Evolutionary Algorithms for Parameter Optimization-Thirty Years Later

Thirty years, 1993-2023, is a huge time frame in science. We address some major developments in the field of evolutionary algorithms, with applications in parameter optimization, over these 30 years. These include the covariance matrix adaptation evolution strategy and some fast-growing fields such as multimodal optimization, surrogate-assisted optimization, multiobjective optimization, and automated algorithm design. Moreover, we also discuss particle swarm optimization and differential evolution, which did not exist 30 years ago, either. One of the key arguments made in the paper is that we need fewer algorithms, not more, which, however, is the current trend through continuously claiming paradigms from nature that are suggested to be useful as new optimization algorithms. Moreover, we argue that we need proper benchmarking procedures to sort out whether a newly proposed algorithm is useful or not. We also briefly discuss automated algorithm design approaches, including configurable algorithm design frameworks, as the proposed next step toward designing optimization algorithms automatically, rather than by hand.

Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning

This study focuses on predicting the dynamic viscosity of nanofluids, specifically Polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) using machine learning models. The primary goal of this research is to assess and contrast the effectiveness of three distinct machine learning models: Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main objective is the identification of a model that demonstrates the highest level of accuracy in predicting a nanofluid's viscosity namely, PAO-hBN nanofluids. The models were trained and validated using 540 experimental data points, where the mean square error (MSE) and the coefficient of determination R² were utilized for performance evaluation. The results demonstrated that all three models could predict the viscosity of PAO-hBN nanofluids accurately, but the ANFIS and ANN models outperformed the SVR model. The ANFIS and ANN models had similar performance, but the ANN model was preferred due to its faster training and computation time. The optimized ANN model had an R² of 0.99994, which indicates a high level of accuracy in predicting the viscosity of PAO-hBN nanofluids. The elimination of the shear rate parameter from the input layer improved the accuracy of the ANN model to an absolute relative error of less than 1.89% over the full temperature range (-19.7 °C-70 °C) compared to 11% in the traditional correlation-based model. These results suggest that the use of machine learning models can significantly improve the accuracy of predicting the viscosity of PAO-hBN nanofluids. Overall, this study demonstrated that the use of machine learning models, specifically ANN, can be effective in predicting PAO-hBN nanofluids' dynamic viscosity. The findings provide a new perspective on how to predict the thermodynamic properties of nanofluids with high accuracy, which could have important applications in various industries.

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called 'novel' if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on 'novel ideas', so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

A survey on the utilization of Superpixel image for clustering based image segmentation

Superpixel become increasingly popular in image segmentation field as it greatly helps image segmentation techniques to segment the region of interest accurately in noisy environment and also reduces the computation effort to a great extent. However, selection of proper superpixel generation techniques and superpixel image segmentation techniques play a very crucial role in the domain of different kinds of image segmentation. Clustering is a well-accepted image segmentation technique and proved their effective performance over various image segmentation field. Therefore, this study presents an up-to-date survey on the employment of superpixel image in combined with clustering techniques for the various image segmentation. The contribution of the survey has four parts namely (i) overview of superpixel image generation techniques, (ii) clustering techniques especially efficient partitional clustering techniques, their issues and overcoming strategies, (iii) Review of superpixel combined with clustering strategies exist in literature for various image segmentation, (iv) lastly, the comparative study among superpixel combined with partitional clustering techniques has been performed over oral pathology and leaf images to find out the efficacy of the combination of superpixel and partitional clustering approaches. Our evaluations and observation provide in-depth understanding of several superpixel generation strategies and how they apply to the partitional clustering method.

Influence of Stacking Sequence on Mechanical Properties of Basalt/Ramie Biodegradable Hybrid Polymer Composites

In this study, the mechanical properties of basalt/ramie/polyester hybrid composite laminates were investigated. A matrix of 45% polyester was used, as it has good bonding properties between fibers. The composite laminates were fabricated using a hand layup technique, with seven layers stacked in different sequences and impregnated in the polyester matrix to create a hybrid configuration. Tensile, flexural, impact, compression, and hardness tests were conducted according to ASTM standards for mechanical characterization. The results showed that the overall stacking sequence of sample number seven (BRBRBRB) had the highest tensile strength at 120 MPa, impact energy at 8 J, flexural strength at 115 MPa, compression strength at 70 MPa, and hardness of 77. Natural fiber-reinforced composites are being used in current automotive industry applications, such as in electric vehicles.

Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning

Software Fault Prediction (SFP) is an important process to detect the faulty components of the software to detect faulty classes or faulty modules early in the software development life cycle. In this paper, a machine learning framework is proposed for SFP. Initially, pre-processing and re-sampling techniques are applied to make the SFP datasets ready to be used by ML techniques. Thereafter seven classifiers are compared, namely K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The RF classifier outperforms all other classifiers in terms of eliminating irrelevant/redundant features. The performance of RF is improved further using a dimensionality reduction method called binary whale optimization algorithm (BWOA) to eliminate the irrelevant/redundant features. Finally, the performance of BWOA is enhanced by hybridizing the exploration strategies of the grey wolf optimizer (GWO) and harris hawks optimization (HHO) algorithms. The proposed method is called SBEWOA. The SFP datasets utilized are selected from the PROMISE repository using sixteen datasets for software projects with different sizes and complexity. The comparative evaluation against nine well-established feature selection methods proves that the proposed SBEWOA is able to significantly produce competitively superior results for several instances of the evaluated dataset. The algorithms' performance is compared in terms of accuracy, the number of features, and fitness function. This is also proved by the 2-tailed P-values of the Wilcoxon signed ranks statistical test used. In conclusion, the proposed method is an efficient alternative ML method for SFP that can be used for similar problems in the software engineering domain.

Evolutionary Image Registration: A Review

Image registration is one of the most important image processing tools enabling recognition, classification, detection and other analysis tasks. Registration methods are used to solve a large variety of real-world problems, including remote sensing, computer vision, geophysics, medical image analysis, surveillance, and so on. In the last few years, nature-inspired algorithms and metaheuristics have been successfully used to address the image registration problem, becoming a solid alternative for direct optimization methods. The aim of this paper is to investigate and summarize a series of state-of-the-art works reporting evolutionary-based registration methods. The papers were selected using the PRISMA 2020 method. The reported algorithms are reviewed and compared in terms of evolutionary components, fitness function, image similarity measures and algorithm accuracy indexes used in the alignment process.

Collaborative Bullwhip Effect-Oriented Bi-Objective Optimization for Inference-Based Weighted Moving Average Forecasting in Decentralized Supply Chain

Downstream demand inference (DDI) emerged in the supply chain theory, allowing an upstream actor to infer the demand occurring at his formal downstream actor without need of information sharing. Literature showed that simultaneously minimizing the average inventory level and the bullwhip effect isn't possible. In this paper, the authors show that demand inference is not only possible between direct supply chain links, but also at any downstream level. The authors propose a bi-objective approach to reduce both performance indicators by adopting the genetic algorithm. Simulation results show that bullwhip effect can be reduced highly if specific configurations are selected from the Pareto frontier. Numerical results show that demand's time-series structure, lead-times, holding and shortage costs, don't affect the behaviour of the bullwhip effect indicator. Moreover, the sensitivity analysis show that the optimization approach is robust when faced to varied initializations. Finally, the authors conclude the paper with managerial implications in multi-level supply chains.

Developing of neuro-swarm system to estimate the undrained shear strength of soil by CPT data

In this study, a novel hybrid metaheuristic model was developed to forecast the undrained soil shear (USS) property from cone penetration test (CPT) data (data from bore log sample from 70 different sites in Louisiana). This algorithm produced with the integration of grey wolf optimization (GWO) and multilayer perceptron neural network (MLP), named GWO - MLP, where different numbers of hidden layers were tested (1 to 4). The duty of optimization algorithm was to determine the optimal number of neurons in each hidden layer. To this objective, the system comprised five inputs entitled sleeve friction, cone tip persistence, liquid limit, plastic limitation, too much weight, and USS as outcome. The developed models for forecasting the USS of soil show the proposed best models have R2 at 0.9134 and 0.9236 in the training and predicting stage. Although the total ranking score of GWO-MLP2 and GWO-MLP4 is equal, the OBJ value shows that GWO-MLP4 has better performance than GWO-MLP2. In this case, considering the time of model running and a greater number of hidden layers suggests that GWO-MLP2 could be most appropriate. Therefore, the GWO-MLP3 model outperforms other GWO-MLP networks in the training and testing phase.

A novel minimum generalized cross entropy-based multilevel segmentation technique for the brain MRI/dermoscopic images

BACKGROUND

One of the challenging and the primary stages of medical image examination is the identification of the source of any disease, which may be the aberrant damage or change in tissue or organ caused by infections, injury, and a variety of other factors. Any such condition related to skin or brain sometimes advances in cancer and becomes a life-threatening disease. So, an efficient automatic image segmentation approach is required at the initial stage of medical image analysis.

PURPOSE

To make a segmentation process efficient and reliable, it is essential to use an appropriate objective function and an efficient optimization algorithm to produce optimal results.

METHOD

The above problem is resolved in this paper by introducing a new minimum generalized cross entropy (MGCE) as an objective function, with the inclusion of the degree of divergence. Another key contribution is the development of a new optimizer called opposition African vulture optimization algorithm (OAVOA). The proposed optimizer boosted the exploration, skill by inheriting the opposition-based learning.

THE RESULTS

The experimental work in this study starts with a performance evaluation of the optimizer over a set of standards (23 numbers) and IEEE CEC14 (8 numbers) Benchmark functions. The comparative analysis of test results shows that the OAVOA outperforms different state-of-the-art optimizers. The suggested OAVOA-MGCE based multilevel thresholding approach is carried out on two different types of medical images - Brain MRI Images (AANLIB dataset), and dermoscopic images (ISIC 2016 dataset) and found superior than other entropy-based thresholding methods.

Stable and actionable explanations of black-box models through factual and counterfactual rules

Recent years have witnessed the rise of accurate but obscure classification models that hide the logic of their internal decision processes. Explaining the decision taken by a black-box classifier on a specific input instance is therefore of striking interest. We propose a local rule-based model-agnostic explanation method providing stable and actionable explanations. An explanation consists of a factual logic rule, stating the reasons for the black-box decision, and a set of actionable counterfactual logic rules, proactively suggesting the changes in the instance that lead to a different outcome. Explanations are computed from a decision tree that mimics the behavior of the black-box locally to the instance to explain. The decision tree is obtained through a bagging-like approach that favors stability and fidelity: first, an ensemble of decision trees is learned from neighborhoods of the instance under investigation; then, the ensemble is merged into a single decision tree. Neighbor instances are synthetically generated through a genetic algorithm whose fitness function is driven by the black-box behavior. Experiments show that the proposed method advances the state-of-the-art towards a comprehensive approach that successfully covers stability and actionability of factual and counterfactual explanations.

Structural Optimisation and Design of a Cable-Driven Hyper-Redundant Manipulator for Confined Semi-Structured Environments

Structural optimisation of robotic manipulators is critical for any manipulator used in confined semi-structured environments, such as in agriculture. Many robotic manipulators utilised in semi-structured environments retain the same characteristics and dimensions as those used in fully-structured industrial environments, which have been proven to experience low dexterity and singularity issues in challenging environments due to their structural limitations. When implemented in environments other than fully-structured industrial environments, conventional manipulators are liable to singularity, joint limits and workspace obstacles. This makes them inapplicable in confined semi-structured environments, as they lack the flexibility to operate dexterously in such challenging environments. In this paper, structural optimisation of a hyper-redundant cable-driven manipulator is proposed to improve its performance in semi-structured and challenging confined spaces, such as in agricultural settings. The optimisation of the manipulator design is performed in terms of its manipulability and kinematics. The lengths of the links and the joint angles are optimised to minimise any error between the actual and desired position/orientation of the end-effector in a confined semi-structured task space, as well as to provide optimal flexibility for the manipulators to generate different joint configurations for obstacle avoidance in confined environments. The results of the optimisation suggest that the use of a redundant manipulator with rigid short links can result in performance with higher dexterity in confined, semi-structured environments, such as agricultural greenhouses.

Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. DE has grown steadily since its beginnings due to its ability to solve various issues in academics and industry. Different mutation techniques and parameter choices influence DE's exploration and exploitation capabilities, motivating academics to continue working on DE. This survey aims to depict DE's recent developments concerning parameter adaptations, parameter settings and mutation strategies, hybridizations, and multi-objective variants in the last twelve years. It also summarizes the problems solved in image processing by DE and its variants.

Analysis of heat transmission in convective, radiative and moving rod with thermal conductivity using meta-heuristic-driven soft computing technique

ABSTRACT

The present study analyzes the thermal attribute of conductive, convective, and radiative moving fin with thermal conductivity and constant velocity. The basic Darcy's model is utilized to formulate the governing equation for the problem, which is further nondimensionalized using certain variables. Moreover, an effective soft computing paradigm based on the approximating ability of the feedforword artificial neural networks (FANN's) and meta-heuristic approach of global and local search optimization techniques is developed to quantify the effect of variations in significant parameters such as ambient temperature, radiation-conduction number, Peclet number, nonconstant thermal conductivity, and initial temperature parameter on the temperature gradient of the rod. The results by the proposed FANN-AOA-SQP algorithm are compared with radial basis function approximation, Runge-Kutta-Fehlberg method and machine-learning algorithms. An extensive graphical and statistical analysis based on solution curves and errors such as absolute errors, mean square error, standard deviations in Nash-Sutcliffe efficiency, mean absolute deviations, and Theil's inequality coefficient are performed to show the accuracy, ease of implementation, and robustness of the design scheme.

A hybrid method of time series forecasting based on information granulation and dynamic selection strategy1

Forecasting trend and variation ranges for time series has been challenging but crucial in real-world modeling. This study designs a hybrid time series forecasting (FIGDS) model based on granular computing and dynamic selection strategy. Firstly, with the guidance of the principle of justifiable granularity, a collection of interval-based information granules is formed to characterize variation ranges for time series on a specific time domain. After that, the original time series is transformed into granular time series, contributing to dealing with time series at a higher level of abstraction. Secondly, the L 1 trend filtering method is applied to extract trend series and residual series. Furthermore, this study develops hybrid predictors of the trend series and residual series for forecasting the variation range of time series. The ARIMA model is utilized in the forecasting task of the residual series. The dynamic selection strategy is employed to identify the ideal forecasting models from the pre-trained multiple predictor system for forecasting the test pattern of the trend series. Eventually, the empirical experiments are carried out on ten time series datasets with a detailed comparison for validating the effectiveness and practicability of the established hybrid time series forecasting method.

A Multi-Part Orientation Planning Schema for Fabrication of Non-Related Components Using Additive Manufacturing

Additive manufacturing (AM) is a technique that progressively deposits material in layer-by-layer manner (or in additive fashion) for producing a three-dimensional (3D) object, starting from the computer-aided design (CAD) model. This approach allows for the printing of complicated shaped objects and is quickly gaining traction in the aerospace, medical implant, jewelry, footwear, automotive, and fashion industries. AM, which was formerly used for single part customization, is currently being considered for mass customization of parts because of its positive impacts. However, part quality and build time are two main impediments to the deployment of AM for mass production. The optimal part orientation is fundamental for maximizing the part's quality as well as being critical for reducing the fabrication time. This research provides a new method for multi-part AM production that improves quality while reducing overall build time. The automatic setup planning or orientation approach described in this paper employs two objective functions: the quality of the build component and the build time. To tackle the given problem, it introduces a three-step genetic algorithm (GA)-based solution. A feature-based technique is utilized to generate a collection of finite alternative orientations for each component within a specific part group to ensure each part's individual build quality. Then, a GA was utilized to find the best combination of part build orientations at a global optimal level to reduce material consumption and build time. A case study of orienting nine components concurrently inside a given building chamber was provided for illustration. The findings suggest that the developed technique can increase quality, reduce support waste, and shorten overall production time. When components are positioned optimally rather than in random orientations, build time and support volume are reduced by approximately 7% and 16%, respectively.

Optimization of Zero-Energy Buildings Based on Multicriteria Optimization Method

This paper uses SketchUp and OpenStudio to design and optimize the multiperformance parameters of the near-zero energy consumption residential building scheme stage. By establishing a basic model, the performance parameters such as insulation thickness, typical outer window, window-to-wall ratio, and their contribution rates within the threshold range are optimized for energy conservation; simulate the energy consumption of the determined multiperformance parameter combination scheme, select the most energy-saving scheme, and calculate its energy-saving contribution rate; calculate the life cycle cost of the above schemes, select the scheme with the lowest cost, and analyze its economic benefits; select energy-saving cost and economic benefit, and calculate energy-saving contribution rate and economic benefit. Finally, through the comparative analysis of the design and test data, this paper concludes that EnergyPlus will optimize the multiperformance parameters of the near-zero energy consumption residential building scheme stage. At the same time, the multiobjective optimization method and process of the passive energy-saving technology of the near-zero energy consumption building proposed in this paper, as well as the technology combination template under different needs, provide a reference for the selection of the technology path.

Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion

Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.

MLP-PSO Hybrid Algorithm for Heart Disease Prediction

Background: Machine Learning (ML) is becoming increasingly popular in healthcare, particularly for improving the timing and accuracy of diagnosis. ML can provide disease prediction by analyzing vast amounts of healthcare data, thereby, empowering patients and healthcare providers with information to make informed decisions about disease prevention. Due to the rising cost of treatment, one of the most important topics in clinical data analysis is the prediction and prevention of cardiovascular disease. It is difficult to manually calculate the chances of developing heart disease due to a myriad of contributing factors. Objective: The aim of this paper is to develop and compare various intelligent systems built with ML algorithms for predicting whether a person is likely to develop heart disease using the publicly available Cleveland Heart Disease dataset. This paper describes an alternative multilayer perceptron (MLP) training technique that utilizes a particle swarm optimization (PSO) algorithm for heart disease detection. Methods: The proposed MLP-PSO hybrid algorithm and ten different ML algorithms are used in this study to predict heart disease. Various classification metrics are used to evaluate the performance of the algorithms. Results: The proposed MLP-PSO outperforms all other algorithms, obtaining an accuracy of 84.61%. Conclusions: According to our findings, the current MLP-PSO classifier enables practitioners to diagnose heart disease earlier, more accurately, and more effectively.

Optimization of the Delivery Time within the Distribution Network, Taking into Account Fuel Consumption and the Level of Carbon Dioxide Emissions into the Atmosphere

The evolution of changes in shopping in the modern society necessitates suppliers to seek new solutions consisting of increasing the efficiency of transport processes. When it comes to controlling the flow of goods in modern distribution networks, planning and timely deliveries are of particular importance. The first factor creating a competitive advantage involves the tendency to shorten order delivery times, especially for products with a short shelf life. Shorter delivery times, in turn, extend the period of effective residence of the product “available on the shelf”, increasing the likelihood of its sale. The second component in line with the Sustainable Development Strategy consists of aspects related to the protection of the natural environment, in particular those related to car transport. In this case, the fuel consumption and the level of emitted toxic substances (including carbon dioxide) are analyzed and assessed. Bearing in mind the above, this article presents the problem of optimizing the delivery time within the assumed distribution network and its solution, enabling the company to develop and optimal plan for the transport of products with a short shelf life. The paper proposes a model that takes into account minimization of the delivery time, while estimating the values of fuel consumption and CO2 emissions for the variants considered. The means of transport were medium-duty trucks. Three variants of the assumptions were considered, and algorithms implemented in MS Excel and MATLAB software were used to perform the optimization. Using the MATLAB environment, a more favorable value of the objective function was obtained for the variant without additional constraints. On the other hand, the algorithm implemented in MS Excel more effectively searched the set of acceptable solutions with a larger number of constraining conditions.

Milk-Run Collection Monitoring System Using the Internet of Things Based on Swarm Intelligence

In our country Morocco, several dairy factories are placed in rural regions with a bad road network, which means that milk collection has a significant impact on profit, affecting milk transport costs. Actually, the milk run logistics process has been transformed from a traditional farm to the new cheese factory, so it’s needed efficient methods and models to improve the process of production and collection of milk from those units. For that, we will apply new technologies such as the internet of things (IoT) and big data to collect and analyze this information to optimize the milk delivery process. The main goal of this work is to design a new smart decision method using the internet of things and big data to optimize the milk run logistics, reduce the cost of transportation and improve collection density. This method will be based on the swarm artificial intelligence concept to find and calculate the shortest path between units to optimize the collection of milk.

Blockchain Technology in the Food Supply Chain

This paper explains about the feasibility of Blockchain Technology in food organization. The technology of BCT helps the organizations to achieve integrity among peer-to-peer nodes, such as maintaining proof of work, reducing intermediaries, traceability, etc. It can be applied in the BCT at different levels of Supply Chain Management processes. This empirical study was conducted with the help of the primary data. The data was collected from food industry managers who have knowledge about the BCT in the process of supply chain management. The questionnaire was prepared based on the different supply chain activities like procurement, pre-processing, logistics, warehousing, inventory management, distribution, retailing, processing, and marketing activities. Based on the literature and data analysis, the BCT had the greatest advantages are cost reduction, traceability, time-saving, immutability, authentication and proof of work. The major weaknesses that are associated with present employees having a lack of knowledge, limited scalability, complexity in usage, and high initial cost.

Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions

Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as K-means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems.

Memetic algorithm for unrelated parallel machine scheduling problem with grey processing times

Purpose

This paper aims to provide a promising memetic algorithm (MA) for an unrelated parallel machine scheduling problem with grey processing times by using a simple dispatching rule in the local search phase of the proposed MA.

Design/methodology/approach

This paper proposes a MA for an unrelated parallel machine scheduling problem where the objective is to minimize the sum of weighted completion times of jobs with uncertain processing times. In the optimal schedule of the problem’s single machine version with deterministic processing time, the machine has a sequence where jobs are ordered in their increasing order of weighted processing times. The author adapts this property to some of their local search mechanisms that are required to assure the local optimality of the solution generated by the proposed MA. To show the efficiency of the proposed algorithm, this study uses other local search methods in the MA within this experiment. The uncertainty of processing times is expressed with grey numbers.

Findings

Experimental study shows that the MA with the swap-based local search and the weighted shortest processing time (WSPT) dispatching rule outperforms other MA alternatives with swap-based and insertion-based local searches without that dispatching rule.

Originality/value

A promising and effective MA with the WSPT dispatching rule is designed and applied to unrelated parallel machine scheduling problems where the objective is to minimize the sum of the weighted completion times of jobs with grey processing time.

Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization

The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficiency and efficacy of regression testing techniques can be improved with the use of optimization approaches. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing. The algorithm is improved by employing a fix-up mechanism to perform perturbation for the combinatorial TCP problem. Second, the dynamic contraction-expansion coefficient is used to accelerate the convergence. It is followed by an adaptive test case selection strategy to choose the modification-revealing test cases. Finally, the superfluous test cases are removed. Furthermore, the algorithm’s robustness is analyzed for fault as well as statement coverage. The empirical results reveal that the proposed algorithm performs better than the Genetic Algorithm, Bat Algorithm, Grey Wolf Optimization, Particle Swarm Optimization and its variants for prioritizing test cases. The findings show that inclusivity, test selection percentage and cost reduction percentages are higher in the case of fault coverage compared to statement coverage but at the cost of high fault detection loss (approx. 7%) at the test case reduction stage.

A Brain Tumor Image Segmentation Method Based on Quantum Entanglement and Wormhole Behaved Particle Swarm Optimization

Purpose

Although classical techniques for image segmentation may work well for some images, they may perform poorly or not work at all for others. It often depends on the properties of the particular image segmentation task under study. The reliable segmentation of brain tumors in medical images represents a particularly challenging and essential task. For example, some brain tumors may exhibit complex so-called “bottle-neck” shapes which are essentially circles with long indistinct tapering tails, known as a “dual tail.” Such challenging conditions may not be readily segmented, particularly in the extended tail region or around the so-called “bottle-neck” area. In those cases, existing image segmentation techniques often fail to work well.

Methods

Existing research on image segmentation using wormhole and entangle theory is first analyzed. Next, a random positioning search method that uses a quantum-behaved particle swarm optimization (QPSO) approach is improved by using a hyperbolic wormhole path measure for seeding and linking particles. Finally, our novel quantum and wormhole-behaved particle swarm optimization (QWPSO) is proposed.

Results

Experimental results show that our QWPSO algorithm can better cluster complex “dual tail” regions into groupings with greater adaptability than conventional QPSO. Experimental work also improves operational efficiency and segmentation accuracy compared with current competing reference methods.

Conclusion

Our QWPSO method appears extremely promising for isolating smeared/indistinct regions of complex shape typical of medical image segmentation tasks. The technique is especially advantageous for segmentation in the so-called “bottle-neck” and “dual tail”-shaped regions appearing in brain tumor images.

Applying Deep Learning-Based Human Motion Recognition System in Sports Competition

The exploration here intends to compensate for the traditional human motion recognition (HMR) systems' poor performance on large-scale datasets and micromotions. To this end, improvement is designed for the HMR in sports competition based on the deep learning (DL) algorithm. First, the background and research status of HMR are introduced. Then, a new HMR algorithm is proposed based on kernel extreme learning machine (KELM) multidimensional feature fusion (MFF). Afterward, a simulation experiment is designed to evaluate the performance of the proposed KELM-MFF-based HMR algorithm. The results showed that the recognition rate of the proposed KELM-MFF-based HMR is higher than other algorithms. The recognition rate at 10 video frame sampling points is ranked from high to low: the proposed KELM-MFF-based HMR, support vector machine (SVM)-MFF-based HMR, convolutional neural network (CNN) + optical flow (CNN-T)-based HMR, improved dense trajectory (IDT)-based HMR, converse3D (C3D)-based HMR, and CNN-based HMR. Meanwhile, the feature recognition rate of the proposed KELM-MFF-based HMR for the color dimension is higher than the time dimension, by up to 24%. Besides, the proposed KELM-MFF-based HMR algorithm's recognition rate is 92.4% under early feature fusion and 92.1% under late feature fusion, higher than 91.8 and 90.5% of the SVM-MFF-based HMR. Finally, the proposed KELM-MFF-based HMR algorithm takes 30 and 15 s for training and testing. Therefore, the algorithm designed here can be used to deal with large-scale datasets and capture and recognize micromotions. The research content provides a reference for applying extreme learning machine algorithms in sports competitions.

Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

The simplicity, transparency, reliability, high efficiency and robust nature of PID controllers are some of the reasons for their high popularity and acceptance for control in process industries around the world today. Tuning of PID control parameters has been a field of active research and still is. The primary objectives of PID control parameters are to achieve minimal overshoot in steady state response and lesser settling time. With exception of two popular conventional tuning strategies (Ziegler Nichols closed loop oscillation and Cohen-Coon's process reaction curve) several other methods have been employed for tuning. This work accords a thorough review of state-of-the-art and classical strategies for PID controller parameters tuning using metaheuristic algorithms. Methods appraised are categorized into classical and metaheuristic optimization methods for PID parameters tuning purposes. Details of some metaheuristic algorithms, methods of application, equations and implementation flowcharts/algorithms are presented. Some open problems for future research are also presented. The major goal of this work is to proffer a comprehensive reference source for researchers and scholars working on PID controllers.