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The paper addresses challenges arising from the unpredictable and probabilistic nature of wind speed and solar irradiance, leading to variable power production in renewable sources. Power imbalance issues in grids result from the difficulty in accurately estimating these variables. The study introduces an improved data-driven uncertainty set, utilizing a neural network trained with extensive historical data from a multi-microgrid system. This set efficiently identifies strong coupling and extracts characteristic scenarios, ensuring high reliability with a minimal number of scenes. The proposed probabilistic model, implemented in the coordination of pumped storage, wind, and solar power plants, incorporates energy limits and storage characteristics. A four-mode IEEE model for storage units allows quick response to system needs. Simulating storage unit states every hour, considering system load, output power, and energy limitations, optimizes power dispatch based on battery capacity. Additionally, the paper addresses the uncertainty in electric power demand in microgrids, proposing an improved data-driven uncertainty set obtained from a neural network. By eliminating unrealistic worst-case scenarios, the approach accurately captures system characteristics, maintaining high reliability while significantly reducing convergence time. The proposed uncertainty set is applied in a novel two-stage robust optimization dispatch model, transforming the optimization problem into a mixed-integer linear programming problem, and solving it using a column and constraint generation algorithm. Numerical case studies verify the feasibility and superiority of the proposed approach in terms of economy and convergence performance compared to existing robust optimization methods. [ABSTRACT FROM AUTHOR]

With the increasing complexity of the power grid, the precision of the thermal power units' execution of automatic generation control (AGC) commands is gradually increasing the impact on the online regulation of the power grid. The deviation between the actual output of thermal power units and the AGC command of the grid will not only affect the consumption of new energy output, but also endanger the safe operation of the grid. This paper introduces "deep learning" technology to solve the problem. Firstly, an AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. The algorithm builds a neural network model to accurately predict the unit output and the confidence evaluation of the prediction results. Next, a high-dimensional input preprocessing strategy based on variational autoencoder (VAE) is proposed to reduce the dimensionality of the model input attributes, improving the convergence and accuracy of the model. Finally, an AGC optimal command fast inversion solution method (AOCFISM) is designed. This method transforms the unit output deviation problem into an objective optimisation problem. And improve the efficiency of solving the optimal AGC command value by constraining the unit command value. The calculation results show that the error of the prediction results of the model proposed in this paper is 5% lower than that of the traditional neural network. The difference between the output value of the optimal AGC command and the expected output value obtained is less than 0.5 MV, which can support AGC online decision-making.© 2017 Elsevier Inc. All rights reserved. • An AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. • Results show the ACEEI-CEA performs well in command prediction compared to the state-of-the-art methods. • ACEEI-CEA achieves confidence assessment of predicted values. • The pre-processing strategy based on variational autoencoder shortens the training time of the model. • The AGC optimal command fast inversion solution method (AOCFISM) can obtain the AGC optimal command value. [ABSTRACT FROM AUTHOR]

Continuously growing demands due to the predictable faults or abnormal events of the flexible production line are becoming a challenge to easily cause the energy waste, which needs to accurately predict the real-time energy consumption of the actual production process. By reviewing the related works about prediction model approach and energy consumption analysis in the actual production application, it is necessary to overcome two main challenges: optimal prediction selection and internal relation mining of energy consumption under different equipment working conditions. To deal with the existing challenges, the paper proposes a novel attention-based Long and Short-Term Memory (LSTM) approach to predict energy consumption with temporal series under various equipment working conditions. This method offers forecasting support to form a complete prediction framework for the automatic Miniature Circuit Breaker (MCB) production line, which can be used to predict the specific working condition based on attention-based LSTM approach. By comparing various prediction approaches for energy consumption under the working condition prediction with various equipment of different production line, the Fully Connected Neutral (FCN) network approach can achieve a high accuracy and effectiveness based on MAE, R2 and MSE evaluations. [ABSTRACT FROM AUTHOR]

An energy functional describes the equilibrium state of a system. In this work, we present a novel technique, Functional Optimization using Neural Networks (FONN), for minimizing the system's energy. FONN utilizes neural networks to process information at discrete grid points, considering their interactions with neighboring grid points, to update the state of the system. The training process involves formulating a loss function based on the system's energy, and with the help of multiple fine-tuning steps, the method employs a progressive energy reduction technique that decreases the energy in multiple steps. FONN's effectiveness is demonstrated across various problems, including the minimization of the heat and Lyapunov energy. Moreover, the paper explores the minimization of the elastic bending energy with an area constraint. [ABSTRACT FROM AUTHOR]

Continuously growing demands due to the predictable faults or abnormal events of the flexible production line are becoming a challenge to easily cause the energy waste, which needs to accurately predict the real-time energy consumption of the actual production process. By reviewing the related works about prediction model approach and energy consumption analysis in the actual production application, it is necessary to overcome two main challenges: optimal prediction selection and internal relation mining of energy consumption under different equipment working conditions. To deal with the existing challenges, the paper proposes a novel attention-based Long and Short-Term Memory (LSTM) approach to predict energy consumption with temporal series under various equipment working conditions. This method offers forecasting support to form a complete prediction framework for the automatic Miniature Circuit Breaker (MCB) production line, which can be used to predict the specific working condition based on attention-based LSTM approach. By comparing various prediction approaches for energy consumption under the working condition prediction with various equipment of different production line, the Fully Connected Neutral (FCN) network approach can achieve a high accuracy and effectiveness based on MAE, R2 and MSE evaluations. [ABSTRACT FROM AUTHOR]

An energy functional describes the equilibrium state of a system. In this work, we present a novel technique, Functional Optimization using Neural Networks (FONN), for minimizing the system's energy. FONN utilizes neural networks to process information at discrete grid points, considering their interactions with neighboring grid points, to update the state of the system. The training process involves formulating a loss function based on the system's energy, and with the help of multiple fine-tuning steps, the method employs a progressive energy reduction technique that decreases the energy in multiple steps. FONN's effectiveness is demonstrated across various problems, including the minimization of the heat and Lyapunov energy. Moreover, the paper explores the minimization of the elastic bending energy with an area constraint. [ABSTRACT FROM AUTHOR]

Energy wastage and service level access are challenges of cloud networks, while efficient migration of virtual machines is a solution for them. Of course, uncontrolled migration increases overhead in cloud comprising: violation in service and high energy consumption. Performance requirements is very important in cloud, generally, it in the form of service level agreements are formalized. The main goal of migration is energy and performance trade-off. Detecting overloaded and under-loaded physical machine as sub-problem of migration, which effects the performance and energy. In this paper, with definition a system modelling, in first for detect overloaded and under-loaded physical machines a predict detection method is proposed. The forecast of the future utilization of the processor by using predict physical machines utilization method is provided. Predict physical machine's utilization module in predict detection method has been called. Unlike the previous physical machine detection methods, the current load of physical machines along with the future load of them is considered, and will lead to more exact results. Then for selection of virtual machines on overloaded physical machines a fuzzy selection method is proposed. Unlike the previous virtual machine-selection methods, all affective variables are considered and will lead to better results. In overall, the predict detection-fuzzy selection algorithm as intelligent approach has proposed and simulated with PlanetLab workload, CloudSim toolkit and Matlab. Simulation results demonstrate that significantly outperforms benchmark algorithms in terms of the energy consumption, migration count, service level agreement violation, energy-service level agreement violation, and energy-service level agreement violation-migration. [ABSTRACT FROM AUTHOR]

Remanufacturing attains much attention from both industrial and academic sectors due to its beneficial roles in energy conservation and environment protection, where disassembly is a crucial part. To reach the comprehensive sustainability and global optimization of disassembling multiple end-of-life products, this paper suggests a multi-objective multi-product disassembly line balancing problem with considering disassembly profit, energy consumption and noise pollution simultaneously. According to the natures of the problem under consideration, a multi-objective integer programming model is constructed. Its goals are to reach maximum disassembly profit and realize minimum energy consumption while observing noise pollution requirements and resource constraints. Accordingly, a multi-objective group teaching optimization algorithm is specially devised. In it, rank and crowding distance methods are employed to partition the population into two groups. Moreover, precedence preserving crossover and mutation methods are severally used on the two groups to realize the teacher phase. Furthermore, to achieve the student phase, an adaptive local search method is applied to refine solutions in an external archive, and thus its exploitation ability is enhanced. By executing contrast experiments between the devised approach and its powerful competitors on a set of real-world test instances, the experimental results validate that it has highly-adaptive and well-superior performance in tackling the problem of concern. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, we propose a lightweight approach for industrial defect detection that is modeled based on anomaly detection using point pattern data. Most recent works use global features for feature extraction to summarize image content. However, global features are not robust against lighting and viewpoint changes and do not describe images' geometrical information which, thus, cannot be fully utilized in manufacturing industry. To the best of our knowledge, we are the first to propose using transfer learning of local/point pattern features to overcome these limitations and capture geometrical information of the interested image regions. We model these local/point pattern features as a random finite set (RFS). In addition, we propose RFS energy, in contrast to RFS likelihood as an anomaly score. The similarity distribution of point pattern features of the normal sample has been modeled as a multivariate Gaussian. Parameters learning of the proposed RFS energy does not require any heavy computation. We evaluate the proposed approach on the MVTec AD dataset, a multi-object defect detection dataset. Experimental results show the outstanding performance of our proposed approach compared to the state-of-the-art methods, and the proposed RFS energy outperforms the state-of-the-art in the few shot learning settings. Also, we evaluated the proposed approach on needle dataset, a real life application, to detect defective needle samples given three different views. The results show superior performance compared to DiffeNet. [Display omitted] • A lightweight approach for defect detection based on anomaly detection is proposed. • Transfer learning of deep local features is used instead of global features. • We model these local/point pattern features as a random finite set (RFS). • We propose RFS energy, in contrast to RFS likelihood as an anomaly score. • Results show the outstanding performance of our proposed approach compared to the state-of-the-art methods in few-shot learning. [ABSTRACT FROM AUTHOR]

In recent years, the European Commission and the International Maritime Organization (IMO) implemented various operational measures and policies to reduce ship fuel consumption and related emissions. The effectiveness of these measures relies upon developing accurate predictive models encompassing the influence of real operational conditions. This paper presents a deep learning method for the prediction of ship fuel consumption. The method utilizes big data analytics from sensors, voyage reporting and hydrometeorological data, comprising of 266 variables made available following sea trials of a Kamsarmax bulk carrier of Laskaridis Shipping Co. Ltd. A variable importance estimation model using a Decision Tree (DT) is used to understand the underlying relationships in the available dataset. Consequently, a deep learning model is developed to understand the influence of sailing speed, heading, displacement/draft, trim, weather, sea conditions, etc. on ship fuel consumption (SFC). This is achieved by incorporating attention mechanism into Bi-directional Long Short-Term Memory (Bi-LSTM) network. The potential of the new method is demonstrated by training data streams corresponding to real ship fuel consumption rates as well as internal and external operational conditions. A comprehensive comparison with existing methods indicates that the Bi-LSTM with attention mechanism presents the best fit when using high frequency data. It is concluded that subject to further testing and validation the method could be used for the development of decision support systems for monitoring environmentally sustainable ship operations. [ABSTRACT FROM AUTHOR]

A photovoltaic thermal collector (PV/T) is incorporated into the combined cooling, heating, and power (CCHP) system to decrease primary energy consumption, operation cost and carbon dioxide emission. Six CCHP scenarios based on three strategies are investigated for the energy utilization of the CCHP system. Also, a non-dominated sorting genetic algorithm III using competition crossover and opposition-based learning (NSGA-III-CO) is proposed for the six scenarios. The competition crossover determines the starting point of search in the decision space, and more potential regions can be exploited sufficiently. The opposition-based learning (OBL) carries out random searches and facilitates the convergence of the population. In addition, a constraint handling approach (CHA) is presented to decrease the constraint violations of the infeasible individuals and turn them into feasible individuals, and it guarantees the feasibility of the population. Experimental results suggest that each CCHP scenario with PV/T is more efficient than the one with photovoltaic panel (PV) and the one neglecting PV/T and PV, and it achieves the lowest primary energy consumption, operation cost and carbon dioxide emission. Also, NSGA-III-CO and the other three algorithms are applied to the six CCHP scenarios with PV/T, and it obtains the highest hypervolume and coverage rate for each scenario. [Display omitted] • This paper studies dispatch of combined cooling, heating, and power system (CCHP). • The CCHP involves photovoltaic thermal collector. • An improved NSGA-III is proposed for CCHP. • The improved NSGA-III has exhibited good comprehensive performance for CCHP. • A constraint handling approach is presented to satisfy the constraints of CCHP. [ABSTRACT FROM AUTHOR]

Nowadays, recycling end-of-life (EoL) products has emerged as a vital approach to address resource scarcity. Within the recycling process, disassembly plays a pivotal role and has garnered substantial attention from researchers. Disassembly sequence planning (DSP) is a crucial method to enhance disassembly efficiency. Among the various DSP models, selective disassembly sequence planning (SDSP) has gained prominence as a means to save time and reduce costs. It empowers operators to locate specific components or materials in real time, thereby boosting efficiency and minimising resource wastage. However, a notable research gap exists in the domain of SDSP, particularly in uncertain environments. To bridge this gap and render SDSP solutions more practical for real-world disassembly operations, this study adopts trapezoidal fuzzy numbers to represent uncertain information within the disassembly process and formulates a comprehensive SDSP model. In response to the intricate challenges posed by this problem, we propose a hybrid approach termed nondominated sorting genetic algorithm-II with simulated large neighborhood search (NSGA–II–SLNS). This innovative algorithm leverages the strengths of the nondominated sorting genetic algorithm-II (NSGA-II), simulated annealing algorithm (SA), and large neighborhood search (LNS). Additionally, we introduce several novel search operators into NSGA–II–SLNS, including a crossover and mutation strategy based on chaotic mapping, as well as a local search operator founded on the SA criterion and LNS. To assess the effectiveness of the proposed algorithm and model, extensive numerical case studies are conducted in this research. The outcomes contribute to the advancement of rapid, nearly optimal SDSP strategies in the face of uncertainty and ambiguity in problem settings. • This paper models an uncertain selective disassembly sequence planning problem with trapezoidal fuzzy numbers. • A model for disassembly that minimizes disassembly time and energy consumption and maximizes profit is developed. • A hybrid metaheuristic algorithm combining NSGA-Ⅱ, SA and LNS is applied to solve the model. • Real-world case studies and several stochastic cases validate the proposed model's feasibility in small and large scales. • The hybrid algorithm's superior performance is demonstrated through multi-objective metrics and comparison with popular literature algorithms. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) face challenges related to limited energy resources in sensor nodes, making network lifetime and energy consumption critical considerations. In this paper, we introduce a novel approach to extend network lifetime and reduce energy consumption in WSNs by optimizing network architecture selection. We explore various architecture options, including single-layer and multi-layer networks, to determine the most suitable configuration. Several studies have applied knowledge transfer in evolutionary multitasking optimization to maximize network lifetime, achieving state-of-the-art results for the problem. However, they still have some drawbacks, such as redundant representations, i.e., many genotypes represent the same phenotype, and invalid solution generation. To address these limitations, we propose a new evolutionary multitasking algorithm incorporating efficient encoding to represent tree-structure solutions (network architectures) to the problem. Additionally, we tailor new genetic operators, such as recombination and mutation, to meet the specific requirements of our proposed evolutionary multitasking algorithm. The standout feature of our proposal lies in its ability to consistently generate valid solutions for the problem, which significantly reduces redundancy within the genotype search space and effectively satisfies the problem constraints. Additionally, our multitask evolutionary algorithm facilitates the exploration of various network architectures, harnessing knowledge transfer to enhance optimal performance and reduce computation time compared to single-task methods. We conduct comprehensive experiments on diverse datasets and use statistical tests, e.g., the Wilcoxon signed rank test, to verify the performance of our proposed algorithms. The empirical results demonstrate that our proposed algorithm outperforms existing methods and achieves state-of-the-art results in terms of solution quality, convergence rate, and running time across a wide range of data instances. Specifically, our algorithm provides 49.54% better solution quality on average across all data types compared to the previous state-of-the-art method. [ABSTRACT FROM AUTHOR]

With the guidance of the advanced manufacturing philosophy, green scheduling and energy efficiency have received considerable attention from enterprises and countries. Meanwhile, distributed manufacturing is becoming widespread due to the exploration of the business. Thus, this paper investigates the energy-efficient scheduling of the distributed flexible job shop problem (EEDFJSP) with the goal of minimizing the makespan and total energy consumption (T E C). Considering the difficulty of simultaneously optimizing both objectives, a knowledge-guided bi-population evolutionary algorithm (KBEA) is proposed to address this issue. Firstly, a problem-specific initialization strategy based on a four-vector representation is presented, which corresponds to four sub-problems including factory assignment, operation sequence, machine assignment, and speed assignment. Secondly, five different types of evolutionary operators with adaption strategy is designed to guide the bi-population to complete efficient evolution. Thirdly, a knowledge-guided local search strategy is used to enhance the exploitation capability of the algorithm. Furthermore, an elaborately-designed energy-saving strategy based on knowledge is developed to further reduce energy consumption. Additionally, to verify the effectiveness of the proposed KBEA, extensive experiments are conducted to compare with other 7 comparison algorithms on 39 instances. Experimental results manifest that KBEA is superior to its competitors. [ABSTRACT FROM AUTHOR]

In recent years, collecting data from IoT devices by unmanned aerial vehicles (UAVs) has become a very hot research topic. This paper focuses on the energy consumption problem of a UAV-based IoT data collection system. To solve the considered energy consumption problem, this paper proposes a new population-based optimization algorithm called the backtracking search algorithm with dynamic population (BSADP), which can determine the optimal number and locations of stop points of the UAV simultaneously. In addition, BSADP has a simple framework, which consists of the proposed enhanced backtracking search algorithm (EBSA) and the designed population adjustment mechanism with opposition-based learning (PAMOBL). In the search process, the population is regarded as the entire deployment of the UAV. BSADP firstly generates the trail deployment of the UAV by EBSA and then the next generation deployment of the UAV is produced based on the trail deployment and PAMOBL. The performance of BSADP is investigated by two energy consumption formulations. Experimental results support the superiority of BSADP in optimizing the deployment of the UAV and prove the application value of BSADP in the real scenario. The source code of the proposed algorithm can be found from: https://github.com/jsuzyy/BSADP. • A backtracking search algorithm with dynamic population (BSADP) is reported. • BSADP does not need to do any effort for fine tuning its initial parameters. • BSADP is used to optimize the deployment of a UAV in collecting data of IoT devices. • The excellent global search ability of BSADP is proven. [ABSTRACT FROM AUTHOR]

Cellular technologies have evolved continuously from the 1st to the 5th generation (5G) to meet the exponentially growing needs of bandwidth, throughput and latency. However, the energy consumption experienced a proportional rise generation-wise when new hardware to support additional applications were incorporated. 5G, which already consumes four times more energy than 4G, is expected to bring about a significant spike in the conventional trajectory of energy consumption. 5G is therefore triggering a major concern for energy efficiency and with an even higher technical and network complexity, 6G will pause an unprecedented challenge to energy efficiency and sustainability. This paper focuses on the energy consumption at the base station and access network levels, which amount to around 80% of energy consumption in mobile networks. Machine learning techniques can be employed in several ways to improve the energy efficiency in these components. In this paper, efficient base station deployment strategies and adaptive operational modes as well as access network technologies such as massive MIMO and millimeter waves, which employ machine learning to enhance the energy efficiency, have been reviewed in depth. Since existing research works have focused mostly on a single optimization strategy at either the base station or access network level, this paper proposes a framework, which combines efficient base station deployment methods and machine learning-based switching between different operation modes based on traffic load. Moreover, an adaptive beamforming methodology through identification of hotspots and user association, sub-channel and power allocation in heterogeneous networks is discussed in detail. [ABSTRACT FROM AUTHOR]

As part of the fourth industrial revolution, logistics processes are augmented with connected information systems to improve their reliability and sustainability. Above all, customers can analyse process data obtained from the networked logistics operations to reduce costs and increase margins. The logistics of managing liquid goods is particularly challenging due to the strict transport temperature requirements involving monitoring via sensors attached to containers. However, these sensors transmit much redundant information that, at times, does not provide additional value to the customer, while consuming the limited energy stored in the sensor batteries. This paper aims to explore and study alternative approaches for location tracking and state monitoring in the context of liquid goods logistics. This problem is addressed by using a combination of data-driven sensing and agent-based modelling techniques. The simulation results show that the longest life span of batteries is achieved when most sensors are put into sleep mode yielding an increase of × 21. 7 and × 3. 7 for two typical routing scenarios. However, to allow for situations in which high quality sensor data is required to make decisions, agents need to be made aware of the life cycle phase of individual containers. Key contributions include (1) an agent-based approach for modelling the dynamics of liquid goods logistics to enable monitoring and detect inefficiencies (2) the development and analysis of three sensor usage strategies for reducing the energy consumption, and (3) an evaluation of the trade-offs between energy consumption and location tracking precision for timely decision making in resource constrained monitoring systems. [ABSTRACT FROM AUTHOR]

The traditional flexible job shop scheduling problem (FJSP) ignores transportation issues or merely introduces a time lag for transportation tasks while assuming an infinite number of transportation resources. With the development of intelligent manufacturing, automated guided vehicles (AGVs), which are the key transportation equipment for manufacturing enterprises, have been widely used for their high flexibility and stability. In addition, the increase in energy consumption and the trend of green manufacturing make it critical to take into account energy-related objectives in the decision-making of scheduling. Therefore, the multi-objective green scheduling problem of integrated flexible job shop and AGVs (MOGSP-IFJS&AGVs) is addressed in this paper. To solve this problem effectively, the multi-objective mixed-integer programming (MMIP) model is formulated to minimize total energy consumption and makespan simultaneously. An efficient heuristic algorithm (EHA) is designed to solve the MMIP model. In the EHA, one solution encoding scheme and corresponding greedy insertion decoding method considering the selection of AGVs are presented. To acquire a high-quality initial population, the population initialization method balancing the processing time and energy consumption is designed. Further, a local search strategy is presented to enhance the quality of solutions and accelerate the convergence speed of the EHA. Experiment results of 45 test instances indicate that the EHA can obtain better solutions than that of comparison algorithms, which confirms the effectiveness of the EHA for solving the MOGSP-IFJS&AGVs. [ABSTRACT FROM AUTHOR]

In this review paper, the integration of Machine Learning (ML) and Model Predictive Control (MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided into these three main subsystems: enhancing safety, improving comfort, and reducing fuel consumption and emissions. Due to the development of new technologies such as advancing autonomous and connected vehicles the complexity of these subsystems is increasing. The ACS is meant to encompass the vehicle dynamics, powertrain control, passenger comfort, and accessories. Since vehicle manufacturers must meet stringent performance and emission requirements, optimal control methods for ACS applications are seen as a promising technology. MPC is an optimal control method for closed-loop control applications that allows constraints to be enforced in real-time while an objective function is minimized. The application of MPC in the automotive industry has been shown in the past decade. An important challenge in the design and real-time implementation of MPC is having a accurate predictive model that also does not require excessive real-time computation. Using ML to provide an accurate model at decreased computational cost improves MPC performance of ACS and is the main focus of this paper. How MPC in ML-based ACS applications ensures stability while meeting constraint is also discussed. Method to combine MPC and ML for the ACS subsystems of vehicle dynamics and powertrain control are reviewed and an outlook on future ACS is discussed. [ABSTRACT FROM AUTHOR]

Multi-UAV (unmanned aerial vehicle)-supported mobile edge computing system deploys multiple UAVs as flight edge clouds for large-scale users. In this system, how to optimize the deployment of UAVs is important for providing good service for all mobile users. Specifically, for each mobile user, we need to determine whether the task can be performed locally or on the drone (i.e., the unload decision) and how much resources should be allocated. In this paper, a new two-level optimization method is proposed for UAV deployment and task scheduling problem to minimize the system energy consumption. The proposed algorithm is named TSHGHSDE algorithm, in which a few features are designed to enhance the algorithm performance. Firstly, a two-stage optimization strategy is proposed based on HS-DE algorithm. In the early stage, HS algorithm is used for the local search in a limited space, and the DE algorithm is employed for the global search in the late stage. Secondly, we design an effective control parameter to adjust the search process, which aims to effectively combine the local search ability of HS algorithm and the global search ability of DE algorithm. Thirdly, a novel encoding mechanism is proposed in the process of the UAV deployment optimization. A global search strategy is designed to enhance the performance of the proposed algorithm. To assess the performance of the proposed algorithm, some well-known algorithms are used for comparison in experiments. The results demonstrate that the proposed algorithm performs better in terms of the search efficiency, search accuracy and stability. • A two-stage optimization strategy is proposed based on HS-DE algorithm. • An effective control parameter is designed to balance the local and global search ability. • A novel encoding mechanism is proposed in the process of the UAV deployment optimization. • A global search strategy is designed to enhance the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Modern powertrain control design practices rely on model-based approaches accompanied by costly calibrations in order to meet ever stringent energy use and emissions targets specified for standardized drive cycles. These practices struggle to capture the complexity and uncertainty of real-world driving. However, the deluge of operational data now available with connected vehicle technology presents opportunities to foster data-driven control design methods that adapt the powertrain control systems to their field use conditions. While the most attractive of these methods is reinforcement learning (RL), it is rarely directly applicable in physical applications due to its challenges of learning efficiency (sample complexity) and guaranteeing safety. In this paper, we propose and evaluate an adaptive policy learning (APL) framework that leverages existing source policies shipped with vehicles to accelerate initial learning while recovering the asymptotic performance of a reinforcement learning-based powertrain control agent trained from scratch. We present a critique of related residual policy learning approaches and detail our algorithmic implementations for two versions of the proposed framework. We find that the APL powertrain control agents offer in the order of 10% fuel economy improvement over a default powertrain controller of a commercial vehicle without compromising driver accommodation metrics. We demonstrate that the APL frameworks offer a viable approach towards potentially applying RL for real-world scenarios by addressing its learning efficiency issues. • Adaptive Reinforcement Learning (RL) of vehicle powertrain control for eco-driving • Accelerated policy learning by leveraging existing source control policies • Evaluation of proposed algorithms with conventional RL via Monte Carlo simulations [ABSTRACT FROM AUTHOR]

Sensors deployment is one of the most fundamental issues in wireless sensor networks (WSNs) design. One of the major challenges in sensors deployment is to find a tradeoff between conflicting objectives of network, coverage and lifetime, under certain connectivity constraints. This paper proposes a constrained Pareto-based multi-objective evolutionary approach (CPMEA) which aims at finding Pareto optimal layouts that maximize the coverage and minimize the sensors energy consumption for the sake of prolonging the network lifetime, while maintaining the full connectivity between each sensor node and the high energy communication node (HECN). In the proposed CPMEA, certain problem-specific operators are designed to direct the search into feasible regions of the search space. For this purpose, during the evolution, the designed operators are adapted to the objectives as well as constraints of the problem in order to make overall improvements on the CPMEA performance. In this paper, the proposed method is numerically examined in certain WSN test instances and a study of its performance is carried out using certain performance metrics. The results have shown the effectiveness of the designed operators as well as the superiority of the proposed approach over the non-dominated sorting genetic algorithm-II (NSGA-II). [ABSTRACT FROM AUTHOR]

Worldwide, buildings are responsible for almost 30% of energy consumption, and those buildings that intensively use refrigeration systems, such as supermarkets and grocery stores, are also among the most energy-intensive consumers. Refrigeration devices, either commercial or residential, are responsible for a significant part of net emissions. Based on careful measurements, it is possible to reduce energy consumption in these devices by up to 15% only by improving the fault detection and diagnosis techniques. Thus, improving maintenance programs has become a crucial area in energy management in recent years. Nowadays, the market has experienced a hike after smart systems and new network interfaces applied to smart buildings that have allowed previously isolated devices to become smart devices, interacting with control algorithms smartly and, to some extent, autonomously. Here, we propose a reinforcement learning framework to develop a maintenance policy for mechanical compression refrigeration devices. Firstly, a test bench is built in which each component is assigned to be individually repairable and individually degradable in parallel and interconnected processes. Then, the degradation of the components is combined to formulate the system degradation, and the optimal maintenance policy is modeled via Markov decision processes and solved by a reinforcement learning algorithm. The agent-proposed maintenance program if compared to corrective maintenance, managed to reduce energy use and emissions by around 6% while avoiding shortfalls, as well as about the preventive program, where the agent managed to accomplish the same level of energy efficiency while reducing the maintenance costs by 31% and the time under maintenance in 10%. It was found that the reinforcement learning frameworks applied to maintenance have a series of challenges but are innovative and can show promising results compared to traditional maintenance techniques, such as preventive and corrective ones. • Several frameworks of Condition Based Maintenance had been proposed for freezers. In this paper, we present Reinforcement Learning as an alternative tool. • Hidden Markov Models and Markov Decision Processes were implemented to model degradation schemes in parallel and competing processes. • The Double Deep Q-Learn algorithm is used to solve the problem of maintenance scheduling for freezers. • Reinforcement Learning applied to maintenance schedule can reduce emissions, and maintenance costs and increase the availability of refrigeration devices. [ABSTRACT FROM AUTHOR]

The accurate and reasonable prediction of natural gas consumption is important for enhancing national energy security and alleviating the environmental problems caused by energy consumption. Starting with the traditional nonlinear grey Bernoulli model, in this paper, we expand the development coefficient a and grey action quantity b, propose a new self-adaptive time-varying grey Bernoulli prediction model, deduce the time response formula of the model, and explore the relationship between model parameters and model accuracy. Then, the nonlinear parameter and power parameter coefficient of the new model are optimized by a genetic algorithm, and the effectiveness of the model is analyzed by the natural gas consumption of China, the United States, and Russia. The results show that, compared with other traditional grey prediction models, the new model has a better simulation effect, higher prediction accuracy, and stronger applicability. Meanwhile, the model was used to forecast the natural gas consumption of China, the United States, and Russia in the period from 2022 to 2026. The prediction results show that China's natural gas consumption will continue to steadily increase in the future, which is consistent with the global goal of adhering to the orderly development of natural gas. The prediction results can provide a theoretical basis for relevant departments to formulate relevant natural gas policies. [ABSTRACT FROM AUTHOR]

Shape-shifting robots are the feasible solutions to solve the Complete Coverage Planning (CCP) problem. These robots can extend the covered areas by reconfiguring their shape to different forms according to space conditions. Since energy usage while navigating is constrained by the number of shape-shifting, it is desirable to cover the confined areas by trajectory using minimal robot actions within finite states. This paper presents a CCP method using deep reinforcement learning (DRL) for a reconfigurable robot with a trapezoid shape called Transbot. The framework derives optimal action policy for robot trajectory within the grid-based workspace. DRL model relies on Convolutional Neural Networks (CNNs) with Long Short Temporary Memory (LSTM) layers using Actor-Critic with Experience Replay (ACER) as the decision layers. The trained DRL model simultaneously generates robot shapes and directions with optimal energy cost by maximizing the cumulative reward representing the Transbot actions. By creating the trajectory with less 28.95% energy and 19.42% time in tested simulation and real-world experiments, the proposed CCP framework outperforms the existing tiling-based heuristic optimization techniques. • The transformable Transbot shifts flexibly between expand and compress shapes. • DRL based CCP allows Transbot robot plan to shape and the trajectory simultaneously. • DRL with the reward derives from the reconfigurable Transbot action. • The DRL model is validated in simulation and real environments. • The framework saves 28.95%, 19.42% the energy, and time spent. [ABSTRACT FROM AUTHOR]

The pervasive application of artificial intelligence and machine learning algorithms is transforming many industries and aspects of the human experience. One very important industry trend is the move to convert existing human dwellings to smart buildings, and to create new smart buildings. Smart buildings aim to mitigate climate change by reducing energy consumption and associated carbon emissions. To accomplish this, they leverage artificial intelligence, big data, and machine learning algorithms to learn and optimize system performance. These fields of research are currently very rapidly evolving and advancing, but there has been very little guidance to help engineers and architects working on smart buildings apply artificial intelligence algorithms and technologies in a systematic and effective manner. In this paper we present B-SMART: the first reference architecture for autonomic smart buildings. B-SMART facilitates the application of artificial intelligence techniques and technologies to smart buildings by decoupling conceptually distinct layers of functionality and organizing them into an autonomic control loop. We also present a case study illustrating how B-SMART can be applied to accelerate the introduction of artificial intelligence into an existing smart building. [Display omitted] • B-SMART is a reference architecture that accelerates the application of artificial intelligence in smart buildings. • B-SMART supports autonomic smart building operations. • B-SMART supports both startup and ongoing commissioning. [ABSTRACT FROM AUTHOR]

Data transmission for Industrial Internet of Things (IIoTs) is of the utmost importance, especially in the Industry 5.0 era, where human–machine collaboration is increasingly intensive. On the one hand, as a key characteristic for Industry 5.0, deterministic transmission aims to ensure the data arrive at destination devices accurately, has attracted remarkable attentions. On the other hand, energy efficiency problem is another major concern in Industry 5.0 since the massive Machine-Type Devices (MTDs). Consequently, in this paper, we investigate the tradeoff between energy efficiency and delay determinacy in energy harvesting-powered Zero-Touch Deterministic Industrial Machine-to-Machine (ZT-DI-M2M) communications. In particular, we first derive the probability of the transmission delay falling within a certain time window to characterize the delay determinacy and then figure out the relationship between delay determinacy and the energy efficiency by taking the system control performance into account. After that, in order to model the uncertainty of the stochastic environment, we formulate the problem as a stochastic game by jointly considering transmission power control, frequency spectrum allocation and the selection of base stations. Afterwards, a random graph-based sparse Long Short-Term Memory (LSTM) network is proposed to solve the optimization problem while reducing the computational complexity. Finally, numerical result demonstrates that the proposed sparse LSTM algorithm enables to achieve a specific delay determinacy with a higher energy efficiency as compared to Deep Q-Learning Network (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms. In addition, we also analysis the influence of sparse coefficient and the size of time window on the relationship between energy efficiency and delay determinacy. [ABSTRACT FROM AUTHOR]

For computation offloading problem (COP) in mobile edge computing (MEC), the energy consumption of terminal equipments(TEs) and the delay of mobile equipment applications are two optimization goals. In real life, terminal equipment is dynamic, and their number, mobility, and continuous changes in wireless channels will affect the balance between the mentioned energy consumption and delay. Different from available works, we model the COP in MEC as a dynamic multi-objective problem (DMOP) in this paper, and propose an improved dynamic multi-objective evolutionary optimization based on decomposition (DMOEA/D) to solve it, namely DMOEA/D-COPMEC. In the proposed algorithm, the environmental change is detected by a fixed detector, and whether the current change is similar to the historical change is determined. If so, the difference prediction is used to re-locate the population individual in the new MEC environment, otherwise, the memory-based strategy is used to response environmental change. In MOEA/D, an adaptive weight adjustment strategy based on chain segmentation (CS) is adopted to generate a set of uniformly distributed weight vectors. The simulation results show that the proposed algorithm can better balance the application delay and the terminal energy consumption if there is environment change. The solution set is closer to reality and better than the related algorithms. [ABSTRACT FROM AUTHOR]

Uniform energy consumption and its optimization is a major concern for designing the clustering and routing protocol for large scale wireless sensor networks. Most of the existing nature-inspired computing based solutions for cluster-based routing problem for wireless senor network, suffer from unbalanced energy consumption issues. This is due to fact that nodes near to the sink are overloaded in terms of traffic load. In this research work, problem of energy balanced node clustering and routing between cluster heads and the sink are considered. This paper proposed an improved cuckoo search-based energy balanced node clustering protocol which uses a novel objective function for uniform distribution of cluster heads. In addition, an improved harmony search based routing protocol is proposed for routing of the data packet between cluster heads and the sink. The performance of the proposed integrated clustering and routing protocol is evaluated using the average energy consumption, the number of alive nodes, number of dead nodes and network lifetime. The proposed Cuckoo–Harmony Search-based integrated clustering and routing protocol shows significant improvement over the state-of-art protocols. [ABSTRACT FROM AUTHOR]

Deep anomaly detection (DAD) is essential in optimizing building energy management. Nonetheless, most existing works concerning this field consider unsupervised learning and involve the analysis of sensor readings through a one-dimensional (1D) energy time series, which limits the options for detecting anomalies within the building's energy consumption. To the best of the authors' knowledge, this paper presents the first study that explores using two-dimensional (2D) image representations as features of a supervised deep transfer learning (DTL) approach. Specifically, using 2D image representations allows taking advantage of any underlying data within the feature set, providing more possibilities to encode data and detect pertinent features which may not be considered in standard 1D time-series. Furthermore, the effects of using CNN activations as machine learning (ML) model features are also investigated to combine the advantages of both techniques. Additionally, the concept of layer and hyperparameter variation for the CNN model is also studied, with the objective of reducing the overall time computation and resource requirements of the proposed system. Hence, this makes our approach a candidate for edge-based applications. As per the conducted experiments, the top methodology rests at the F1-scores of 93.63% and 99.89% under simulated and real-world energy datasets, respectively. This involves using grayscale 2D image representations that combine CNN activations extracted from AlexNet and GoogleNet pre-trained models as features to a linear support vector machine (SVM) classifier. Finally, the comparison analysis with the state-of-the-art has shown the superiority of the proposed method in various assessment criteria. • Deep anomaly detection of energy consumption using energy time-series images. • Converting 1D energy time series into 2D representations. • 2D energy representations as features of a neural network-based transfer learning. • Using neural network activations as machine learning model features. • Combining neural network activations extracted from AlexNet and GoogleNet. [ABSTRACT FROM AUTHOR]

Due to the increasingly severe resource scarcity and environmental pollution, appropriate recovery of end-of-life (EOL) products has gained significant importance in recent years. The disassembly lines are one of the vital steps during the entire recovery process. However, considering the disadvantages of manual disassembly lines such as high labor cost, low efficiency, and harm to workers' health, the transition to robotic disassembly lines is underway. Therefore, in this paper, a sustainable robotic disassembly line balancing problem (RDLBP) is proposed to improve the disassembly efficiency (cycle time) and environmental friendliness (total energy consumption) simultaneously. To narrow the gap between theory and practice, uncertain processing time, sequence-based tool changeover, and robots with different efficiencies and energy consumption rates are considered, which requires adding the steps of resequencing the tasks and allocating the most suitable robots within each workstation on the basis of assigning the tasks to workstations as evenly as possible. To solve this problem, the epsilon constraint algorithm is used to obtain the exact solutions for small scale cases and verify the validity of the proposed model. Simultaneously, due to the NP-hard nature, a modified bi-objective Harris Hawks optimization (MBOHHO) algorithm is developed to solve the line balancing problem for the first time, which combines opposition-based learning, differential evolution, and Gaussian mutation mechanisms, and modifies the renewal strategy of two vital parameters based on bioenergy consumption pattern, contributing to improve the diversity and help jump out of local optimum. Finally, computational experiments are performed to evaluate the performance of the MBOHHO algorithm by comparing it with four other outstanding optimization algorithms, and the results reveal its effectiveness and superiority in various metrics. [ABSTRACT FROM AUTHOR]

Minimizing energy consumption is an important issue in robotic assembly lines where a set of robots are assigned to a set of workstations to perform different tasks. When it is planned to assemble several models of one product in the same robotic assembly line, the minimization of energy consumption becomes more difficult since the best assignment of tasks and robots to workstations must be found, taking into consideration all models. The authors cannot find in the literature a work that aims to minimize energy consumption in robotic assembly lines that produce several models with one configuration. Furthermore, the introduction of the heterogeneity of models and robots makes the problem more complex and hard, even for small-scale instances, and for this reason, the authors propose in this paper a Memory-Based Cuckoo Search Algorithm (MBCSA) to tackle this problem. The principle of memory is used in this new Cuckoo Search Algorithm in order to escape from the local optima and discover new search zones. Six problems of different sizes are generated and solved by the proposed MBCSA, and to evaluate its performance, two comparisons are made with two meta-heuristics, the genetic algorithm and another version of the cuckoo search algorithm. Obtained results show that this new version of the Cuckoo search algorithm is promising and can obtain good solutions for problems of different sizes. • Minimizing the energy consumption in Robotic mixed-model assembly line. • Proposing a new version of the Cuckoo Search Algorithm to solve problem. • Showing the importance of using the principal of the memory while solving complex optimization problems. • Problem never solved before. [ABSTRACT FROM AUTHOR]

Evolutionary robotics is concerned with optimizing autonomous robots for one or more specific tasks. Remarkably, the energy needed to operate autonomously is hardly ever considered. This is quite striking because energy consumption is a crucial factor in real-world applications and ignoring this aspect can increase the reality gap. In this paper, we aim to mitigate this problem by extending our robot simulator framework with a model of a battery module and studying its effect on robot evolution. The key idea is to include energy efficiency in the definition of fitness. The robots will need to evolve to achieve high gait speed and low energy consumption. Since our system evolves the robots' morphologies as well as their controllers, we investigate the effect of the energy extension on the morphologies and on the behavior of the evolved robots. The results show that by including the energy consumption, the evolution is not only able to achieve higher task performance (robot speed), but it reaches good performance faster. Inspecting the evolved robots and their behaviors discloses that these improvements are not only caused by better morphologies, but also by better settings of the robots' controller parameters. • We investigate an evolvable robot system where controllers and morphologies evolve. • Robots' energy usage is approximated using a mathematical battery model. • Fitness functions are the robot's gait speed and robot's energy consumption. • Energy efficiency in fitness improves task performance; changes the morphology. [ABSTRACT FROM AUTHOR]

Traffic congestion causes important problems such as delays, increased fuel consumption and additional pollution. In this paper we propose a new method to optimize traffic flow, based on reinforcement learning. We show that a traffic flow optimization problem can be formulated as a Markov Decision Process. We use Q -learning to learn policies dictating the maximum driving speed that is allowed on a highway, such that traffic congestion is reduced. An important difference between our work and existing approaches is that we take traffic predictions into account. A series of simulation experiments shows that the resulting policies significantly reduce traffic congestion under high traffic demand, and that inclusion of traffic predictions improves the quality of the resulting policies. Additionally, the policies are sufficiently robust to deal with inaccurate speed and density measurements. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to medium access control for single-hop wireless sensor networks. The ALOHA-Q protocol applies Q-Learning to frame based ALOHA as an intelligent slot selection strategy capable of migrating from random access to perfect scheduling. Results show that ALOHA-Q significantly outperforms Slotted ALOHA in terms of energy-efficiency, delay and throughput. It achieves comparable performance to S-MAC and Z-MAC with much lower complexity and overheads. A Markov model is developed to estimate the convergence time of its simple learning process and to validate the simulation results. [ABSTRACT FROM AUTHOR]

The petrochemical industry evaluation is affected by numerous factors. Many previous studies proposed a use of data envelopment analysis (DEA) as a methodology for energy efficiency analysis in the petrochemical industry. However, excessive decision-making units (DMUs) of DEA model result in difficulties in evaluation and comparison of the different DMUs. In this paper, a new energy analysis framework of petrochemical industrial processes based on DEA integrated interpretative structural model (ISM) is proposed. The ISM method is brought up based on the partial correlation coefficient method to find the main factors and basic reasons that affect the energy consumption of the ethylene production system, which serve as the inputs of the DEA. Meanwhile, ethylene, propylene and C4 productions of the ethylene production system sever as the outputs of the DEA. Then the fractional DEA model is solved by using the linear programming method. The proposed evaluation method can overcome the shortcomings of the DEA model mentioned above, and also is able to reflect the effectiveness of the DMUs and guide the improvement directions of the ineffective DMUs based on slack variables. Our approach is applied in the energy efficiency analysis of Chinese ethylene industry in the petrochemical field. The empirical results show that the proposed energy consumption analysis method is valid and efficient in improvements of energy efficiency in ethylene production systems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel integrated framework for nesting (i.e., part orientation selection and two-dimensional packing) and scheduling parts assigned to batches (or jobs) on additive manufacturing machines. For the first time, a tri-objective optimization model is designed that interprets profit, energy utilization of machines, and goodwill losses (i.e., tardiness, negotiation, and increasing due date) as three sustainability criteria. Besides, considered negotiation plans may reduce the prices of ordered parts for a possible increase in the initial due date set by customers. The model's scalability is supported by tailoring three algorithms: robust improved ɛ -constraint method, non-dominated sorting genetic algorithm (NSGA-II), and multi-objective grey wolf optimizer (MOGWO). Several insights are derived by analyzing the model's sensitivity to its key parameters and validating its applicability by a case study of Amazon's last-mile delivery process. The results confirm the conflicting objectives, suggested action plans, and proposed algorithms. [Display omitted] • Developing a flexible/faster nesting method in additive manufacturing (AM) systems. • Considering sustainability criteria for nesting and scheduling in AM systems. • Proposing a framework to set negotiation plans for scheduling and nesting decisions. • Developing a representation method for meta-heuristic algorithms. • Deriving insights for a last-mile delivery system in AM by a real-world case study. [ABSTRACT FROM AUTHOR]

One of the main priorities for metro line operators is the reduction of energy consumption, due to the environmental impact and economic cost. The new moving block signalling system CBTC (Communication Based Train Control) is being installed in order to increase the transport capacity of new metro lines, and to upgrade lines equipped with the former fixed block signalling systems. In addition, this new technology could be used to improve energy efficiency in the traffic operation, due to its capability to update the ATO (Automatic Train Operation) driving commands not only at stations but also along the journey. In this paper a new optimization algorithm is proposed to design ATO CBTC speed profiles to minimize energy consumption, generating the Pareto optimal curve. The algorithm is a multi-objective NSGA-II-F based on simulation of the train motion under uncertainty. The main source of uncertainty in the consumption calculation is the mass of the train, which is dependent on the passenger load, and it is modeled as a fuzzy number. In addition, a new method is proposed to fill running time gaps with dominated points, providing a pseudo-Pareto curve. The NSGA-II-F algorithm has been applied to design ATO CBTC optimal speed profiles in a case study of Metro de Madrid, providing additional energy savings up to 7.3% compared to former fixed block signalling systems and a well time-distributed pseudo-Pareto curve for the running times required by the traffic regulation system. [ABSTRACT FROM AUTHOR]

Abstract: Energy efficient clustering and routing are two well known optimization problems which have been studied widely to extend lifetime of wireless sensor networks (WSNs). This paper presents Linear/Nonlinear Programming (LP/NLP) formulations of these problems followed by two proposed algorithms for the same based on particle swarm optimization (PSO). The routing algorithm is developed with an efficient particle encoding scheme and multi-objective fitness function. The clustering algorithm is presented by considering energy conservation of the nodes through load balancing. The proposed algorithms are experimented extensively and the results are compared with the existing algorithms to demonstrate their superiority in terms of network life, energy consumption, dead sensor nodes and delivery of total data packets to the base station. [Copyright &y& Elsevier]

This paper describes the generation of sketches of small single-family dwellings that satisfy habitability requirements and are energy efficient. The proposed approach considers three stages in the generation process, and each one is based on a combination of shape grammars and reinforcement learning. First a set of very simple shape grammar rules is defined that are capable of generating a great variety of sketches. In order to guarantee the generation of sketches that are both suitable for habitation and energy efficient, a reinforcement learning process is applied on this set. Then the grammar so trained is used to generate only "good" sketches. More precisely, the learning process applies positive rewards to sketches that satisfy desired habitability and energy efficiency guidelines. As a result, sequences of grammar rules that lead to good sketches are identified. In this paper we present the general approach followed to develop the system and describe in detail the procedure applied in the reinforcement learning process. Experimental results are also presented, to show convergence of the learning process, and to compare the obtained results with those of real designs. A standard energy simulation program is used to validate the approach. [ABSTRACT FROM AUTHOR]

The extremely sensitive and highly non-linear search space of interplanetary transfer trajectory design brings about big challenges on global optimization. As a representative, the current known best solution (fuel consumption) of the global trajectory optimization problem designed by the European space agency is very hard to be found. To deal with this difficulty, a powerful differential evolution with the guided movement for population, named G_DE is proposed in this paper. G_DE employs a two-stage evolutionary process, which concentrates on learning global structure in the earlier process, and tends to self-adaptively probe the structures of numerous local spaces at a later stage. During these two stages, four guidance strategies related to the learning from the population distribution are proposed. The experimental test results show that G_DE can find the current known best solutions of Cassini1 and Sagas directly. For the newly proposed GTOP-X problem, G_DE has found state-of-the-art solutions for four encounter sequences, and using five gravity assists, G_DE found encounter sequences with fuel consumption as low as 1.5676 km/s. The experimental test on 10D CEC2017 problems proves G_DE has a comparable performance with recently proposed differential evolution variant. [ABSTRACT FROM AUTHOR]

The growing energy demand and environmental consciousness provoke the conventional single-objective optimization framework no longer satisfies new power system planning and control requirements. The number of optimization objectives being considered in power system optimization is increasing, necessitating the development of many-objective Optimal Power Flow (OPF) problems and the development of solution methods. In this paper, the fitness functions for the Many-Objective OPF (MaO-OPF) problem have been formulated, and a new Many-Objective Gradient-Based Optimizer (MaOGBO) based on reference point strategy is proposed to solve the MaO-OPF problem. The objectives of the MaO-OPF problem is to minimize the Reactive Power Loss (RPL), Active Power Loss (APL), Voltage Magnitude Deviation (VMD), Voltage Stability Indicator (VSI), Total Emission (TE), and the Total Fuel Cost (TFC) by satisfying different complex equality and inequality constraints. In the proposed MaOGBO, a reference point strategy is employed to acquire evenly spread Pareto -optimal solutions in each objective space. In order to improve the effectiveness of Pareto solutions, a mixed-multi-constraint handling approach is also implemented. In order to deal with the complex non-linear constraints, the process utilizes both the repair technique and penalty function. Besides, a fuzzy-based membership function strategy is also applied to locate the Best Compromise Solution (BCS) from the Pareto -optimal analytical solution. In order to validate the effectiveness of the proposed MaOGBO, DTLZ and MaF benchmark test suites are considered. In addition, a standard Institute of Electrical and Electronics Engineers (IEEE) bus test systems, such as IEEE-30/IEEE-57/IEEE-118 with different case studies, are also considered to assess the performance of the proposed algorithm. The obtained results are compared with other state-of-the-art algorithms, and the proposed MaOGBO proves the superiority over other competitors for most of the selected problems, including MaO-OPF. Finally, the proposed MaOGBO is also validated on large-scale Algerian 59-bus power systems and proved its superiority in handling realistic systems. This research is further supported up with extra online service and guidance at https://premkumarmanoharan.wixsite.com/mysite. • MaOGBO is developed using a reference-point mechanism to obtain the solution diversity and faster convergence. • MaOGBO is combined with a multi-constraint handling mechanism and fuzzy membership-based best compromise solution strategy. • The performance of the MaOGBO is validated on 22 benchmark test suites and three test frameworks of MaO-OPF problems, and also validated on large scale Algerian 59-bus real-time system. • Performance metrics, such as GD, IGD, SP, HV, and RT are calculated for all selected problems. • Three different statistical significance tests are carried out to assess the performance of MaOGBO. [ABSTRACT FROM AUTHOR]

This paper studies an important extension of energy-efficient production scheduling research, where machine on/off control and machine maintenance are considered simultaneously. The inspiration of this extension is that a machine must be turned off if it needs to be maintained, and an already-turned-off machine can be maintained without needing to be restarted. We therefore formulate an energy-efficient production scheduling problem with machine maintenance through machine on/off control, aiming to optimise three objectives – the makespan, total number of machine restarts, and energy consumption – at the same time. Four rules are designed to set the machine on/off criteria, maintenance periods and predefined maintenance windows, based on solutions of the job shop scheduling problem (JSP) as a test case. Three heuristics are proposed to insert the maintenance activities into the solutions and move their maintenance-operation blocks to optimise the objectives. The effectiveness of the first rule and the moving of maintenance-operation blocks have been proven mathematically. Our proposed heuristics, unlike traditional heuristic algorithms, are expected to be applicable and effective even if we change the objectives and constraints, require minimal computational time (only a few seconds) to optimise a scheduling solution, and can solve different types of scheduling problems without needing any modification. Experiments undertaken indicate promising performance of the proposed heuristics based on 182 JSP benchmark instances. • Energy-efficient scheduling is achieved via machine on/off control during maintenance. • Four rules are devised to set on/off criteria, maintenance periods and time windows. • Three heuristics are designed to set maintenance activities and move operation blocks. • Results on 182 problem instances confirm the efficacy of the proposed heuristics. [ABSTRACT FROM AUTHOR]

This paper deals with the sink placement and anycast routing to increase the lifetime of multi-sink wireless sensor networks. Two algorithms are proposed, namely "Multi-sink Placement and Anycast Routing (MPAR)" and "Extended Multi-sink Placement and Anycast Routing (EMPAR)", to jointly address the problems of clustering, multi-sink placement, and load-balanced anycast routing. MPAR and EMPAR rely on a two-level architecture in which sensors are clustered at the lower level. Each sensor transmits its data to the corresponding Cluster Head (CH) via a load-balanced data aggregation routing tree. At the upper level, both schemes use a modified particle swarm optimization algorithm to determine the best location of sinks. For each sink, a high-level anycast routing tree is developed using the ant colony optimization algorithm. Each anycast tree uses the hybrid Compressive Sensing (CS) method to forward the aggregated data from CHs to sinks. Extensive simulations are conducted to illustrate the efficiency of the proposed algorithms in terms of energy consumption, energy consumption variance, and network lifetime. The results show that EMPAR has a better performance than MPAR due to its CH selection strategy. As an advantage, EMPAR considers both remaining energy and distance criteria along with a rest factor to select the best CH for each cluster. For an average number of clusters, EMPAR reduces the energy consumption by 5.98% and 12.20%, respectively, compared to the MPAR algorithm and the energy-aware CS-based data aggregation algorithm. It also increases the network lifetime in comparison with the aforementioned algorithms by 12.26% and 30.38%, respectively. [ABSTRACT FROM AUTHOR]

Accurately forecasting energy demand can better predict future changes in energy demand. This study is aimed to develop more accurate clean energy prediction model from two perspectives of the modeling mechanisms and improvements on the model structure. For this purpose, a novel Riccati–Bernoulli differential equation is established through social practice theory, supply–demand relationship, and the preference of consumption in the energy economy. Considering the limited information in observation data, this differential equation is then transformed into a grey Riccati–Bernoulli model (GRBM(1,1)) according to the differential information principle. With the response function solved on the basis of polynomial equation theory and the power exponent optimized combining the modified flower pollination algorithm, the main process of GRBM(1,1) can be summarized. The four validation examples are provided for confirming the effectiveness and reliability of the new model by comparing with other existing models. Finally, the proposed model is employed to estimate and forecast the clean energy consumption in China and India. The results show that the proposed model demonstrates better estimation in all cases and efficiency in short-term clean energy consumption forecasting. Therefore, by using this optimum model, China's preference coefficient in total energy consumption is 0.7103, lower than that of India (0.8799), and China's preference coefficient in clean energy consumption is 0.9665, higher than that of Indian (0.7155), which means China has less interest to increase total energy consumption but more interest in popularizing the clean energy. • This paper proposed a novel grey Riccati–Bernoulli model for energy consumption prediction. • A specific solution algorithm for this Riccati–Bernoulli equation is discussed in this research. • We compared the proposed novel grey model with other 12 exiting prediction models. • The novel model also reveals China's effort in popularizing the use of clean energy. [ABSTRACT FROM AUTHOR]

The production rate of an ore crushing circuit depends on the amount of equipment in operation. If the amount of active equipment is less than the optimum level, the reduced ore flow paths restrict the production rate. However, if the amount of active equipment is greater than the optimum level, the excess circulating load ore and extra energy consumption reduce the circuit efficiency. In addition, the optimum amount of active equipment can change over time due to changes in the ore characteristics, such as hardness and particle size. In this paper, a decision support system is proposed for optimizing the amount of active equipment for maximum crushing production considering changes in the circuit feed rate. The proposed solution is based on a simheuristic approach in which a simulated plant model is used to evaluate the production rate. Real production scenarios at a Brazilian mining plant are used in computational experiments. The results show that the simheuristic solutions generate a higher production rate and result in less energy consumption. Production is increased by up to 9%, and energy consumption is reduced by up to 59%, demonstrating the efficacy of the proposal. • A decision support system for equipment operation in a crushing circuit is proposed. • The proposed approach is based on a simheuristic strategy in an industrial simulator. • The objective function aims to maximize the production of a crushing plant. • Industrial cases are used to validate the effectiveness of the decision system. • The results show a 9% increase in production and a 59% reduction in energy consumption. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), it is critical to design an advisable transmission power allocation strategy for balancing the latency and energy efficiency, and prolonging the lifetime of WSNs. However, some measured key parameters, e.g., data latency, energy consumption and communication radius, are with high levels of uncertainties, which deteriorate the transmission power allocation performance greatly. How to employ an advanced method to deal with the uncertainties and to further improve the network performance is a pressing issue. Type-2 fuzzy logic system (T2FLS) as a powerful tool for handling the uncertainties provides an effective way for designing such advisable allocation strategies. Therefore, this paper adopts the interval T2FLS (IT2FLS) to design the transmission power allocation (TPA) strategy for lifetime maximization of WSNs. Firstly, the problem of lifetime enhancement in WSNs is formulated in detail, and then it is converted into a TPA problem. Secondly, the IT2FLS method is applied to the transmission power decision making process for maximizing the lifetime of WSNs. In the designed IT2FLS-based TPA strategy, expected latency, residual energy and distance between nodes are taken as input variables, while the transmission power and communication radius are considered as the output variables. Finally, both simulation and experiment results are given. The results indicate that the proposed TPA strategy using IT2FLS can effectively realize the tradeoff between the latency and energy efficiency, and can prolong the network lifetime of the WSNs. Moreover, compared with other TPA strategies, including the minimum total energy algorithm, the flow augmentation algorithm and the type-1 fuzzy logic method, the proposed IT2FLS-based TPA strategy has obvious advantages in terms of network lifetime, average latency and energy consumption. [ABSTRACT FROM AUTHOR]