Heuristic Optimization Approach Research Articles

Data-driven and physics-based methods to optimize structures against delamination

Composite materials and multi-material components often fail at their internal interfaces/adhesive joints, and hence special attention should be given to such catastrophic delamination events to guarantee the system’s functional requirements. So far, however, the majority of structural topology optimization problems have focused on optimal distribution of the bulk materials by considering interfaces as perfectly bonded. This motivates the introduction of optimization methods that explicitly take into account the role of the material interfaces to optimize structures against delamination. In this work, we propose a data-driven heuristic optimization approach for the identification of optimal cohesive interfaces with linearly graded fracture properties to increase the ability of the composite structure to withstand peeling. Moreover, for given cohesive interface properties, we investigate the applicability of the physics-based Solid Isotropic Material with Penalty (SIMP) topology optimization approach to optimize the internal structure of a substrate in problems where the stress field is affected by interface delamination.

A Heuristic Robust Design Optimization Approach for Batch Production Permanent Magnet Actuator Consider Various Uncertainties and Life-Cycle Quality Loss

A Heuristic Robust Design Optimization Approach for Batch Production Permanent Magnet Actuator Consider Various Uncertainties and Life-Cycle Quality Loss

Maximum span determination and optimal sizing of cable for improved performance of droop-controlled DC microgrid

DC microgrids are seen as smart solutions to interface DC loads and distributed energy resources (DERs). However, in DC microgrids, the placement of sources and loads as well as the size of cable impact the system’s span, regulation of voltage, and losses. This paper proposes novel algorithms to determine the maximum span and optimal size of cable for the improved performance of a droop-controlled DC microgrid. The proposed algorithms utilize an improved power flow analysis (IPFA) method based on Newton-Raphson technique to determine the maximum span and the optimal size of cable, enhancing the voltage regulation and reducing the cost. Additionally, the impacts of power rating, droop constants, and size of cable on the DC microgrid’s maximum span are investigated and reported. Owing to the intricate nature of the problem concerning the ideal size of cable for the droop-controlled DC microgrid, a heuristic optimization approach is employed. Further to enhance the rate of convergence and computational performance, an improved particle swarm optimization (IPSO) is also proposed. The constraint of keeping the bus voltage variations below the allowable voltage regulation limits is applied to the objective function of the optimization problem. In the case of a droop-controlled, DC microgrid having a specified configuration, the suggested algorithm can determine the ideal size of cable, guaranteeing both the least cost and enhanced voltage regulation. Comprehensive numerical and modelling results have been presented for a droop-controlled DC microgrid with different loads and DERs to verify the effectiveness of the proposed methods. Furthermore, the analysis reveals that configuration III of the DC microgrid with an optimal cable size of 19 mm2 lowers the absolute voltage regulation to as low as 1.06 V. The results of the detailed analysis validate the enhanced performance of the proposed algorithms and are highly useful to both system designers and consumers of the DC microgrids, eventually paving the way for the widespread use of DC microgrids in the future.

An adaptive heuristic approach for Volt-VAr optimization in distributed generation integrated system

Volt-VAr optimization (VVO) is an important tool in improving the efficiency of distribution system by simultaneously coordinating voltage and reactive power both. Today, loads are largely dependent on voltage and thus, this voltage-dependence affects both real and reactive power. So, minimizing apparent power is more important than minimizing the energy. VVO enables distribution system utilities to run their network on comparatively lower voltage which ultimately increases energy savings. Devices such as, on load tap changers (OLTC), and voltage regulators (VRs) are commonly adopted techniques to regulate/boost the voltage. Capacitive banks (CBs) are also used to inject reactive power (VAr) into the system keeping the operational constraints and system constraints specified and satisfied. This paper presents a new methodology of Volt-VAr optimization using Genetic algorithm considering a variety of constraints. The test is performed on IEEE-69 bus system and results validate the efficacy of the proposed algorithm.

Multi-day tourism recommendations for urban tourists considering hotel selection: A heuristic optimization approach

With the development of tourism, digital technology is increasingly being applied in the design of tourist routes. This study takes into account that tourists are experience-driven in tourism activities and hotel selections. In this study, the tourist trip design problem with hotel selection is formulated based on bi-objective optimization with total utility of the points of interest maximization and the average utility of the hotels maximization, and a three-step hybrid algorithm combined with discrete particle swarm optimization, an adaptive differential evolution with an optional external archive, and a local search is designed to identify the optimal route. To examine the performance of the designed algorithm, a numerical experiment was conducted. The results of Wilcoxon rank sum tests verified that the proposed algorithm performed distinctly better than extant approaches. Moreover, the results also indicate that the two main innovative mechanisms about initialization and hybrid evolution play a critical role in improving the algorithm's efficiency for the tourist trip design problem with hotel selection.

Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique’s efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly.

A Heuristic Optimization Approach for Synthesizing the Relationship between Shippers and Recipients in Urban Freight Transport

This paper presents a heuristic optimization approach in order to model relationships between shippers and recipients in urban freight transport. It proposes an algorithm with which these relationships can be synthesized in the absence of survey data. In an urban environment, freight customers are assigned to distribution centres in a way that replicates the observed distance distribution between shippers and recipients. The result is a synthetic freight demand population that can serve as a starting point for analyzing behavior-based urban freight and freight policy effectiveness. It presents an application for a real-world case study of retail food in Berlin.

A Survey on Model-Based, Heuristic, and Machine Learning Optimization Approaches in RIS-Aided Wireless Networks

A Survey on Model-Based, Heuristic, and Machine Learning Optimization Approaches in RIS-Aided Wireless Networks

FRACTIONAL ORDER MODEL FOR CARDIOVASCULAR SYSTEM USING HEURISTIC OPTIMIZATION APPROACH

Cardiovascular diseases like atherosclerosis, peripheral arterial disease, coronary heart disease, etc. are dangerous and hence early detection of such diseases is important in the field of medical sciences. The existing methods used for diagnosing diseases are time-consuming and highly expensive. On the other hand, experimentation with Cardiovascular System (CVS) for the analysis and decision making for treatment is unsafe and hence it is important to develop an accurate CVS model. It is observed from the existing integer order cardiovascular models that the viscoelastic property of four chambers is not taken into consideration. To accommodate these properties, this paper proposes a fractional order model by introducing fractionality to the dynamical equation of CVS. Further, an optimization method is presented to obtain the fractionality of different chambers by minimizing the integral square error between clinical data of healthy human and model output using Cuckoo search, Firefly and Accelerated particle swarm algorithms. The results indicate that fractional models are better than integer order CVS models and specifically, the Firefly algorithm provides a better fractional model than the models obtained from other algorithms. Further, the best model obtained using firefly algorithm is considered for simulating the diseases (i) increased arterial stiffness and (ii) atherosclerosis.

Optimisation of pipes with constant diameter using the heuristic optimality criterion

Background: Minimising internal pressure drop in pipes is crucial for energy efficiency of fluid flow applications. Numerous computational optimisation tools that are capable of modifying flow geometries to reduce the pressure drop have been developed. Among these is a comparably simple heuristic optimisation algorithm which mimics erosion and sedimentation processes based on the shear stress in the vicinity of the domain boundaries. Although this method succeeds in modifying flow geometries for reduced pressure drop, it allows the fluid domain to widen during the reshaping process. Therefore, a reported reduction of pressure drop is not only caused by an improvement of the flow path, but also by an increase in the domain width. However, pipes with a constant circular diameter are favoured in many applications because they can be easily manufactured. Methods: Here we combine the heuristic optimisation approach with a new geometrical constraint that kept the average diameter constant. This way, a reduction of pressure drop caused solely by the modification of the flow path can be assessed. We determined the applicability of the modified algorithm for 2D channel and 3D pipe geometries, conducting numerical simulations using the Lattice Boltzmann method with Reynolds numbers ranging from 40 to 500. Results: For all investigated cases, potentially optimal shapes could be derived at all tested Reynolds numbers. However, the shape originally derived at a simulated flow with Re = 40 yielded a smaller pressure drop even for flows at Re ≥ 100 than shapes derived specifically at these higher Reynolds numbers. Conclusions: This observed trend should be kept in mind when employing this approach as a simple way to improve channel and pipe layouts.

Analysis of Heuristic Optimization Technique Solutions for Combined Heat-Power Economic Load Dispatch

Thermal power plants use coal as a fuel to create electricity while wasting a significant amount of energy as heat. If the heat and power plants are combined and used in cogeneration systems, it is possible to reuse the waste heat and hence enhance the overall efficiency of the power plant. In order to minimize production costs while taking system constraints into account, it is important to find out the optimal operating point of power and heat for each unit. Combined heat and power production is now widely used to improve thermal efficiency, lower environmental emissions, and reduce power generation costs. In order to determine the best solutions to the combined heat and power economic dispatch problem, several traditional as well as innovative heuristic optimization approaches were employed. This study offers a thorough analysis of the use of heuristic optimization techniques for the solution of the combined heat and power economic dispatch problem. In this proposed work, the most well-known heuristic optimization methods are examined and used for the solution of various generating unit systems, such as 4, 7, 11, 24, 48, 84, and 96, taking into account various constraints. This study analyzes how various evolutionary approaches are performed for various test systems. The heuristic methodologies’ best outcomes for various case studies with restrictions are contrasted.

A surrogate-assisted heuristic approach for the joint optimization of resource allocation and scheduling of an aircraft final assembly line

In an aircraft final assembly line, there exists flexibility in resource allocation in the sense that processing time of a task can be reduced by allocating more workers to the task. As task processing times change, assembly task scheduling needs to be adjusted accordingly, which in turn has an impact on resource allocation. This paper studies the joint optimization problem of resource allocation and task scheduling. Both resource requirement and starting time of each task need to be determined such that the total resource investment cost is minimized. We propose a surrogate-assisted heuristic approach to solve the problem by decomposing it into two phases. In the first phase, a multi-start iterative search algorithm is developed to perform a dedicated exploration on resource allocation. A surrogate model trained on numerical data is applied to evaluate the resource allocation strategy. In the second phase, a hybrid genetic algorithm embedded with a novel local search procedure is designed for task scheduling. Meanwhile, a fine search for resource allocation can also be achieved. Computational experiments demonstrate the superiority of our proposed approach and that the surrogate plays a significant role in terms of improving solution quality. A realistic case study provides valuable managerial insights for real production.

Light can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Spot Light

With the development of machine learning models throughout industries, there has been a corresponding increase in research demonstrating its vulnerability to adversarial examples (AE). Realizing physically robust AE that survive real-world environmental conditions faces the challenges such as varied viewing distances or angles. Laser beam-based methods claim to overcome the obviousness, semi-permanence, and unchangeable drawbacks of adversarial patches. However, laser beam-based AE could not be captured by the camera under daylight, which makes the application scenarios limited. In this research, we introduce Adversarial Spot Light (AdvSL), a novel approach that enables adversaries to build physically robust real-world AE by utilizing spotlight flashlights. Since the spotlight flashlights may be switched on and off as required, AdvSL allows adversaries to perform more flexible attacks than adversarial patches. Especially, AdvSL is feasible in a variety of ambient light conditions. As a first step, we modeled a spot light with a set of parameters that can be physically controlled by the adversary. To determine the optimal parameters for the light, a heuristic optimization approach is adopted. Further, we use the k-random-restart technique to prevent the approach from being stuck in a local optimum. To demonstrate the effectiveness of the proposed approach, we conduct experiments under different physical conditions, including indoor and outdoor tests. In the digital test, AdvSL causes misclassifications on state-of-the-art neural networks with up to 93.7% attack success rate. In the outdoor test, AdvSL causes misclassifications on the traffic sign classification model with up to 84% attack success rate. In the physical setting, experiments show that the AdvSL is robust in non-bright settings and is feasible in bright settings. Finally, we discuss the defense of AdvSL and evaluate an adaptive defender using adversarial learning, which is able to reduce the attack success rate from 92.2% to 54.8% in the digital.

A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction

Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system’s effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.

A multi-objective Chaos Game Optimization algorithm based on decomposition and random learning mechanisms for numerical optimization

Chaos Game Optimization (CGO) is a heuristic optimization approach that estimates global optima for optimization problems using operators based on chaos theory. This paper first proposes a multi-objective variant of this recent algorithm using decomposition. The proposed algorithm is called Multi-Objective CGO based on Decomposition (MOCGO/D), in which the decomposition step employs a Normalized Boundary Intersection (NBI) technique for decomposing the multi-objective problem into single-objective sub-problems. A novel Random Learning (RL) strategy based on the combination of multiple strategies such as Opposition-based learning, Levy flight operator, Orthogonal learning, and the tangent flight operator, is incorporated in the MOCGO/D to propose an enhanced version called MOCGO/DR algorithm. The RL strategy aims to improve the balance between exploitation and exploration of the conventional CGO, leading to better convergence behavior and avoiding getting trapped in local optima. The first set of experimental results demonstrates that MOCGO/DR can perform better than three other variants of MOCGO, namely archive-based MOCGO (MOCGO/A), crowding-distance based MOCGO (MOCGO/CD) and decomposition-based MOCGO (MOCGO/D) on 62% of test cases. A second set of experiments and evaluation shows that the proposed approach provides better results than well-regarded algorithms, including strength pareto evolutionary algorithm (SPEA2), multiobjective evolutionary algorithm based on decomposition (MOEA/D), multi-objective particle swarm optimization algorithm based on decomposition (MPSO/D), multistage evolutionary algorithm (MSEA), and a fast and elitist multi- objective genetic algorithm (NSGAII) when using performance measures such as GD, IGD, HV, Spacing, Spread, and Hausdorff distance on 65% test cases. This two-stage evaluation was conducted on three different benchmark test sets: the Deb–Thiele–Laumanns–Zitzler (DTLZ) test suite, the Zitzler–Deb–Thiele (ZDT) test suite, and the bias test suite (BT). Overall, the Friedman test results for all performance measures show that MOCGO/DR is demonstrated to be a competitive candidate as a multi-objective optimization algorithm in this space.

A heuristic optimization approach for the scheduling home appliances

In order to develop and execute a demand response (DR) system for a household energy management system, an effective and adaptable energy management architecture is provided in this study. Several issues related to the current home energy management system (HEMS) are among those that do not give their consumers a choice to assure user comfort (UC) or a long-term answer to lowered carbon emissions. Our research suggests a programmable heuristic-based energy management controller (HPEMC) to manage a residential building in order to minimize power costs, reduce carbon emissions, increase UC, and lower the peak-to-average ratio (PAR). In this study, the demand-responsive appliance scheduling problem is solved using an energy management system to reduce the cost and a PAR. Numerous case studies have been used to demonstrate the viability of the suggested method. The simulation results confirmed the effectiveness of the proposed method and that it is capable of running a hybrid microgrid in various modes. The findings indicate that the proposed schedule controller saved 25.98% of energy.

A novel meta-heuristic approach for optimal RPP using series compensated FACTS controller

One of the most important aspects of power system planning is reactive power (VAR- Volt Ampere- Reactive) optimization and voltage control, which has an influence on both economics and stability of the transmission systems. It is perhaps one of the most difficult optimization problem to solve since it is nonlinear and includes both consistent and sporadic choice parameters. The goal is to primarily calculate the cost components, such as operating cost due to energy loss, series compensation (Thyristor-Controlled series Capacitor-TCSC) device operating costs, and actual power loss cost. In the proposed work, to enhance system voltage profile, TCSC is deployed at weak points across several echelon after computing mathematical models for standard benchmark functions. The paper goes into great depth with application of Oppositional based learning on Harris Hawks Optimizer (OHHO). This meta heuristic optimization approach, has been used to solve the VAR optimization issue. Finally, the benchmark functions outputs are thoroughly examined for two test systems like Ward Hale 6 bus system and modified IEEE- 30 bus test system to demonstrate the validity of the proposed hybrid intelligent approach for series compensated FACTS controller.

Integrate computation intelligence with Bayes theorem into complex construction installation: a heuristic two-stage resource scheduling optimisation approach

The cost control challenge in construction and installation projects has always been a critical concern for construction entities. The complexity of task collaboration among various equipment and nodes during the installation process leads to extended construction duration, resulting in increased construction costs. To address this issue, this paper proposes a heuristic two-stage optimal deployment approach called MERD. The MERD approach incorporates intelligent computing principles from computer science into the resource scheduling of the construction process, modelling the installation scheduling problem into a combinatorial optimisation problem. Designing the probability method based on Bayes theorem, the MERD approach carries out an installation provisioning mechanism to optimise personnel and device allocation in the selected area. As a result, the MERD approach minimises construction hours and reduces labour costs in the construction process. Experimental results demonstrate the effectiveness and efficiency of the MERD approach in reducing work time and cost in engineering projects.

Deep learning method based on autoencoder neural network applied to faults detection and diagnosis of photovoltaic system

The paper presents an application of deep learning for fault detection in PV systems located in Algiers –Algeria which has a nominal power of 9.54 kW. Each PV array comprises 30 PV modules each string contains 15 PV modules in series. Fault detection and diagnosis of Photovoltaic Systems (PVS) is an important task for successful solar power generation. Several faults have been occurred such as short-circuit cases and open-circuit string cases in PV generator can leads to fire and risk. The design of efficient defect detection methods for PV systems presents a challenge especially for small scale PV farms. Faults detection methods are failed under the presence of noisy signals. This paper presents a deep learning (DL) based method for detection, diagnosis and classification of the aforementioned defects. The proposed procedure consists of four fundamental steps, first a heuristic optimization approach based on Coyote Optimization Algorithm (COA) the five-unknown electrical parameters of the One Diode Model (ODM) and their insertion into a PSIM-based simulation that aims to mimic the operating PV system. Second, database construction that includes current, voltage and power at the Maximum Power Point (MPP), module temperature and solar irradiance for the PV system is supposed under healthy and faulty operations condition at optimal operational conditions. Then, this is followed by the extraction of new features of the old database using unsupervised learning characteristics of the Auto-Encoder (AE). and last, supervised learning using the new database based on Artificial Neural Network (ANN) construction for PV fault detection and classification. The proposed technique has been validated using monitored data from a real operating PV system situated in Algeria. The obtained results have shown the effectiveness of the proposed technique detection and classification of various PV fault types.

Exploiting Stacked Autoencoders for Improved Sentiment Analysis

Sentiment analysis is an ongoing research field within the discipline of data mining. The majority of academics employ deep learning models for sentiment analysis due to their ability to self-learn and process vast amounts of data. However, the performance of deep learning models depends on the values of the hyperparameters. Determining suitable values for hyperparameters is a cumbersome task. The goal of this study is to increase the accuracy of stacked autoencoders for sentiment analysis using a heuristic optimization approach. In this study, we propose a hybrid model GA(SAE)-SVM using a genetic algorithm (GA), stacked autoencoder (SAE), and support vector machine (SVM) for fine-grained sentiment analysis. Features are extracted using continuous bag-of-words (CBOW), and then input into the SAE. In the proposed GA(SAE)-SVM, the hyperparameters of the SAE algorithm are optimized using GA. The features extracted by SAE are input into the SVM for final classification. A comparison is performed with a random search and grid search for parameter optimization. GA optimization is faster than grid search, and selects more optimal values than random search, resulting in improved accuracy. We evaluate the performance of the proposed model on eight benchmark datasets. The proposed model outperformed when compared to the baseline and state-of-the-art techniques.