Proposed Optimization Algorithm Research Articles

Social Media Marketing Optimization Method Based on Deep Neural Network and Evolutionary Algorithm

Under the background of the vigorous development of China’s market economy, the marketing mix is constantly updated, which promotes the all-round development of various industries. Social media marketing has formed a relatively solid theoretical and practical foundation, especially with the continuous updating and iteration of Internet technology and the improvement of people’s requirements for experience, and we must find ways to optimize the methods of social media marketing. This study mainly introduces several optimization methods of social media marketing based on deep neural networks and advanced algorithms, and the experiments of gradient-based back-propagation algorithm and adaptive Adam’s optimization algorithm show that the proposed optimization algorithm can easily achieve the global optimal state based on the combination of back-propagation algorithm and Adam’s optimization algorithm. Accuracy of marketing is very important, so we introduce a scheme of how to accurately market, and the scheme is effective. Firstly, the FCE model is constructed by a three-layer back-propagation neural network, and then, the data input layer is designed to achieve the effect of the model.

A Many-Objective Multistage Optimization-Based Fuzzy Decision-Making Model for Coal Production Prediction

The traditional coal energy system cannot meet the requirements of modern industrial production for sustainable development because extensive economic growth has caused significant coal consumption. Therefore, it is particularly important to formulate an effective coal production decision-making scheme to support complex coal production systems for sustainable development. To address this challenge, an improved many-objective fuzzy decision-making model with human participation for coal production prediction is established in this article. The model includes five objective functions: economic, energetic, environmental, coal gangue, and safety profits. Moreover, a novel multistage many-objective optimization algorithm is designed to adjust relevant model parameters and make the obtained model solution more accurate when compared with involved algorithms. In the algorithm, there are three stages of optimization. In stage one, the model with the relatively good convergence solution is chosen. In stage two, the diversity of the model solution needs to be retained by employing a diversity maintenance mechanism. In stage three, a comprehensive measurement approach is designed to ensure the balance of convergence and diversity during population evolution. To verify the validity of the model and the superiority of the optimization algorithm, five other advanced many-objective algorithms are used for comparison. The results from simulations on a benchmark function show that the proposed optimization algorithm has better performance than involved algorithms. Model solutions obtained on different metrics have better convergence and diversity for the coal fuzzy decision-making problem.

Optimal multi-objective sizing of a residential microgrid in Egypt with different ToU demand response percentages

Many countries encourage the establishment of microgrids to support the spread of renewable energy sources, as it is the best option for dealing with electricity problems and air pollution. In this study, four sizing scenarios of a residential microgrid in a northern Egyptian city surrounded by rural areas are introduced as an interpretative example to explore the optimal scheduling strategy. This paper’s novelty originated from the techno-economic and ecological performance comparison of different sizing strategies for a residential load based on renewable sources, considering load shifting algorithms and using rice straw as biomass fuel instead of its open-field burning. The proposed optimization algorithm illustrates that the PV/WG/Biomass/two-ways-grid-connection microgrid is the best investable-reliable sizing option with a minimum net present cost of about ($ 0.7073 million), which is reduced by ($ 0.1664 million) when 30% demand response participation is applied. Also, the PV/WG/Biomass system has the most significant environmental impact with a CO2 emissions reduction of around (3.465*108 kg).

RDO-WT: optimised Wallace Tree multiplier based FIR filter for signal processing applications

ABSTRACT Digital Signal Processing (DSP) and communication applications utilise a Finite Impulse Response (FIR) filter which is key component for digital communication. Moreover, quality of signal is enhanced by FIR filter which is mostly employed by filtering applications. The execution speed of the FIR is determined by multiplier’s performance. Hardware resource utilisation and speed-up operations are some of the issues found in existing methods which are occurred due to a critical path in incomplete products. So, this can be improved by the proposed optimisation algorithm based multiplier. This research establishes the execution of FIR Filter design using Red Deer Optimisation (RDO) based Wallace Tree (WT) multiplier. By using the optimised WT multiplier, FIR filter is simulated to examine the area, delay, power and frequency for different tap FIR filter. Moreover, FIR filter with optimised WT is introduced to perform signal denoising application. Proposed multiplier design is simulated using Xilinx tool. Filtering simulation is done by using the publicly available database named as MIT-BIH database and the noise removal process is simulated using Matlab tool. The FIR filter designed with proposed multiplier provides delay around 1.373 ns and consumes 0.143 W as total power which is lesser than the existing methods.

Seismic resilient three-stage enhancement for gas distribution network using computational optimization algorithms

A computer-simulation-based three-stage optimization strategy is proposed for the resilience enhancement of urban gas distribution networks (GDNs). In stage I (pre-earthquake stage), the Fixed Proportion and Direct Comparison Genetic Algorithm (FPDC-GA) is applied to select key pipelines need to be strenthened or replaced under limited funding in preparation for future potential earthquakes. In stage II (post-earthquake stage), pressure tests must be carried out according to the gas leakage situation reported by users or detected by devices. The Multi-Label K-Nearest-Neighbor (ML-KNN) algorithm is used to predict the corresponding failed pipelines and optimize the pipeline pressure test order. In stage III (repair stage), a strategy based on a greedy algorithm is applied to optimize the pipeline repair sequence. The proposed methods were applied to the GDN of a city in northern China. The following conclusions were drawn from the results: (1) The FPDC-GA enhanced the robustness and resourcefulness of the GDN system to the maximum level within the available funding budget. (2) The pipeline pressure test order calculated using the ML-KNN algorithm was significantly improved compared with a random pressure test order or one based on the empirical failure probability of pipelines. (3) After optimization using a greedy algorithm, the performance recovery curves under different earthquake conditions were shaped as an exponential function, which indicates that the performance of the GDNs recovered in the most efficient manner. The findings of this study could be useful as tools for the seismic resilience enhancement of GDNs in different stages. The proposed optimization algorithms can also be extended to the lifeline of other networks.

Vehicle Routing Optimization System with Smart Geopositioning Updates

Solving the vehicle routing problem (VRP) is one of the best-known optimization issues in the TLS (transport, logistic, spedition) branch market. Various variants of the VRP problem have been presented and discussed in the literature for many years. In most cases, batch versions of the problem are considered, wherein the complete data, including customers’ geographical distribution, is well known. In real-life situations, the data change dynamically, which influences the decisions made by optimization systems. The article focuses on the aspect of geopositioning updates and their impact on the effectiveness of optimization algorithms. Such updates affect the distance matrix, one of the critical datasets used to optimize the VRP problem. A demonstration version of the optimization system was developed, wherein updates are carried out in integration with both open source routing machine and GPS tracking services. In the case of a dynamically changing list of destinations, continuous and effective updates are required. Firstly, temporary values of the distance matrix based on the correction of the quasi-Euclidean distance were generated. Next, the impact of update progress on the proposed optimization algorithms was investigated. The simulation results were compared with the results obtained “manually” by experienced planners. It was found that the upload level of the distance matrix influences the optimization effectiveness in a non-deterministic way. It was concluded that updating data should start from the smallest values in the distance matrix.

Motion Planning for Vibration Reduction of a Railway Bridge Maintenance Robot with a Redundant Manipulator

Motivated by the potential applications of maintenance and inspection tasks for railway bridges, we have developed a biped climbing robot. The biped climbing robot can climb on the steel guardrail of the railway bridge with two electromagnetic feet and implement the maintenance and inspection tasks by a redundant manipulator with 7 degrees of freedom. To reduce the vibration of the manipulator caused by the low rigidity of the guardrail and the discontinuous trajectories of joints, a motion planning algorithm for vibration reduction is proposed in this paper. A geometric path accounting for obstacle avoidance and the manipulator’s center of gravity is determined by the gradient projection method with a singularity-robust inverse. Then, a piecewise quintic polynomial S shape curve with a smooth jerk (derivative of joint angular acceleration) profile is used to interpolate the sequence of joint angular position knots that are transformed from the via-points in the obstacle-avoidance path. The parameters of the quintic polynomial S-curve are determined by a nonlinear programming problem in which the objective function is to minimize the maximus of the torque exerted by the manipulator on the guardrail throughout the jerk-continuous trajectory. Finally, a series of simulation experiments are conducted to validate the effectiveness of the proposed algorithm. The simulation results show that the tracking errors of the trajectory with the proposed optimization algorithm are significantly smaller than the tracking errors of the trajectory without optimization. The absolute values of mean deviation of the tracking errors of the three coordinate axes decreased by at least 48.3% compared to the trajectory without vibration-reduction in the triangle working path and linear working path trajectory following simulations. The analysis results prove that the proposed algorithm can effectively reduce the vibration of the end effector of the manipulator.

PI control based on parameter space approach supported metaheuristic optimization algorithms and ANFIS in a natural gas combined cycle power plant

AbstractIn this article, proportional‐integral (PI) control to ensure stable operation of a steam turbine in a natural gas combined cycle power plant is investigated, since active power control is very important due to the constantly changing power flow differences between supply and demand in power systems. For this purpose, an approach combining stability and optimization in PI control of a steam turbine in a natural gas combined cycle power plant is proposed. First, the regions of the PI controller, which will stabilize this power plant system in closed loop, are obtained by parameter space approach method. In the next step of this article, it is aimed to find the best parameter values of the PI controller, which stabilizes the system in the parameter space, with artificial intelligence‐based control and metaheuristic optimization. Through parameter space approach, the proposed optimization algorithms limit the search space to a stable region. The controller parameters are examined with Particle Swarm Optimization based PI, artificial bee colony based PI, genetic algorithm based PI, gray wolf optimization based PI, equilibrium optimization based PI, atom search optimization based PI, coronavirus herd immunity optimization based PI, and adaptive neuro‐fuzzy inference system based PI (ANFIS‐PI) algorithms. The optimized PI controller parameters are applied to the system model, and the transient responses performances of the system output signals are compared. Comparison results of all these methods based on parameter space approach that guarantee stability for this power plant system are presented. According to the results, ANFIS‐ PI controller is better than other methods.

Optimal energy management system for microgrids considering energy storage, demand response and renewable power generation

To ensure the autonomous power supply in microgrids (MGs) in stand-alone mode while also maintaining stability, energy storage systems (ESSs) and demand-side flexibility can be utilized together. Motivated by this fact, in this study, a scenario-based energy management system (EMS) modelled as a mixed-integer linear programming (MILP) problem is presented by taking the stochastic nature of wind and photovoltaic (PV) sources into account in order to analyze the operational behaviour of MGs and thereby to reduce the network energy losses. Direct load control (DLC) based demand response (DR) program is implemented to the system with the objective of exploiting the remarkable potential of thermostatically controllable appliances (TCAs) for energy reduction while satisfying comfort and operational constraints. Furthermore, a common ESS with a bi-directional power flow facility is incorporated in the proposed structure and electric vehicles (EVs) are employed as an additional flexible load in grid-to-vehicle (G2V) mode. To testify the effectiveness of the proposed optimization algorithm, different case studies are conducted considering diverse scenarios. Moreover, the performance is compared with a deterministic method from the perspective of achieving loss reduction and capturing the uncertainties.

Frank-Wolfe Algorithm for Learning SVM-Type Multi-Category Classifiers

Multi-category support vector machine (MC-SVM) is one of the most popular machine learning algorithms. There are lots of variants of MC-SVM, although different optimization algorithms were developed for different learning machines. In this study, we developed a new optimization algorithm that can be applied to many of MC-SVM variants. The algorithm is based on the Frank-Wolfe framework that requires two subproblems, direction finding and line search, in each iteration. The contribution of this study is the discovery that both subproblems have a closed form solution if the Frank-Wolfe framework is applied to the dual problem. Additionally, the closed form solutions on both for the direction finding and for the line search exist even for the Moreau envelopes of the loss functions. We use several large datasets to demonstrate that the proposed optimization algorithm converges rapidly and thereby improves the pattern recognition performance.

Computation Offloading and Wireless Resource Management for Healthcare Monitoring in Fog-Computing-Based Internet of Medical Things

During the COVID-19 pandemic, Internet of Medical Things (IoMT) has been playing an important role in controlling the development of the epidemic, including enabling doctors in different grade hospitals to make a diagnosis and treatment, isolating and care for confirmed and suspected cases promptly, and preventing infection of patients with the novel coronavirus. In this article, we investigate the minimization optimization problem for healthcare monitoring in fog computing-based IoMT (FogC-IoMT), which is nonlinear and nonconvex problem, by considering Quality-of-Service requirement, power limit, and wireless fronthaul constraint. In order to solve the problem effectively, three independent subproblems are decoupled, and the suboptimal low-complexity computation offloading and resource management scheme is proposed in FogC-IoMT. The simulation results reveal the effectiveness of the proposed optimization algorithm in terms of cost utility.

Discrete sizing design of steel truss bridges through teaching-learning-based and biogeography-based optimization algorithms involving dynamic constraints

In this paper, Teaching-Learning Based Optimization (TLBO) and Biogeography-Based Optimization (BBO) algorithms are presented to examine the optimum discrete sizing design of steel truss steel bridges for minimizing the structural weights. Both proposed nature-inspired metaheuristic optimization algorithms are encoded in MATLAB with integration of a structural analysis program (SAP2000) via open application programming interface (OAPI). At the end, optimal steel profiles are selected from available discrete section lists by satisfying the structural restrictions, such as stress and displacement, involved by American Institute of Steel Construction-Allowable Stress Design (AISC-ASD). Additional to these, optimum discrete sizing design process is performed for the cases with and without dynamic constraints, which are adopted from natural periods of the bridge structures with respect to the mode shapes. The algorithmic performance of the proposed algorithms outperforms on both planar and spatial steel truss bridges. To prove this obtained optimal solutions are compared with previously reported optimum designs attaining via different metaheuristics. The final optimum discrete sizing designs of the steel truss bridges reveal that the proposed TLBO and BBO algorithms can easily be applied to discrete nonlinear programming problems.

Intelligent Reflecting Surface Enhanced Secure Transmission Against Both Jamming and Eavesdropping Attacks

Both the jammer and the eavesdropper pose severe threat to wireless communications due to the broadcast nature of wireless channels. In this paper, an intelligent reflecting surface (IRS) assisted secure communication system is considered, where a base station (BS) wishes to reliably convey information to a user, in the presence of both a jammer and an eavesdropper whose transmit informations are not completely known. Specifically, with the imperfect third-party node's channel state information (CSI) and no knowledge of the jammer's transmit beamforming, we aim to maximize the system achievable rate by jointly designing the BS's transmit beamforming and the IRS's reflect beamforming, while limiting the information leakage to the potential eavesdropper. Due to the non-convexity and intractability of the original problem induced by the incompleted information, we utilize the auxiliary variables, Cauchy-Schwarz inequality, and General Sign-Definiteness transformation to convert the original optimization problem into a tractable convex optimization problem, and then obtain the high-quality optimal solution by using the successive convex approximation and penalty convex concave procedure. Numerical simulations demonstrate the superiority of our proposed optimization algorithm compared with existing approaches, and also reveal the impact of key parameters on the achievable system performance.

Optimization of Pipe Network Simulation Algorithm for Tree-Shaped Water Injection System in Large-Scale Oilfield

As intelligence technology develops, there is a higher requirement for computing speed and accuracy of water injection system simulation. In this paper, aiming at the tree-shaped water injection pipe network system of large-scale oilfields, based on the energy equation for calculating the pressure drop [Formula: see text] of pipe section, a mathematical model of the pipeline unit and the node unit is established, and finally, a mathematical model of pipe network for the entire water injection system is established; then, the improved iterative algorithm is used to solve the simulation model of water injection system. In this way, we determine the boundary calculation conditions, take the water injection station as reference node, and use the maximum pressure of water injection well as the initial value of the reference node for calculation, which reduces the number of iterations in model calculation; by comparing the simulation results of different iteration steps, 0.01 is selected as the iteration step size due to its higher calculation accuracy; and the calculation process has also been optimized. The process of solving the characteristic matrix [Formula: see text] is combined with the process of calculating the pressure drop [Formula: see text] of pipe section, and placed outside the algorithm loop, thereby shortening the calculation time of a single cycle and reducing the calculation amount of the algorithm. The application cases show that the proposed optimization algorithm for water injection system pipe network simulation can be used as an effective method to improve the solution speed and calculation accuracy of the simulation algorithm of tree-shaped water injection system in large-scale oilfields.

A novel data-driven air balancing method with energy-saving constraint strategy to minimize the energy consumption of ventilation system

Air balancing is a key technology to reduce energy consumption of ventilation system and improve the quality of indoor living environment. So far, most of the existing data-driven non-iterative air balancing methods only focus on the prediction of terminal damper angle to supply appropriate airflow, but they do not pay attention to the energy-saving constraint of fan voltage and terminal damper. Therefore, their energy efficiencies are not high enough. In this paper, energy-saving constraint strategy of low fan voltage and small damper friction resistance is considered and a novel data-driven non-iterative air balancing model with energy-saving constraint strategy is proposed. The model parameters can be trained by the proposed optimization algorithm inputting acquisition data. Then, given a design airflow rate, the required fan voltage and terminal damper angle can be predicted by the trained model to achieve accurate air balancing control with high energy efficiency. The performance validation of the proposed method is executed on our experimental duct system with five terminals. Compared with the current air balancing method, the proposed method can improve energy saving potential up to 13.7%, while keeping accurate air balancing within 10% relative error standard.

An Improved Heap-Based Optimizer for Optimal Design of a Hybrid Microgrid Considering Reliability and Availability Constraints

The hybrid microgrid system is considered one of the best solution methods for many problems, such as the electricity problem in regions without electricity, to minimize pollution and the depletion of fossil sources. This study aims to propose and implement a new algorithm called improved heap-based optimizer (IHBO). The objective of minimizing the microgrid system cost is to reduce the net present cost while respecting the reliability, power availability, and renewable fraction factors of the microgrid system. The results show that the PV/diesel/battery hybrid renewable energy system (HRES) gives the best solution, with a net present cost of MAD 120463, equivalent to the energy cost of MAD 0.1384/kWh. The reliability is about 3.89%, the renewable fraction is about 95%, and the power availability is near to 99%. The optimal size considered is represented as 167.3864 m2 of PV area, which is equivalent to 44.2582 kW and 3.8860 kW of diesel capacity. The study results show that the proposed optimization algorithm of IHBO is better than the artificial electric field algorithm, the grey wolf optimizer, Harris hawks optimization, and the original HBO algorithm. A comparison of the net present cost with a different fuel price is carried out, in which it is observed that the net present cost is reduced even though its quantity used is mediocre.

Distributed Economic Optimal Scheduling Scheme for Ship-Integrated Energy System Based on Load Prediction Algorithm

In order to enhance navigation safety and promote environmental protection, this paper takes the problem of energy management in a ship-integrated energy system into consideration. According to the characteristics of navigation, an intelligent ship energy management model, simultaneously considering the social and economic benefits, has been proposed. Meanwhile, this paper analyzes a distributed optimal scheduling problem which considers renewable generation devices and an energy storage system. Combined with an electricity-power system and thermal-power system, we propose an optimal scheduling scheme to accurately meet the actual load demand based on the pre-results analyzed by the ensemble learning short-term load forecasting algorithm. In addition, the related stability analysis is given. Further, a series of simulation results have been presented, which denote that the proposed load forecasting algorithm can accurately analyze the short-term load demand trend, and the proposed optimization algorithm can effectively coordinate economic and environmental protection.

Rescheduling‐based congestion management by metaheuristic algorithm: Hybridizing lion and moth search models

AbstractCongestion management (CM) is becoming a challenging task in the power system due to the increase in the system complexities in the deregulated environment. The congestion causes grave damage to the power system components if the outages tend to occur more frequently. Hence, in the present power system scenario, CM is a complex task of an independent system operator (ISO). This proposal intends to introduce a novel optimization‐based CM model with advanced soft computing techniques. A new hybrid algorithm is introduced in this paper, which optimizes the generating power of added generators with the bus system. Thereby manages the congestion with minimized rescheduling cost. The proposed optimization algorithm termed Lion Algorithm with Moth‐based Mutation (LA‐MM) hybridizes the concepts of both Lion Algorithm (LA) and Moth Search Algorithm (MS). Subsequently, the experimentation is conducted in two test bus systems like IEEE 14 and 30 bus systems. The performance of the proposed model is compared over other state‐of‐the‐art models concerning cost analysis and convergence analysis, respectively.

Shape optimization of concrete free-form shells considering material damage

Shape optimization is a pervasive tool for designing concrete free-form shells. However, the existing shape optimization approaches for free-form shells usually assume the material is purely elastic without considering material damage, which may produce undesired results in real-world structural applications, especially in the case of concrete structures. In this article, a framework is presented for performing the shape optimization of concrete free-form shells while considering concrete damage. In the proposed optimization algorithm, the Mazars model is coupled with finite element analysis for modelling structural responses subjected to material damage. Through a series of numerical examples, the proposed algorithm is validated and the effects of material damage on optimal results are investigated. It is demonstrated that the consideration of material damage leads to a more robust design, which highlights the importance of accounting for the material damage during the shape optimization process of concrete free-form shells.

Optimization of electric spring operational strategy to minimize electricity bill

This paper aims at using electric spring to reduce the monthly electricity bill by developing its optimal operational strategy to capitalize on the variable energy cost over a day. An hourly-sampled optimization algorithm is presented to devise an optimum daily strategy for electric spring as a tool to smartly manage energy among the loads, utility, and storage devices, provided that load and solar power forecasts are available. Uncertainty in forecasts is dealt with by embedding certain stochastic processes with the proposed optimization algorithm to identify an envelope of probable operational strategies. The development and deployment of the proposed optimization algorithm are presented on a realistic load profile of a typical net-zero energy house in South Australia. The obtained results are analyzed under different connection configurations. Consequently, a significant reduction in the monthly electricity bill is realized.