Weighted Sum Technique Research Articles

Reinforcement Q-learning algorithm for H∞ tracking control of discrete-time Markov jump systems

In this paper, the H ∞ tracking control problem of linear discrete-time Markov jump systems is studied by using the data-based reinforcement learning method. Specifically, a new performance index function is established by using Markov chain and weighted sum technique, and thus the tracking game algebraic Riccati equation with weight vector and discount factor is obtained. A Q-learning algorithm is proposed to solve the tracking game algebra Riccati equation problem online without knowing the information of the system model. In addition, the convergence analysis of the algorithm is given, and it is proved that the added probing noise will not bias the algorithm. Finally, two simulation examples are given to verify the effectiveness of the proposed algorithm.

An integrated probabilistic bipolar fermatean hesitant fuzzy transportation configuration for sustainable management of floral waste with risk assessment

This paper proposes the probabilistic bipolar fermatean hesitant fuzzy set (PBFHFS) and its triangular version as generalizations of bipolar fuzzy set (BFS) for delineating randomness and imprecision in a single framework. To enhance pragmatic applicability, the devised set has been examined based on its fundamental operations, basic properties, and defuzzification mechanism. Offerings made to temples are considered sacred, so they cannot be disposed of in landfills. Waste from temples is usually disposed of in nearby lakes, ponds, and rivers. Thus, management of the temple floral waste (TFW) is essential to enhance the sustainability of the environment. In this regard, a green 4-dimensional transportation system under PBFHF settings is developed for the effective and sustainable managements of floral waste that is dumped from places of worship. For the most cost-effective and environmentally friendly results, this transportation system optimises a number of metrics, transportation expenditure (TE), carbon emission (CE), transportation time (TT), and labour cost (LC). Three sub-models are further developed from the proposed model under PBFHF conditions by incorporating the theory of carbon policy and risk assessments. The suggested transportation system is addressed by employing a fuzzy methodology, fuzzy programming (FP) as well as two non-fuzzy approaches, weighted sum technique (WST) and goal programming technique (GP). At last, there is a numerical computation along with comparison analysis, management insights, a conclusion with limitations, and a scope for further study.

Enhancing Electric Shuttle Bus Efficiency: A Case Study on Timetabling and Scheduling Optimization

As transit authorities increasingly adopt electric buses (EBs) to mitigate air quality concerns and greenhouse gas emissions, new challenges arise in bus scheduling and timetabling. Unlike traditional buses, EBs face operational obstacles due to their shorter range and extended charging times. Existing mathematical optimization models for operation planning of traditional buses must be revised to address these unique characteristics of EBs. This study introduces a new approach to integrate timetabling and bus scheduling to enhance the level of service and minimize operational costs, using a case study of a University shuttle bus service in Montreal, Canada. The level of service will be enhanced by reducing students waiting time and improving their in-vehicle comfort through seat availability. The scheduling aspect seeks to reduce the total operational costs, which include travel, electricity consumption, and usage costs of EBs. The proposed algorithm calculates the waiting time and seat availability for different headway values and addresses the scheduling problem using a mixed-integer linear programming (MILP) model with an arc-based approach, solved using the Cplex Optimization Studio software version 12.8. A normalized weighted sum technique is then applied to select the optimal headway, balancing waiting time, seat availability, and operational costs. The effectiveness of our approach was tested through a case study of Concordia University’s shuttle bus service. Comparative analysis of the current and proposed schedules shows that our approach significantly improves service quality by decreasing waiting times and increasing seat availability while optimizing cost-effectiveness compared to the existing timetable of the Concordia shuttle bus. The proposed approach ensures a smooth transition to a fully electric transit system for shuttle bus services.

Understanding Age-Driven Preferences: A Weighted Sum Analysis of Consumer Behavior in E-commerce

Consumer activity in online commerce plays a crucial role in modern business functions, impacting market trends and guiding approaches for digital merchants. This research utilizes the weighted sum technique to assess consumer actions among different age brackets, emphasizing significant indicators like Average Order Value, Satisfaction with Customer Support, Timeliness of Delivery, and Rate of Product Returns. The results underscore unique preferences and performance patterns across age segments, offering actionable intelligence for e-commerce enterprises aiming to refine their tactics. The examination indicates that individuals aged 45-54 stand out as the leading performers across every metric, showcasing the highest expenditure, most satisfactory customer support ratings, swiftest delivery times, and minimal product return rates. Conversely, the 35-44 age bracket displays the lowest overall performance, marked by comparatively lower spending, moderate satisfaction with customer support, extended delivery durations, and elevated rates of product returns. Intermediate groups include the 18-24 and 55-65 age groups, with the former showing strong overall performance and the latter displaying good customer support satisfaction and reasonable delivery times but lower spending and slightly higher return rates compared to the top performers. Additionally, the 25-34 age group performs moderately well, with decent customer support satisfaction and delivery times but lower spending and higher product return rates. These findings provide invaluable direction for e-commerce enterprises aiming to customize their strategies adeptly for diverse age segments. By comprehending the unique preferences and actions of each demographic, businesses can refine their online shopping offerings, elevate customer contentment, bolster retention figures, and foster comprehensive advancement within the competitive e-commerce.

Dual hesitant fuzzy set in multi-objective transportation problems in time sequence frame work

Decision makers may have different information in different situations when making decisions about the same attributes, depending on the circumstances. A time-sequential framework is necessary for navigating these kinds of circumstances. We established the notion of a Time-sequential dual hesitant fuzzy set (TS-DHFS) to illustrate the significance of time sequence in decision-making with the aid of a dual hesitant fuzzy set (DHFS), one of the important tools to handle the uncertainty embedded in the real world. The proposed set describes the hesitant situations more accurately by inducing a time sequence framework. We defined the fundamental operations, two score function for ranking and a series distance measures for the proposed set. For the pragmatic application of our proposed set, we devised the mathematical model in three different types of Time-sequential dual hesitant fuzzy multi-objective transportation problems (TS-DHF-MOTPs). The proposed three models are classified based on the nature of the parameters such as transportation cost (TC), transportation time (TT), availability, and demand. The goal of the proposed transportation systems is TC and TT simultaneously in a single framework. We established an approach utilizing one of the ranking functions that have been proposed, followed by the fuzzy programming (FP) and weighted sum technique (WST), to optimize the constructed models. Additionally, numerical computations are also carried out to emphasize the efficiency and benefits of MOTP under the TS-DHF environment.

Coastal Flood Induced Salinity Intrusion Risk Assessment Using a Spatial Multi-criteria Approach in the South-Western Bangladesh

AbstractBangladesh is extremely vulnerable to sea-level rise and other climate-induced extreme events, such as flooding, storm surge, and salinity intrusion. The south-western coastal region of Bangladesh is particularly vulnerable to salinity intrusion caused by cyclone induced storm surges and coastal floods. Salinity intrusion endanger land productivity by increasing both soil and surface water salinity. Detailed risk assessment using spatial mapping approach can contribute to mitigating the effects of salinity intrusion on natural capital and the environment. In this study, we established and evaluated a spatial multi-criteria approach for mapping the risk levels of areas to salinity intrusion impacts using field data and geospatial techniques at the local scale. We evaluated the viability of the proposed approach using Khulna District, a major coastal city and saline prone area in the south-western Bangladesh. We considered three risk components (i.e. vulnerability, exposure and hazard) with 16 relevant criteria for the study. For each criterion, an Analytical Hierarchy Process (AHP) was used to build and weight spatial raster map layers. Individual maps for each risk component were generated using a weighted sum technique, and lastly, a risk map was created by combining those. Our generated maps correctly identified relevant spatial dimensions as well as risk levels (i.e. very-high to very-low). The outcomes of our study suggest that the southern (east and west) parts of the study area are mostly susceptible to salinity intrusion due to higher storm surge impacts, lower elevation, and land use patterns than other parts. We validate our findings using a qualitative and quantitative approach. We believe that this novel approach would be useful to create risk maps that policymakers and relevant stakeholders could potentially use to evaluate risks posed by flood induced salinity intrusion in coastal regions of Bangladesh and elsewhere with similar geo-climatic context.

Time-sequential probabilistic fermatean hesitant approach in multi-objective green solid transportation problems for sustainable enhancement

In order to delineate the randomness and imprecision in a single framework of time-sequential information, we proposed the notion of a time-sequential probabilistic fermatean hesitant set (TS-PFHS). Triangular time-sequential probabilistic fermatean hesitant number (Tr-TS-PFHN) is also initiated as a triangular version of TS-PFHS along with its fundamental operations and a ranking function. For the employment of our proposed set, we formulated a solid transportation problem (STP) with multiple objectives with TS-PFHS parameters for a sustainable green environment. Consequently, we proposed an algorithm by utilizing fuzzy programming (FP) and weighted sum technique (WST) to address proposed green transportation model. Additionally, to highlight the significance of the suggested technique, a numerical instance based on the transportation of electric vehicles in India is also investigated. Finally, conclusion along with the future prospects and limitations of the work done are discussed.

Assessment of Educational Potentiality of Central and Southern States of India Using a New Composite Education Index (CEI)

The present research was aimed to evaluate the educational potentiality of southern states of India using a new innovative Composite Education Index (CEI). An integrated seven step procedure was followed for the calculations of CEI. After preparing a composite hierarchical structure using two parameters, ten criteria and twelve indicators, the Analytical Hierarchy Process (AHP) and weighted sum technique were applied to get the CEI. The lowest category of CEI was marked with 20.573% area, the low category with 27.817% area, the moderate category with 20.771% area and the highest category were marked with 30.839% area of the study region. Further, the CEI was compared with School Education Quality Index (SEQI) and a high R-square value of 97.3% was obtained. Therefore, the CEI can be utilized to measure educational potentialities without hesitation. A large number of indicators are merged in this index, and it is flexible and easy to implement in any region.

Energy efficiency measures in existing buildings by a multiple-objective optimization with a solar panel system using Marine Predators Optimization Algorithm

To enhance the energy effectiveness of old buildings, retrofitting strategies are an efficient technique. The objective here is to apply a multiple-criteria optimization method to retrofit the building envelope. In this respect, a new metaheuristic algorithm, called Marine Predators Optimization Algorithm (MPOA) is used in this paper. The components that are investigated to be retrofitted are including roofs, windows, and outer walls. A photovoltaic system is considered to be employed on the rooftop. The degradation duration of the solar panels is studied. Also, for renewable energy development and its long-lasting impact on the retrofitting scheme, a maintenance design is considered. The optimum utilization of the financial investment for the maximization of energy savings and economic-related advantages are the main targets. Specifically, the major operation indicators of the retrofitting scheme are energy savings, payback period, and net present worth. A weighted sum technique is applied to solve the non-linear problem. The efficiency of the suggested model is a result of the designed retrofitting plan.

A green 4-dimensional multi objective transportation system for disaster relief operations under time-sequential complex fermatean framework with safety measure

In a post-disaster response operation of a severe cyclone, like Biparjoy, the transportation of humanitarian aid (HA) is the foremost activity in the aftermath of the disaster. Here, due to the ubiquity of uncertainty in such operations, we have first devised a time-sequential complex Fermatean hesitant fuzzy set (TS-CFHFS) with all-dimensional aspects such as its structure, fundamental operations, score and accuracy function. For the transportation of HA, we have developed a multi objective multi-item green 4-dimensional transportation system under the TS-CFHF configuration. In our proposed framework, the HA from both private and public sectors is transported to distribution centers (DCs). The aid was then distributed to the disaster relief shelters (DRS) established in the affected areas. We have also incorporated several significant factors that enhance its realistic portrayal, including vehicle speed, disturbance rate, and safety while transporting HAs. The first and foremost objective is to minimize transportation time (TT), for which we have incorporated the objective vehicle speed, while other objectives are transportation cost (TC), carbon emission (CE) and job opportunities (JO) in our proposed model. To obtain the optimal solution, three approaches namely fuzzy programming (FP), global criterion method (GCM) and weighted sum technique (WST) have been utilized. The proposed model has been complemented with two specific case studies based on the nature of the safety constraints by using a numerical computation along with a comparative analysis of the results obtained by three distinct approaches that may be useful to the decision-maker to make an empirical decision that would affect emergency response actions.

Analyzing Sensitivity Analysis Techniques: A Comprehensive Study Across Various Fields

Sensitivity analysis. Users of both simulation and mathematical models are given tools by sensitivity analysis to understand how closely a model's output is related to its input and to determine the relative importance of each input. All application fields are included, including engineering, sociology, and theoretical physics. In order to clarify the structure of the entire "Sensitivity Analysis" section of the Springer Handbook, this introduction paper presents the purposes and objectives of sensitivity analysis. A helpful software programmer, the mathematical notation used in the book papers, certain categorization grids to comprehend the application limits of each method, and the fundamental concepts of sensitivity analysis are also covered. Making decisions can benefit from sensitivity analysis in a number of ways. It first serves as a thorough analysis of all the factors. The predictions might be much more accurate because it's more thorough. Second, it enables decision-makers to pinpoint areas where future improvements can be made. The weighted sum technique is a cross decision-making process; as there are numerous possibilities, there are also many more factors that must be considered before choosing the best one. A weighted or weighted collection of sums is a machine learning strategy that combines predictions from various models, with each model's contribution being weighed according to its capacity or level of expertise. Weighted the with mean evening gown voting ensemble related to this method benefits of using it are ease of use, especially when working with convergent problems, such as when disadvantages an all in solution space make it impossible to find solutions and goals a simple way to ascribe weights there is no way. relative deviation ratio (RDR), partial rank correlation coefficient (PRCC), Standardized regression coefficients (SRC), rank regression coefficient (RRC). Beef cow ingestion rate, Atmospheric concentration, Beef transport time, Biological half-life, breathing rat, Feed-to-meat transfer factor, Feed-to-milk transfer factor, Meat consumption rate. from the result it is seen that Biological half-life, breathing rat and is got the first rank whereas is the Beef transport time got is having the lowest rank. Conclusion: The value of the dataset for Sensitivity analysis technique in GRA (Gray-related analysis) method shows that it results in Biological half-life, breathing rat and top ranking.

Enabling Efficient IoT Device Connectivity and Dynamic Network Management through SDN: A Weighted Sum Method Approach

SDN-Enabled IoT Networks bring about a transformative shift in conventional network models by integrating the core principles of Software-Defined Networking (SDN) into the realm of the Internet of Things (IoT). This integration empowers the agile and effective management of IoT devices, facilitating smooth connectivity, optimized distribution of resources, and flexible network setups. Through the consolidation of control and the utilization of virtualization methods, SDN-Enabled IoT Networks amplify scalability, security, and real-time responsiveness. This addresses the obstacles presented by the extensive proliferation of IoT devices. This paradigm transition heralds a fresh era of interconnectedness, where SDN assumes a central role in harmonizing the intricate interplay of IoT devices and services. The rapid expansion of Internet of Things (IoT) devices has introduced unparalleled complexities in overseeing networks and establishing connections. This compels the need for inventive strategies to effectively manage the substantial surge of IoT devices and their ever-changing connectivity prerequisites. Software-Defined Networking (SDN) emerges as a promising approach to tackle these issues by enabling the flexible management of networks and allocation of resources. This study investigates the amalgamation of SDN within the realm of IoT, aiming to streamline device connections, optimize data transmission efficiency, and accommodate adaptable network setups. Introducing an innovative weighted sum technique for resource allocation optimization, this work lays the foundation for a comprehensive framework that bolsters IoT network performance and expandability. Four different SDN implementations are examined, including the Conventional IoT Network, SDN-enabled IoT utilizing Centralized Control, SDN-enabled IoT employing Distributed Control, SDN-enabled IoT with Hierarchical Control, and SDN-enabled IoT utilizing Hybrid Control. The assessment considers various aspects such as Enhanced Scalability, Enhanced Traffic Engineering, Heightened Security, Implementation Complexity, Difficulty of Migration, and Reliance on Vendors. The Conventional IoT Network secures a moderate 3rd position with a Preference Score of 0.56030, while the SDN-enabled IoT with Centralized Control holds the 5th rank at 0.49732, despite excelling in specific domains. The SDN-enabled IoT with Distributed Control achieves the top rank with a notable Preference Score of 0.79414 due to comprehensive performance, followed by the SDN-enabled IoT with Hierarchical Control securing the 2nd spot (Preference Score: 0.57022), and the SDN-enabled IoT with Hybrid Control taking the 4th position (Preference Score: 0.51300), particularly excelling in Traffic Engineering.

A study of faults in the Superior province of Ontario and Quebec using the random forest machine learning algorithm: Spatial relationship to gold mines

This paper uses the random forest (RF) algorithm to produce two maps of orogenic gold prospectivity (MPM) across the entire Superior geologic province of Ontario and Quebec, Canada. One MPM of this entire study area is based on a compilation of mapped faults while the other is based on a manual interpretation of lineaments from airborne magnetic data. In addition, we have created four MPMs over the Abitibi region as the MPM map of the entire Superior is characterized by a mapping bias due to more intense mapping of faults over the Abitibi portion of the study area with respect to the rest of the Superior province. There are three RF maps generated over the Abitibi region based on faults, fault density and fault intersection density, faults + magnetic data and faults + gravity + magnetic data. To reduce the effect of the fault mapping bias, we developed a fourth map based on a knowledge driven (weighted sum technique). Statistically the best MPM is based on the faults, gravity and magnetic data. The major predictors of gold are NW-SE, NE-SW, EW trending faults, fault intersection density between EW and NW trending faults and to a lesser extent fault density and a magnetic vertical gradient image. In addition, we compare two greenstone belts in three dimensions with respect to their potential for gold exploration. We conclude that the Larder Lake greenstone belt is more fertile with respect to gold mineralization than the Swayze greenstone belt due to deeper penetrating faults which have greater potential for tapping gold mineralized fluid from the mantle or lower crust. Our final MPM map has identified prospective areas that contain known gold mines as well as areas that do not contain any known gold mines. These prospective areas may be prime for orogenic gold exploration.

Efficacy of GIS-based AHP and data-driven intelligent machine learning algorithms for irrigation water quality prediction in an agricultural-mine district within the Lower Benue Trough, Nigeria.

Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models-multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)-were integrated and validated to predict the IWQ parameters. The coefficient of determination (R2) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO3, Cl, Mg, and SO4 were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction.

Evaluation and prediction of irrigation water quality of an agricultural district, SE Nigeria: an integrated heuristic GIS-based and machine learning approach.

Poor irrigation water quality can mar agricultural productivity. Traditional assessment of irrigation water quality usually requires the computation of various conventional quality parameters, which is often time-consuming and associated with errors during sub-index computation. To overcome this limitation, it becomes critical, therefore, to have a visual assessment of the irrigation water quality and identify the most influential water quality parameters for accurate prediction, management, and sustainability of irrigation water quality. Therefore, in this study, the overlay weighted sum technique was used to generate the irrigation water quality (IWQ) map of the area. The map revealed that 29.2% of the area is suitable for irrigation (low restriction), 41.7% is moderately suitable (moderate restriction); and 29.1% is unsuitable (high restriction), with the irrigation water quality declining towards the central-southeastern direction. Multilayer perceptron artificial neural networks (MLP-ANNs) and multiple linear regression models (MLR) were integrated and validated to predict the IWQ parameters using Cl-, HCO3- SO42-, NO3-, Ca2+, Mg2+, Na+, K+, pH, EC, TH, and TDS as input variables, and MAR, SAR, PI, KR, SSP, and PS as output variables. The two models showed high-performance accuracy based on the results of the coefficient of determination (R2 = 0.513-0.983). Low modeling errors were observed from the results of the sum of square errors (SOSE), relative errors (RE), adjusted R-square (R2adj), and residual plots, further confirming the efficacy of the two models; although the MLP-ANNs showed higher prediction accuracy for R2. Based on the sensitivity analysis of the MLP-ANN model, HCO3, pH, SO4, EC, and Cl were identified to have the greatest influence on the irrigation water quality of the area. This study has shown that the integration of GIS and machine learning can serve as rapid decision-making tools for proper planning and enhanced agricultural productivity.

A Probabilistic Model for Minimization of Solar Energy Operation Costs as Well as CO2 Emissions in a Multi-Carrier Microgrid (MCMG)

This paper proposes a probabilistic model with the aim to reduce the solar energy operation cost and CO2 emissions of a multi-carrier microgrid. The MCMG in this study includes various elements such as combined heat and power (CHP), electrical heat pump (EHP), absorption chiller, solar panels, and thermal and electrical storages. A MILP model is proposed to manage the commitment of energy producers, energy storage equipment, the amount of selling/buying of energy with the upstream network, and the energy consumption of the responsible electrical loads for the day-ahead optimal operation of this microgrid. The proposed operation model is formulated as a multi-objective optimization model based on two environmental and economic objectives, using a weighted sum technique and a fuzzy satisfying approach. In this paper, the 2 m + 1-point estimate strategy has been used to model the uncertainties caused by the output power of solar panels and the upstream power supply price. In order to evaluate the performance of the proposed model, and also for minimizing cost and CO2 emissions, the simulation was conducted on two typical cold and hot days. Numerical results show the proposed model’s performance and the effect of electrifying the heating and cooling of the microgrid through the EHP unit on greenhouse gas emissions in the scenarios considered.

A Sustainable Intermodal Location-Routing Optimization Approach: A Case Study of the Bohai Rim Region

The optimal intermodal nodes and routes are two of the most challenging issues for intermodal participants. We present a two-phase approach that includes the fuzzy c-means clustering method (FCM) and a multi-objective optimization model to solve intermodal location-routing issues. A weighted sum technique and a genetic algorithm (GA) are designed to address this model. The two-phase approach is beneficial in meeting different market demand preferences of intermodal participants. It also has applications in solving the sustainable intermodal location-routing problems, further solving the network optimization problem in large-scale scenarios. A typical intermodal transport network in the Bohai Rim region is used to verify the effectiveness of this approach. The results provide references for the participants in the Bohai Rim region to choose the optimal intermodal nodes and routes. The findings also offer theoretical insights for optimizing intermodal networks in other regions of China, with goals of improving sustainable transport efficiencies.

EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications.

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.

A hybrid particle swarm optimization and recurrent dynamic neural network for multi-performance optimization of hard turning operation

Abstract In the present work, a new hybrid approach combining particle swarm optimization (PSO) algorithm with recurrent dynamic neural network (RDNN), which is described as PSO-RDNN algorithm, is proposed for multi-performance optimization of machining parameters in finish turning of hardened AISI D2. The suggested optimization problem is solved using the weighted sum technique. Process parameters including cutting speed and feed rate are optimized for minimizing operation cost, maximizing tool life, and producing parts with acceptable surface roughness. Based on experimental results, two neural network models were developed for predicting tool flank wear and surface roughness during the machining process. Based on trained neural networks and structured hybrid algorithm, optimum cutting parameters were obtained. The coefficient of determination for trained neural networks was calculated as R2 = 0.9893 and R2 = 0.9879 for predicted flank wear and surface roughness, respectively, which proves the efficiency of trained neural models in real industrial applications. Furthermore, the offered methodology returns a Pareto optimality graph, which represents optimized cutting variables for several various cutting conditions.

An Economic and Environmental Optimization Model in a Micro Grid with Demand Response

Abstract To accommodate the current energy system with the renewable energy source, the concept of micro grid (MG) has been developed. Using such systems, different advantage such as minimization of power loss, operating cost, and emission can be achieved. In this paper, the 24-hour power and heat based MG scheduling is investigated under environmental aspects and economic issues as bi-objective modelling in the presence of demand response (DR) program for decreasing greenhouse gas emission and system operation cost. The investigation of the MG contains energy storage system, boiler and combined heat and power (CHP). For solving the problem, first the Pareto optimal solutions have been obtained by the weighted-sum technique, then the fuzzy satisfying approach has been used to get optimal trade-off result. To confirm the performance of the method, the problem is solved in two-case to demonstrate the effects of DR on MG scheduling.