithm and another nonlinear Proportional-Integral-Derivative approach to highlight the usefulness of the proposed mechanism. It was founded that quadrotor follows the desired trajectory with a small tracking error of less than 2% in the X-Y plane and less than 1 % error tracking error in the altitude Z. Also, it is recorded that MONLTA can overcome the simulated wind disturbances of 0.1 N.m as a disturbance torque. [ABSTRACT FROM AUTHOR]

Memetic Algorithm Based Routing in Wireless Sensor Networks (WSNs) is an innovative approach for optimizing data transmission routes within a network of interconnected sensors. Traditional routing algorithms often fail to consider the dynamic and resource-constrained nature of WSNs, resulting in inefficient energy utilization and decreased network lifetime. By incorporating principles of natural selection, crossover, and mutation from genetic algorithms, along with local search methods, memetic algorithms offer a more adaptive solution. This research aims to implement a memetic algorithm to find the optimal routing paths while minimizing energy consumption and maximizing data delivery ratio. Preliminary results indicate a significant improvement in the network's overall performance metrics, including a longer lifespan and higher data reliability, compared to traditional routing methods. [ABSTRACT FROM AUTHOR]

Recently, transformer models have gained interest with their varied range of natural language applications in several domains like education, healthcare, entertainment, and the like. Design of inquisitive bots which ask questions on a given topic of interest is one such application. To improve the quality of questions generated by such a bot while leveraging the power of transformer models, it is necessary to add a component to the pipeline that also considers the topic of discussion. This research examines transformer models pre-trained on natural language with the addition of topic context and subsequent optimization using genetic algorithms to generate more relevant questions. In our hybrid framework, we first use topic modeling for extracting context from the conversation history and then use these topics as input in the conversational training pipeline for generating questions on the given category. We attempt to fine-tune the framework to find the optimized weights using genetic algorithm. Our experiments are carried out using a large-scale dataset that is readily available to the public for the purpose of conversational question generation. We further evaluate the trained models on a dataset of Java passages performing qualitative analysis. Our analysis suggests that utilizing the genetic algorithm-optimized topic aware mechanism improves the quality of generated questions substantially in the conversational set-up. [ABSTRACT FROM AUTHOR]

Thermal stratification widely exists in large reservoirs, causing significant inflow-outflow temperature difference (IOTD), which goes against the downstream ecology. Reservoir optimal operation has recently been conducted to regulate the outflow temperature, while its effects for cascade reservoirs urgently needed to be illuminated. Taking the typical Xiluodu-Xiangjiaba cascade reservoirs (XXCR) as study case, this paper had built a multi-objective optimal operation model combing the non-dominated sorting genetic algorithm II (NSGA-II) and a hydrodynamic-temperature model, and the total power generation (TPG) and IOTD were simultaneously considered. The effects of different inflow discharge and global warming scenarios were analyzed. This paper showed the following: (1) The IOTD and TPG of XXCR showed a positively linear relationship with the water level of the superior Xiluodu Reservoir (XLD), instead of the inferior Xiangjiaba Reservoir (XJB). (2) The reservoir with larger water volume and depth (such as XLD) determined the thermal structure and hydropower generation in cascade reservoirs, compared with the inferior reservoir (such as XJB). (3) As the inflow discharge or air temperature increased, the slope between the IOTD and TPG in the Pareto front curve increased. The results of this paper could support the ecological operation for cascade reservoir to regulate the outflow temperature. [Display omitted] [ABSTRACT FROM AUTHOR]

The proton exchange membrane electrolyzer (PEME) is a promising tool for hydrogen production, and internal two-phase transport significantly influences its performance. In this study, a two-phase analytical PEME model incorporating the liquid saturation jump effect was developed, and intelligent parameter estimation using a genetic algorithm was proposed to achieve high-efficiency model validation. In-house experiments and experimental results from numerous papers in the literature were employed to prove the effectiveness of the proposed intelligent parameter estimation. Moreover, the two-phase simulation results demonstrated that the PEME voltage increased significantly when the current density reached the limiting value, and the liquid saturation in the anode catalyst layer (ACL) dropped to nearly zero. Increasing ACL porosity, decreasing ACL permeability, and decreasing ACL thickness could increase the limiting current density within the investigated range. The simulated limiting current density could be > 5 A cm−2 through proper design of the ACL parameters. For high-pressure cathode operation, increasing the cathode pressure and membrane permeability generally benefits water management inside the PEME and therefore increases the limiting current density. This study provides critical support for the design of cells and operating conditions for future PEME studies. [ABSTRACT FROM AUTHOR]

Effective project planning is crucial for the successful execution of complex industrial projects, as it can significantly reduce costs and enhance overall efficiency. However, applying precise methods for obtaining optimal project plans is often limited to small activity graphs, rendering them impractical for real-world scenarios. To address this issue, heuristic and metaheuristic algorithms have been the focus of extensive research over the past few decades. This study explores an extended problem formulation known as the Multiple Contractor-Resource Project Scheduling Problem (MC-MRCPSP). This formulation extends beyond the basic problem structure, incorporating various constraints typically encountered in industrial project scheduling. To tackle this challenging optimization problem, we propose a novel hybrid algorithm that combines a fine-tuned genetic algorithm with an advanced tabu search. This approach strikes an optimal balance between exploration and exploitation of the solution space, leading to enhanced project plan optimization. Through comprehensive experiments and evaluations, we demonstrate the effectiveness of our proposed approach. Compared to a purely genetic algorithm, our hybrid solution achieves a 38% improvement in project plan optimization. These results underscore our study's significant contribution to project planning and lay the groundwork for future advancements in MC-MRCPSP solutions. Ultimately, our research brings us closer to improved resource utilization, cost reduction, and enhanced efficiency in managing complex industrial projects. [ABSTRACT FROM AUTHOR]

Methyl carbonate (EMC) and Diethyl carbonate (DEC), as excellent solvents for lithium-ion electrolytes, are synthesized by transesterification, and consecutive reaction seriously complicate optimization and control issues. Process synthesis, parameters optimization and advanced control are crucial points that should be addressed for this ill-conditioned and highly nonlinear system, which is the motivation and novelty of present work. With single-objective and multi-objective genetic algorithm, intensified processes for EMC and DEC production were optimized respectively with heterogeneous catalyst. And reactive dividing wall column achieved 23% and 13% total annualized cost reductions compared with reactive distillation for two target carbonates. Based on optimal steady-state solution, intelligent fuzzy logic and model predictive control of EMC and DEC production is conducted to clarify their respective strengths in intensified process with consecutive reaction. Sequential optimization for parameters of FLC was carried out with Integral of Squared Error (ISE) as objective. Finally, control performance of FLC and LPMC are compared with that under PID control, and results show that overshoot and transition time under FLC and LMPC are obviously mitigated, along with evident reduction in ISE. [Display omitted] • Process intensification realized for high-purity EMC and DEC production by embedding heterogeneous RD and RDWC. • Optimization carried out with effective single or multi objective genetic algorithm. • Intelligent fuzzy logic control and model predictive control developed for series intensified processes. • FLC and MPC shows superiority for controlling ill-conditioned system with high nonlinearity. [ABSTRACT FROM AUTHOR]

Biohybrid circuits of interacting living and model neurons are an advantageous means to study neural dynamics and to assess the role of specific neuron and network properties in the nervous system. Hybrid networks are also a necessary step to build effective artificial intelligence and brain hybridization. In this work, we deal with the automatized online and offline adaptation, exploration and parameter mapping to achieve a target dynamics in hybrid circuits and, in particular, those that yield dynamical invariants between living and model neurons. We address dynamical invariants that form robust cycle-by-cycle relationships between the intervals that build neural sequences from such interaction. Our methodology first attains automated adaptation of model neurons to work in the same amplitude regime and time scale of living neurons. Then, we address the automatized exploration and mapping of the synapse parameter space that lead to a specific dynamical invariant target. Our approach uses multiple configurations and parallel computing from electrophysiological recordings of living neurons to build full mappings, and genetic algorithms to achieve an instance of the target dynamics for the hybrid circuit in a short time. We illustrate and validate such strategy in the context of the study of functional sequences in neural rhythms, which can be easily generalized for any variety of hybrid circuit configuration. This approach facilitates both the building of hybrid circuits and the accomplishment of their scientific goal. • We ease the construction of biohybrid circuits between living and model neurons. • We explore and map the parameter space that lead to a specific target dynamics. • A genetic algorithm readily yields instances of the target biohybrid dynamics. • We illustrate this approach in the context of biohybrid dynamical invariants. • We validate the strategy for coordination of functional sequences in neural rhythms. [ABSTRACT FROM AUTHOR]

Fast Flux Botnet (FFB) is an advance method developed by cyber criminals to perpetrate distributed malicious attacks. The major problems of existing FFB detection systems are the vulnerability to evasion mechanisms, long detection time, and high dimensionality of the feature set. In this study, an improved FFB detection architecture called Bot-FFX was developed to address some of these problems. The developed Bot-FFX consists of four modules: extractor, filter, resolver, and detector. The extractor module is responsible for Domain Name System (DNS) queries on domains. The filter module can classify the incoming domains as either blacklist or whitelist and sends the unclassified domains to the resolver. The resolver extracts all IP addresses associated with the domain at its Time-To-Live (TTL) within a time frame of 10 min. The detector module uses a rule-based Genetic Algorithm (GA) and K-Nearest Neighbor (KNN) for botnet detection. The detector computed the Standard Deviation of Round Trip Time (SDRTT), Average Google Hits (AGH) and Genetic Threshold Value (GTV) for all IP addresses associated with the domains. The detector, built on a decision tree rules and the K-Dimensional (KD) tree KNN algorithm, classified the domains using the set of IP addresses, SDRTT, AGH, and GTV. The Bot-FFX was implemented on a dataset of 2,000 benign domains and 1,630 botnet domains. The dataset was split into 50% training and 50% testing sets. The evaluation results on the same datasets showed that Bot-FFX is an effective FFB detection system with accuracy, false positive, and false negative of 99.178%, 0.8%, and 0.8% respectively. [ABSTRACT FROM AUTHOR]

The role of cost, time and labor intensity in the construction process is very important, but there is a problem of poor optimization effect. The standard genetic algorithm cannot solve the problem of index optimization in the construction process, and the optimization rationality is low. Therefore, this paper proposes an improved genetic algorithm to study the objectives of construction projects. Firstly, the k-value clustering theory is used to analyze the characteristic values of the index and divide them according to the changing trend to reduce the disturbance factors in optimization. Then, the k-value clustering theory is used to search for the child-parent node of the indicator to form the final optimization result and complete the multi-objective optimization. MATLAB simulation shows that under certain optimization criteria, improving the genetic algorithm can improve the rationality of multi-objective optimization, and the optimization time is shorter than that standard improves genetic algorithms. [ABSTRACT FROM AUTHOR]

Intelligent machines are an important carrier of artificial intelligence, which are widely used not only in the industrial field, but also in various fields of daily life and work. Path planning is an important research direction for studying machine intelligence. The main purpose is to search for a feasible path so that the robot can walk along the searched path in a complex environment. This paper aims to study the evolutionary algorithm of intelligent machines based on artificial intelligence. On the basis of introducing the characteristics of artificial potential field algorithm, the concept and characteristics of genetic algorithm, using genetic algorithm as the framework, the artificial potential field algorithm is combined with the crossover of genetic algorithm. In the operator, the optimization performance of the crossover operator is improved. The research results show that the length of the path obtained by the improved algorithm is shorter and better than that of the genetic algorithm of the single-point crossover operator. [ABSTRACT FROM AUTHOR]

Packaging design, as an important element in product appearance, can directly affect customers' sensory perception of the product. Many universities even offer packaging design majors, which mainly use natural science and aesthetic knowledge to promote product sales. However, many old brands remain complacent and their packaging design still adopts traditional thinking, which to some extent affects their sales. Therefore, this article decided to use genetic algorithms as a tool to parameterize the 3D fractal images in packaging design, aiming to create more creative and eye-catching packaging designs. At the end of this article, an experiment was conducted on two branches of a certain brand. Branch 1 tried out the new design provided in this article, while Branch 2 continued to use the original design. After Branch 1 fully adopted the design, sales skyrocketed, from the original daily sales of 50-60 units to 70-85 units. Branch 2 remained unchanged, with a sharp contrast. [ABSTRACT FROM AUTHOR]

With the widespread use of substation intelligent simulation systems, existing substation intelligent simulation systems have problems such as platform redundancy, system dispersion, and information sharing, which can lead to the insecurity of substation intelligent simulation systems. Therefore, this paper proposes a security operation analysis of substation intelligent simulation system based on improved genetic algorithms, aiming to improve the security of substation intelligent simulation system by using improved genetic algorithms. This paper experimentally tests that the number of vulnerabilities in a substation intelligent simulation system using improved genetic algorithms is between 0 and 20 within a month, while the number of vulnerabilities in a traditional substation intelligent simulation system is between 30 and 50, indicating that improved genetic algorithms do have the effect of improving the safety coefficient of the substation intelligent simulation system. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet leads to the rapid growth of network information, we call it information explosion. The Internet is full of information, and it is difficult for users to find this information and useful knowledge of the ocean. The Web has become the world's largest information repository, and there is an urgent need for efficient access to the valuable knowledge of vast amounts of web information. The purpose of this paper is to study the process and algorithm analysis of text mining system based on artificial intelligence. This paper presents an algorithm of document feature acquisition based on genetic algorithm. Selecting suitable features is an important task in specific text classification and information retrieval. Finding appropriate feature vectors to represent the text will undoubtedly help with subsequent sorting and grouping. Based on the genetic algorithm of variable length chromosome, this paper improves the crossover, mutation and selection operations, and proposes an algorithm to obtain text feature vectors. This method has a wide range of applications and good results. [ABSTRACT FROM AUTHOR]

To achieve the goal of exploiting all the advantages and avoiding the disadvantages, this paper presents a genetic algorithm-based development to integrate the spider colony algorithm for teaching optimization of English data recommendation methods. This algorithm first takes advantage of the genetic algorithm to extract the pheromone required by the ant colony algorithm, and then obtains a good solution through the fast and comprehensive integration mechanism of the global optimization of the ant colony algorithm. Therefore, combining the two algorithms can increase the processing time and efficiency. According to the experimental results, compared to using the genetic algorithm or the ant colony algorithm, the violation of the soft parameters of the training data based on the genetic ant hybrid algorithm is reduced to a minimum, and the feasibility and quality of the training data are good. alone. The power of our algorithm is directly proportional to the number of assignments. The security of the algorithm seems to be stable when the number of scales reaches one. For the same scale, the power of the hybrid genetic colony algorithm is the highest, the ant colony algorithm is the second, and the genetic algorithm is the last. When the class setting is 500, when the motor reaches 300, the algorithm stops working and the test is repeated 10 times. The genetic ant colony hybrid algorithm needs the shortest time to reach the fitness value. Conclusion: this algorithm is much better than genetic algorithm or spider colony algorithm. [ABSTRACT FROM AUTHOR]

The increase in computer numerical control machine efficiency highly contributes to environmental emission reduction and energy-savings. Path and trajectory optimizations are used to improve machine efficiency in a coverage motion such as pocket milling, polishing, inspection, gluing, and additive manufacturing. Several studies have proposed coverage motion optimization in improving machine efficiency for time and energy consumption. Ensuring the smoothness and satisfaction of the machine constraints in coverage motion is necessary. This paper proposes a multi-objective path and trajectory optimization to obtain a trade-off between time and energy consumption for coverage motion. Jerk limited acceleration profiles describe the trajectory where velocity profiles generated for each linear segment attain desirable velocities. The energy model of an industrial two-axis feed drive system is used in finding solutions to the optimization problem. The non-dominated sorting genetic algorithm II generates a Pareto front for trade-off time and energy consumption solutions. Simulation results of the proposed method are validated through experiments using the industrial two-axis feed drive system. Experimental results show the effectiveness of the proposed approach where time reduction and energy savings are 10.05% and 2.10%, respectively. In addition, the optimized path has a lower maximum error of 76.6% compared to the constantly commanded velocity optimized path. • A method of simultaneous trajectory generation and path optimization is proposed. • Jerk limited acceleration profiles describe the trajectory. • The NSGA II is used to find the trade-off between time and energy consumption. • The best trade-off solution achieves time reduction and energy savings of 10.05% and 2.10%. • Optimized path motion with variable velocities reduces maximum error by 76.6%. [ABSTRACT FROM AUTHOR]

On Flores island (Azores, Portugal), energy production depends up to 47% on fossil fuels, namely Diesel. To minimize CO 2 emissions, the dependency on fuel prices, and to mitigate the consequences of variability and intermittency of renewable energy sources, a new energy system, based on H 2 storage was analysed. To achieve the optimal size of the system, a computer model was developed and a multi-objective genetic algorithm function was used to minimize three objectives: the difference of levelised cost of energy (△ LCOE), CO 2 emissions and the percentage of renewable energy dumped (R RES, dump). From the set of solutions obtained, one that meets R RES, dump ≤ 1%, lowest CO 2 emissions and lowest △ LCOE is chosen and an economical and energetic analysis is performed. The newly proposed system reduces Diesel consumption by 68,7% (1057487 L/year) and CO 2 emissions by 65,9% (2455,1 CO 2 tons/year) achieving a renewable energy sources (RES) penetration of 89% (36% increase), but fails to decrease the levelised cost of energy (56,62 €/MWh increase). However, a way to make the project viable through financial support is presented and an alternative to reduce the levelised cost of energy by commercialising the products of electrolysis, hydrogen and oxygen, is proposed. Finally, it is expected that with further research and development of H 2 technologies, economic and energetic results will get more advantageous, opening up new perspectives for the future. [Display omitted] • Development of a control system to simulate the energy system of an island. • Optimum design of a H 2 energy storage system. • Utilization of a multi-objective genetic algorithm (MOGA). • Economic and energetic analysis of a H 2 energy storage system. • H 2 energy storage system increases RES energy in over 35%. [ABSTRACT FROM AUTHOR]

The fuzzy proposed system is a multigeneration system capable of producing different multi-products including power, cooling, heating, and fresh water. The energy required for the operation of the system is geothermal renewable energy, which is a very serious issue in the green transient. To analyze the function of the suggested subsystems, such thermodynamic rules have been considered as an important tool. Another important parameter that is evaluated is the entransy parameter that evaluates the function of thermodynamic cycles. In this study, the rate of G ˙ l o s s is achieved at 699699 KW.K. The results of the suggested system demonstrate the rate of electrical power is 226.1 KW, the rate of cooling load is 24.5 KW, freshwater production is 0.235 kg/s, and the heating load is 418.5 KW of the system output products. In addition, energy efficiency and exergy are calculated as 59.51% and 51.5%, respectively. To provide an optimal performance state of the system, a multi-objective optimization method with a genetic algorithm method is used to optimize the objective functions ( G ˙ l o s s and η I I ) of Matlab software. In this research, it has been attempted to reduce entransy and increase exergy efficiency using algorithm genetic (GA). [ABSTRACT FROM AUTHOR]

The transport sector as a whole plays an important role in the economy of Bangladesh as its contribution to gross domestic product (GDP) stood at 10.98 percent in the fiscal year (FY) 2018-19 with a growth rate of 6.88 percent. However, this sector also accounted for 9.92 percent of the country's total greenhouse gas (GHG) emissions in 2020. This GHG emission is projected to grow by 116 percent by 2030, making decarbonization of transport an urgent priority by uptaking the electric vehicle (EV). Hence, the Government of Bangladesh is presently working towards issuing an EV policy to remove the barriers to accommodate this paradigm shift in the transportation sector. In order to develop an effective policy for scaling up EV uptake in Bangladesh, systematic and detailed forecasting needs to be carried out considering various influencing parameters. In this study, an EV outlook for Bangladesh from 2022 to 2040 has been formulated by applying the concepts of the additive linear programming mechanism. The predictive modelling includes linear growth of ICE vehicles, EV price reduction, and inclusion of carbon tax on ICE cars as contributing factors in this paper. It has been estimated that, with the inclusion of a 40% carbon tax, the forecasted number of EVs would reach around 3,000,000 in the year 2040. The outlook has led to a comparative financial analysis between ICE vehicles and EVs through the establishment of financial modelling. The genetic algorithm optimizes total tax incidence (TTI) that limits the payback period, and hence, reduces the price gaps between EVs and ICE vehicles. Based on the findings of the study, a number of policies are recommended on fiscal and financial incentives to pave the way for increased uptake of EVs in Bangladesh. • Electric vehicle outlook and policy formulation for decarbonizing the Bangladesh transport sector. • Factors such as growth, the global price of EVs, and the carbon tax for fossil fuel vehicles are considered. • Calibrated outlook has led to comparative financial analysis between fossil fuel vehicles and EVs. • A genetic algorithm is applied to optimize the total tax incidence (TTI) related to the investment payback period. • Policy recommendations are formulated to pave the way for increased uptake of EVs in Bangladesh. [ABSTRACT FROM AUTHOR]

Sizing optimization and power smoothing methodology in highly fluctuating grid-connected renewable systems represent important challenges nowadays, the total cost and energy quality depend on the effective balance between source and load with certain restrictions to increase reliability and enhance the use of renewable sources at all scales. This article presents a novel power smoothing effect and sizing optimization methodology for a grid-connected photovoltaic-hydrokinetic system by pattern search method comparatively with three optimization algorithms encompassing a multi-objective optimization. In this context, the method considers the reduction of power fluctuations using supercapacitors, where the objective function considers technical and economic indexes. The main contribution of the research is that the objective function decreases the fluctuation suppression ratio to sizing optimization of renewable system through three pattern search size optimization algorithms. Experiments have shown that pattern search method can find the global optimum by reducing the computational effort; the Latin hypercube algorithm reduces the photovoltaic capacity by approximately 5 % and 2.3 % with respect to the Nelder Mead and genetic algorithm respectively. The influence of the supercapacitor successfully reduces power fluctuations and causes an increase in self-consumption of 32.32 %, this result represents an annual cost savings of 42 % on energy purchased from the grid. Furthermore, this paper goes further by calculating the optimal location of a hydrokinetic turbine in a river using the HEC-RAS software to determine with high precision the HKT power fluctuations as input signal to novel proposed method. [Display omitted] • Hybrid supercapacitor-integrated dispatch model with analysis of pattern search method. • Dispatch-coupled sizing renewable sources and energy storage with power smoothing, improving energy quality. • Numerical analysis on different size optimization methods comparatively considering experimental data. • An improved pattern search optimization that exceeds the standard one. • Validation of the effectiveness of the proposed framework in a laboratory microgrid test system. [ABSTRACT FROM AUTHOR]

Hyper-heuristics can be applied to solve complex optimization problems. However, they need a substantial number of fitness function evaluations to discover a good approximation to the global optimum, especially for large-scale problems. Recently, surrogate-assisted algorithms have drawn increasing attention, and have shown their potential to deal with expensive complex optimization problems. This paper aims to use surrogates to approximate HHGA's (Ahmed Bacha et al., 2019) fitness functions, an efficient hyper-heuristic for solving the permutation flowshop problem, one of the most important scheduling types in modern industries. The objective is to approximate, in an online approach, the fitness function, reducing considerably the execution time of HHGA while maintaining its quality. The proposed online surrogate model is mainly designed to capture the details of the fitness function to enhance the accuracy estimation. The experimental results on Taillard's widely used benchmark problems show that the proposed fitness approximation-assisted HHGA is able to achieve competitive performance on a limited computational budget. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the results of two hyper-heuristics HHGA and HHabs that generate per-instances genetic algorithms for the permutation flow shop problem. They are competitive with literature approaches for most of instances of the benchmark of Taillard. Nevertheless, they are not effective enough for some difficult instances. For this purpose, we propose a workflow to analyse GAs configurations and their results in order to detect which components influence the most on generated GAs quality in order to enhance the quality of these hyper-heuristics. [ABSTRACT FROM AUTHOR]

In recent years, the negative impacts of neglecting the environment, particularly global warming caused by greenhouse gases, have gained attention. Many countries and organizations are taking steps to reduce their greenhouse gas emissions and promote sustainable practices. In this paper, we aim to address the gap in the classical Vehicle Routing Problem (VRP) by taking into consideration the environmental effects of vehicles. To find a balance between cost-efficiency and environmental impact, we propose a Hybrid Genetic Algorithm (HGA) to address the Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) and a heterogeneous fleet, taking into account new orders that arrive dynamically during the routing process. This approach takes into consideration the environmental effects of the solutions by optimizing the number and type/size of vehicles used to fulfill both static and dynamic orders. The goal is to provide a solution that is both cost-effective and environmentally friendly, addressing the issue of over-exploitation of energy and atmospheric pollution that threaten our ecological environment. Computational results prove that the hybridization of a genetic algorithm with a greedy algorithm can find high-quality solutions in a reasonable run time. [ABSTRACT FROM AUTHOR]

A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations. [ABSTRACT FROM AUTHOR]

Granting credit involves certain risks and thus financial institutions try to minimize them using various methods. The aim of this article is to develop a method of selecting borrowers' characteristics for credit risk assessment. The article uses non-genetic algorithms (such as CFS, ReliefF or Lasso), that perform calculations for all features, with only financial institutions granting the loan selecting the significant features to be assessed. The Holland and NSGA II genetic algorithms also begin with all features and gradually reduce their number in the consequent iterations without compromising the accuracy of feature selection. Only the GAAM genetic algorithm gives the lender the ability to choose the number of features entered into the calculations. During the operation, this algorithm increases the accuracy of feature selection by reducing their number. This significantly shortens the time to analyse loan application while maintaining acceptable accuracy, which results in making the right decision quickly. The features selected by the algorithms are additionally classified by the LDA classifier to demonstrate the correct selection of features by a given algorithm compared to the benchmark, which is the actual percentage of credits granted and then repaid. [ABSTRACT FROM AUTHOR]

The string site-removal problem (SSRP) originates from the field of computational biology and is known to be NP-hard. The problem is defined on the set of strings of equal length and an integer k, and its goal is to find the largest subset of the strings that become identical after removing at most k sites (columns). This paper proposes two new approaches to SSRP. The first is an exact algorithm that uses the quadratically constrained program (QCP). The second approach combines QCP and genetic algorithm (GA). In a series of experiments, we showed that our exact program performs faster on average than the state-of-the-art method (Integer Linear Programming, ILP). Regarding heuristic approaches, our new proposal gives the identical results as the state-of-the-art reference method (Large Neighborhood Search, LNS) with the same CPU time budget. All source codes and benchmark instances we made publicly available via GitHub. [ABSTRACT FROM AUTHOR]

Natural Language Processing (NLP) systems have, over the past decade, shifted from using rule-based techniques to using machine learning-based algorithms. This has led to the development of different architectures and models for different tasks. Some of these architectures include models like the transformers, the CNN and the RNN, which have now become ubiquitous in NLP. However, designing these neural network architectures usually requires in-depth analysis and knowledge of multiple domain areas involved with the problem at hand. In our work, we evaluate an alternative solution to this problem in the domain of text classification. Here, we suggest using the Genetic Algorithm with gradient descent (GAGD) and NeuroEvolution of Augmenting Topologies (NEAT) to search for an optimal neural architecture for the Reuters-21578 and 20 Newsgroups datasets. We evaluate and compare the results of the two algorithms against the current state-of-the-art architectures and provide insight into their performance. [ABSTRACT FROM AUTHOR]

The performance and power of Wavelet Neural Networks (WNNs) rely on the proper structure of the WNN. In this study, a hybrid approach is suggested to build a Wavelet Neural Network. We design a network based on the genetic algorithm (GA), the Gram-Schmidt Selection (GSS) method, and the multilibrary wavelet function (MLWF). The initialization of the WNN is performed by the GSS method, which aims to select the candidate library wavelet functions (MLWF) to develop the WNN. The GA is used to solve the structure and the learning of the WNN and the GSS algorithm is applied to select the important wavelets. The GA was used to calculate the suitable values of the Network parameters, to solve the structure and learning of the WNN. In this research, the GSS method is used to determine a set of best wavelets whose centres and dilation parameters are used as initial values for subsequent training. Experimental tests proved that the proposed approach is very efficient and accurate. [ABSTRACT FROM AUTHOR]

The aim of this research is to develop continuous reactor network models to determine the reactor configuration and corresponding optimal operating conditions of a polyolefin elastomers polymerization process. To achieve this objective, a modified vapor-liquid equilibrium model is integrated with both the continuous stirred tank reactor (CSTR) model and the plug flow reactor (PFR) model to accurately predict the vaporization rates of liquid. The multi-objective problem is formulated to maximize the monomer conversion and minimize the total annualized cost simultaneously. To solve the multi-objective problem, the genetic algorithm is utilized to identify the optimal operating conditions. Furthermore, the impact of the reactor combination mode is analyzed by varying the number and sequence of CSTR or PFR. The research findings reveal that a multi-phase CSTR without circulating stream followed by a PFR is the optimal reactor configuration. The proposed configuration can enhance reactor productivity and reduce the total annualized cost simultaneously while meeting the desired polymer properties. [Display omitted] • Optimization for a polyolefin elastomers polymerization process is performed. • Modified vapor-liquid equilibrium model is adopted in reactor simulation. • The genetic algorithm is applied in solving multi-objective optimization problem. • Reactor configuration and corresponding optimal operating conditions are obtained. [ABSTRACT FROM AUTHOR]

Air source heat pump is recognized as an effective environmental protection and energy-saving technology. The heating temperature lift is one of the key factors affecting the performance of heat pump systems. In this paper, an in-depth quantitative thermodynamic comparison of cascade heat pump, recuperative heat pump and CO 2 heat pump for direct-heating and space heating was conducted. Aspen HYSYS was used to invoke advanced genetic algorithm in MATLAB to optimize the system parameters. The temperature matching in heat exchangers was employed to reveal the essence of the performances of different heat pump systems. The recuperative heat pump is more suitable for direct-heating with the temperature lift 60 °C, and its COP is up to 4.81 at the ambient temperature of 20 °C. The cascade heat pump performs best in space heating with the maximum COP of 2.47. The exergoeconomic factor of the compressor is less than 7%, which illustrated that the investment in compressor should be increased to further improve the system efficiency. The annual CO 2 and SO 2 lowest emissions is 4431 kg and 130 kg of recuperative heat pump for direct-heating, and the cascade heat pump has the lowest annual 4234 kg CO 2 and 127 kg SO 2 emissions for space heating. [ABSTRACT FROM AUTHOR]

The positioning stage composed of piezoelectric stacks and flexible mechanisms is highly competitive in the field of micro/nanomanipulation. Driving stroke and positioning accuracy are the two most important factors to evaluate the positioning stage. However, the attenuation of the driving stroke caused by the stiffness load of the flexible mechanism is an important but neglected problem. On the other hand, the hysteresis of piezoelectric ceramics directly affects the positioning accuracy of the platform. In this study, a two-degrees-of-freedom positioning stage with low displacement attenuation and hysteresis is proposed. Static and dynamic models of the positioning stage were established to derive analytical models of the displacement amplification ratio, structural stiffness, and natural frequency. The displacement transfer coefficient was introduced to establish an equivalent physical model for structural stiffness and displacement attenuation. Genetic algorithms were used to optimize the structural parameters. Finite element analysis is performed to verify the accuracy of the analytical model. The hysteresis is suppressed by the combination of bidirectional piezoelectric driving and inverse Bouc-Wen model feedforward compensation. The measurement results indicated that the proposed positioning stage had a displacement transfer coefficient of 0.82, which was 95.89% lower than the hysteresis of the piezoelectric stack. [Display omitted] • Performance study of piezoelectric micropositioning stages. • The attenuation of structural stiffness on the working stroke is considered. • Structural parameters optimized by Genetic Algorithm. • Hysteresis is suppressed by combining structural design and feed-forward compensation methods. [ABSTRACT FROM AUTHOR]

It is very important to identify the thermal behaviors of semi-batch reactors (SBRs) in isoperibolic operating mode. First, a default bilayered back propagation neural network (BPNN) is selected from multiple recognition algorithms to achieve this task, which is suitable for three kinds of reactions with arbitrary reaction orders: homogenous, kinetically−controlled, and diffusion−controlled liquid−liquid heterogeneous reactions. Then, it is further optimized by Bayesian regularization and genetic algorithm (GA). The result shows that the optimal bilayered BPNN undoubtedly has better recognition accuracy and generalization performance. It is found that the accuracy of the two test sets are 98.8% and 100%, respectively. Subsequently, a simple and user-friendly standalone desktop application program (app) is designed to easily use the developed optimal bilayered BPNN. Finally, a case of acetic anhydride hydrolysis reaction with acetic acid solvent is studied to prove the good validity of the bilayered BPNN and the great reliability of the standalone desktop app. [ABSTRACT FROM AUTHOR]

Breast cancer is common among women resulting in mortality when left untreated. Early detection is vital so that suitable treatment could assist cancer from spreading further and save people's life. The traditional way of detection is a time-consuming process. With the evolvement of DM (Data Mining), the healthcare industry could be benefitted in predicting the disease as it permits the physicians to determine the significant attributes for diagnosis. Though, conventional techniques have used DM-based methods to identify breast cancer, they lacked in terms of prediction rate. Moreover, parametric-Softmax classifiers have been a general option by conventional works with fixed classes, particularly when huge labelled data are present during training. Nevertheless, this turns into an issue for open set cases where new classes are encountered along with few instances to learn a generalized parametric classifier. Thus, the present study aims to implement a non-parametric strategy by optimizing the embedding of a feature rather than parametric classifiers. This research utilizes Deep CNN (Deep Convolutional Neural Network) and Inception V3 for learning visual features which preserve neighbourhood outline in semantic space relying on NCA (Neighbourhood Component Analysis) criteria. Delimited by its bottleneck, the study proposes MS-NCA (Modified Scalable-Neighbourhood Component Analysis) that relies on a non-linear objective function to perform feature fusion by optimizing the distance-learning objective due to which it gains the capability of computing inner feature products without performing mapping which increases the scalability of MS-NCA. Finally, G-HPO (Genetic-Hyper-parameter Optimization) is proposed. In this case, the new stage in the algorithm simply denotes the enhancement in the length of chromosome bringing several hyperparameters into subsequent XGBoost, NB and RF models having numerous layers for identifying the normal and affected cases of breast cancer for which optimized hyper-parameter values of RF (Random Forest), NB (Naïve Bayes), and XGBoost (eXtreme Gradient Boosting) are determined. This process helps in improvising the classification rate which is confirmed through analytical results. [ABSTRACT FROM AUTHOR]

• The distribution of equilibrium points and bifurcation characteristics were studied. • The bifurcation angle range and the safe basin of the system are proposed as the optimization objectives. • Competitive Pareto parameter optimization solutions were obtained through the sensitivity analysis and algorithm NSGA-II. • Optimization of the structure parameters can remarkably improve the stability. The main structural parameters that affect vehicle bifurcation characteristics and the safe basin are obtained, with the goal of improving the stability. First, the distribution of the system equilibrium points in three-dimensional space is studied, and the bifurcation characteristics of the front steering angles are obtained. Second, the front steering angle range corresponding to the bifurcation point (FSAR-BP) and the safe basin of the system (SBS) are determined and used as optimization objective functions. Considering the influence of the structural parameters on the FSAR-BP and SBS, a sensitivity analysis of the parameters is performed to establish their importance ranking in terms of their influence on the FSAR-BP and SBS. Third, a non-dominated sorting genetic algorithm II (NSGA-II) is utilized to optimize the parameters, and the competitive Pareto parameter optimization solution sets are obtained. Finally, the three solution sets with better comprehensive performance are selected for comparison with the pre-optimized set in the FSAR-BP and SBS. The results indicate that the optimized FSAR-BP increased by 218.81%, 237.83%, and 267.88%, and the optimized SBS increased by 207.52%, 197.06%, and 173.93%, respectively. [ABSTRACT FROM AUTHOR]

• Evolutionary stable strategy analysis in a consensus reaching game. • There is an asymptotic evolutionary stable strategy under penalty policy. • Two-objective model of the optimal suggestions, adjusted opinions and unit costs. • Some enlightenments are provided for moderators. Consensus-reaching process (CRP) plays an important role in the group decision-making (GDM) process. And CRP is a game between the decision makers (DMs) and the moderator, which involves a series of issues about whether DMs accept the modification suggestions from moderators, how the moderators determine the optimal recommendations and whether compensation is available. To reflect the dynamic interaction between moderators and DMs in CRP, we propose a novel consensus model based on the evolutionary game theory. Specifically, we conduct the strategy stability analysis between DMs and moderators, and prove that there is an evolutionary stable strategy (ESS) with mixed strategies. Then, an ESS-based multi-objective programming consensus model (MOPCM) is developed with the maximum expected utility of DMs and moderators. A non-dominated sorting genetic algorithm is designed to obtain the Pareto solution set containing suggested opinions, adjusted opinions, and the unit adjustment cost. In addition, we provide decision-making guidance for consensus improvement through sensitivity analysis and demonstrate the significance of the evolutionary game between DMs and moderators on CRP. [ABSTRACT FROM AUTHOR]

Cloud Computing is no longer just a fad and announcement. Since the beginning of this decade and up to the present, cloud computing has changed and evolved. Workflows are employed in many different scientific disciplines to coordinate data demanding applications, but their virtualized apps, platforms, compute, and storage must be promptly provisioned, scaled, and released instantly. The vast majority of researchers agree that using scientific workflows as a paradigm to define, arrange, and communicate complex scientific analysis is helpful. Workflow scheduling aims to optimally assign and execute sequence of tasks on various virtual machine instances shared and controlled by the workflow scheduler. Numerous scheduling algorithms have been proposed to tackle difficult problems more quickly than metaheuristic ones due to the NP-complete nature of this problem and its dependence on theproblem size. Energy consumption has emerged as a major issue in the cloud computing environment, in addition to the standard quality of service(QoS) restrictions like time and cost to handle this issue. The goal of this paper is to develop the Dynamic Provisioning Based on Demand (DPBD) algorithm for workflow scheduling. While sequential task completion and task priority are design restrictions, the dual objectives of the cloud service are to minimize makespan and energy usage. Using the non-dominated sorting genetic algorithm (NSGA-II), the multi-objective optimization problem's Pareto optimal solutions are found. Our approach is validated by simulating a complex workflow application. [ABSTRACT FROM AUTHOR]

This paper proposed an improved jellyfish Search (ImpJS) technique for torque ripple minimization on CUK converter based BLDC motor. In this paper, crossover and mutation operator are utilized to improve searching behavior of the JS algorithm. Hence, it is named as improved jellyfish algorithm (ImpJS). At first, BLDC motor is considered along with the Cuk converter is improved through the switched inductor. Simultaneously, the execution of the BLDC motor operation includes speed and torque control strategy is also analyzed. In order to improve these two strategies, the proposed ImpJS system is introduced. The best gain parameter is tuned to upgrade the controller operation considering the objective function. Finally, the proposed technique-based BLDC motor is performed on MATLAB/Simulink platform in order to analyze the performance is compared with other existing system to determine the effectiveness. The existing techniques like particle swarm optimization (PSO), ant lion optimizer (ALO) and salp swarm algorithm (SSA). In case 1 and case 2, the MSE for proposed technique achieves the value of 0.01093 and 0.01095. In case 1 and case 2, the voltage deviation for the proposed system achieves the value of 2 and 2 • An improved technique for torque ripple minimization in cuk converter based BLDC motor. • Crossover and mutation operator are utilized to improve the JS algorithm. • Execution of the BLDC Motor operation includes speed and torque control. • Best gain parameter is tuned to upgrade the controller operation. [ABSTRACT FROM AUTHOR]

With the successful in-orbit operation of the cold atomic optical clock in the Mengtian laboratory cabin, a highly precise space time-reference will be formed. The BeiDou navigation satellite constellation has the ability of global coverage. By establishing time-transfer links with BeiDou satellites, the space station can realize precise transfer of its time-reference to wide area users. Aiming at the problem of highly precise and robust time-transfer link assignment from the space station to the BeiDou constellation, a link assignment model was established with taking the maximum hop and the connectivity as the evaluation indexes respectively of the accuracy and robustness of the time-transfer network. A topology optimization algorithm based on the genetic algorithm (GA) was further designed. Taking the time-reference transfer from the space station to the BeiDou Walker constellation as an example, the link assignment simulation was implemented in a minimum common orbit period. The precise time-reference transfer was realized using about 30% of inter-satellite link (ISL) timeslots. The maximum hop of the time-transfer links reached the theoretical optimal value, and the network connectivity was not less than 3. It was demonstrated that with the complex constraints of the ISL system, the payload quantity and visibility, the proposed assignment method could realize highly precise and robust time-reference transfer from the space station to the BeiDou constellation in any operation period. [ABSTRACT FROM AUTHOR]

The dichromatic number of a digraph D is the minimum number of colors of a vertex coloring of D such that D has no monochromatic cycles, the dichromatic number is an extension of the chromatic number to the class of digraphs. The Hajós join is a tool to obtain r -chromatic graphs, using the dichromatic number, J. Bang-Jensen et. al. extended the Hajós join to digraphs, and thus obtained a tool to obtain r-dichromatic digraphs. J. Bang-Jensen et. al. posed in 2020 the problem of how to obtain the symmetric cycle of length 5 from symmetric cycles of length 3. We recently solved this problem by applying a genetic algorithm. In this article, we generalize the construction of the symmetric cycle of length 5 and determine the computational complexity as Θ(n ln(n)). [ABSTRACT FROM AUTHOR]

The steam reforming reaction is widely used for obtaining hydrogen. The reforming reaction has a strong endothermic character, which means it requires a considerable and continuous heat supply to proceed. Due to the process character, a highly non-uniform temperature field develops inside the reactor. It has a consequence in large temperature gradients, leading to the catalyst degradation and a reduced lifetime of the reforming unit. The aim of the presented research is to unify the temperature field developing in the reactor, for easier control of the process and extension of the reformer's life expectancy. A conventional plug-flow reactor consists of a cylindrical pipe body filled with catalyst. The presented methodology included optimizing the catalyst distribution in the reactor to acquire the most uniform temperature field possible. A genetic algorithm is selected as an optimization technique for finding the most advantageous alignment of the catalyst. It is an example of evolutionary algorithms, basing on rules similar to natural selection. The algorithm generates a random, initial population of reactors and the reforming simulation is executed for each of them. The computation results are then evaluated and ranked using predefined fitness functions. The ranked reactors parameters' are further recombined with selection probability based on the fitness values, until a whole new population is created and the algorithm's loop restarts. The higher the fitness value of a specific reactor, the higher are the chances of passing its segments composition to the proceeding generation. The fitness computation leaves a vast space for improvements, as it may be computed based on many different process' parameters. This work focuses on distinguishing differences in the algorithm performance, depending on the formula for fitness calculation. The algorithm's converging speed, overall fitness values of specimens and optimization results were investigated and compared. The results show that the algorithm with an updated fitness calculation procedure performs considerably better. Only about 50% of computational time was required, to acquire results of the same quality for the presented numerical cases, when comparing with the previously prepared procedure. • Development of an optimization strategy tailored for the process character. • Improvement of the reforming reaction effectiveness. • Limitation of the adverse phenomena influence on the catalytic material. • Significant reduction of the optimization's computation cost. [ABSTRACT FROM AUTHOR]

Solid oxide fuel cell (SOFC) based integrated energy system (IES) is promising in the future low-carbon power generation market, due to the high efficiency and flexibility. However, it is challenging for the dynamic control design in dealing with the conflicting objectives in terms of fast power tracking and overall efficiency during the transient process of load response. To this end, this paper develops a multi-objective optimal droop control strategy for the real-time power dispatch of the IES. Firstly, a nonlinear implicit dynamic model consisting of SOFC, lithium-ion battery, photovoltaic array and DC-DC converter is developed. Then, a multi-objective optimization is formulated to balance the power tracking performance and transient efficiency. Non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to search the optimal parameters for droop controller. Simulation results demonstrates that the electricity loss of the proposed method can be reduced by 96.26% with a slight compromise in power tracking performance. • Dynamic model of integrated energy system is developed using Kirchhoff's law. • Controller parameters are optimized by multi-objective genetic algorithm. • Reduce 92.6% of electricity loss compared with single-objective optimization. [ABSTRACT FROM AUTHOR]

• Optimal location of interurban electric vehicle charging station infrastructure. • Multi-objective genetic algorithm to improve stalls utility and stations coverage. • Experimental results in the USA improve the current infrastructure. In recent years, there has been a strong desire to meet the challenge of electrification of vehicles in order to achieve the decarbonization objective. However, as sales of electric vehicles have increased, there is a significant lack of infrastructure to support the charging of this type of vehicle. The infrastructural deficiencies are even more evident in the interurban environment, where the autonomy in kilometers of the battery is a critical issue. To minimize the substantial economic costs involved in installing sufficient charging points to ensure any interurban journey, it is necessary to establish mechanisms that evaluate appropriate locations to deploy the necessary stations. Accordingly, this paper proposes using an evolutionary approach to calculate the most suitable locations in an interurban environment for electric charging stations. For this purpose, different input information is taken into account in the allocation process. The proposed algorithm has been tested using real data from the USA. The results assess the current infrastructure and show the advantages of the locations proposed by the algorithm. [ABSTRACT FROM AUTHOR]

A surrogate model-based method is proposed for optimising batch distillation processes and applied to the recovery of methanol from a five-component azeotropic waste solvent mixture, where pollutants are removed in two fore-cuts and an after-cut. The objective function is the profit of a single batch, while constraints are for the purity of the main cut and composition of the second fore-cut to be recycled. Simulations are performed by a flow-sheet simulator in a set of points in the space of optimisation variables (reflux ratios of steps, stopping criteria of fore-cuts). Algebraic surrogate models are fitted by ALAMO to simulation results to describe the objective function and the constraints. The resulting optimisation problem is solved numerically. The profit obtained is by 5% higher than the one previously obtained by genetic algorithm (commonly used for optimisation of batch distillation), while the number of simulations is reduced to its third. The highest profit, previously obtained by the Nelder-Mead simplex method, is approached within 1%. Although the simplex method required fewer simulations, the new method proposed here is a global one. The process is re-optimised for different prices to investigate their influence on the profit and optimal values of operational parameters. [Display omitted] • A surrogate model-based method is proposed for batch distillation optimisation. • The profit of methanol recovery from a five-component mixture is optimised. • The new method gave higher profit with fewer simulations than a genetic algorithm. • The optimum is within 0.5% of the best one previously found by a local method. • Re-optimisation is possible for different economic data without further simulations. [ABSTRACT FROM AUTHOR]

Exergoeconomic assessment of a multigeneration system, which is fed with a mixture of natural gas and syngas produced from municipal solid waste gasification process, has been conducted under climatic restrictions of both hot and cold seasons. In the basic state of the 1st scenario, for the constant net power of 30 M W and 50% mixing ratio, exergy efficiency and total cost rate of the system are 27.92% and 1328 U S D / h in summer, and 33.81% and 1246 U S D / h , in winter, respectively. Moreover, the total relative cost difference in the summer is considerably higher than that in the winter. In the basic state of the 2nd scenario, for the constant fuel mass flow rate of 1.5 k g / s , the system's annual exergy efficiency, annual total cost rate, and payback period are 30.79%, 1166.4 U S D / h , and 2 years, respectively. The optimization has been performed based on the genetic algorithm considering two objective functions. The optimization results revealed that, the system's annual exergy efficiency and annual total cost rate got improved from 30.79% to 32.37%, and from 1166.4 U S D / h to 796.52 U S D / h , respectively, while the payback period increases from 2 to 2.1 years. The complete results of parametric studies are also provided for both scenarios. [ABSTRACT FROM AUTHOR]

A kernel-based intuitionistic weight fuzzy k-modes algorithm (KIWFKM) is proposed in this paper which can improve the clustering performance of categorical data. The current FKM algorithm which were proposed by researchers generally have three drawbacks. Firstly, the algorithms were easily limited to the local optimal solution. Secondly, most of algorithms were considering all attributes equally. Thirdly, most algorithms are sensitive to noise data points. So the intuitionistic fuzzy sets (IFS), kernel trick and weight concept are introduced into the objective function which can not only solve the problem of all attributes equally but also improve the robustness to noise. In addition, a coupled DCP (chained tissue-like P system combines DNA genetic rules) system is established which is used for realizing the KIWFKM algorithm (KIWFKM-DCP). The uncertainty and implicit parallelism of the DCP system can help the KIWFKM algorithm jump out of the local optimal solution and find better solution. Finally, we conduct experiments and compare experiment results with six state-of-the-art clustering methods. Experimental results conduct that the KIWFKM-DCP algorithm perform better than the other comparison clustering algorithms. [ABSTRACT FROM AUTHOR]

Machine learning and stochastic optimization approaches are commonly used in wire electrical discharge machining (WEDM) of specific Ni-based superalloys and required separate optimization approaches for materials within the same group of superalloys, which is a time-consuming process due to the complicated parametric correlations. The most likely solution is a multi-material model that can deal with multiple Ni-based superalloys, which is uncommon in published literature. The current study aims to develop genetic algorithm (GA) and particle swarm optimization (PSO) integrated multi-objective multi-layer neural network (MOMLNN) models for multi-material optimization to achieve efficient strategies for multi-material systems. Six process variables, seven compositional elements and four response variables are used to generate MOMLNN with 13–300–300–4 architecture integrated with GA and PSO techniques to identify the optimum parameters for simultaneous WEDM of IN625 and IN718. It is observed that the GA integrated model has a slightly higher accuracy than the PSO integrated model. The material removal rate (MRR) and kerf width (KW) increases with increase in T on and A on. The surface roughness (Ra) decreases with decrease in A on and increase in WF. The recast layer thickness (RLT) increases with increase in A off. Changes in Cr, Mo, and Si among the alloying elements have a positive impact on MRR, KW, and Ra, while changes in Fe, Nb, Ti, and Al mainly govern the RLT. The optimum responses, obtained from GA integrated model are validated with experimental results and the error fraction is generally less than 5%, indicating good predictive accuracy of the model. [Display omitted] [ABSTRACT FROM AUTHOR]