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This research introduces a novel optimization algorithm, weIghted meaN oF vectOrs (INFO), integrated with the Extreme Learning Machine (ELM) to enhance the predictive capabilities of the model for carbon dioxide (CO2) emissions. INFO optimizes ELM’s weight and bias. In six classic test problems and CEC 2019 functions, INFO demonstrated notable strengths in achieving optimal solutions for various functions. The proposed hybrid model, ELM-INFO, exhibits superior performance in forecasting CO2 emissions, as substantiated by rigorous evaluation metrics. Notably, it achieves a superior R2 value of 0.9742, alongside minimal values in Root Mean Squared Error (RMSE) at 0.01937, Mean Squared Error (MSE) at 0.00037, Mean Absolute Error (MAE) at 0.0136, and Mean Absolute Percentage Error (MAPE) at 0.0060. These outcomes underscore the robustness of ELM-INFO in accurately predicting CO2 emissions within the testing dataset. Additionally, economic growth is the most significant element, as indicated by ELM-INFO’s permutation significance analysis, which causes the model’s MSE to increase by 19%. Trade openness and technological innovation come next, each adding 7.6% and 8.1% to the model’s MSE increase, respectively. According to ELM-INFO’s performance, it’s a powerful tool for developing ecologically sound policies that improve environmental resilience and sustainability.

Health-related data has a decisive role in disease diagnosis. Collecting relevant information from health-related data in medical records has been facilitated by evaluating the features of the data. Relevant research has shown that outcomes are significantly impacted by the use of feature selection (FS) in different medical domain data. FS provides an analysis of the most significant features to improve classification accuracy. The FS technique aims at minimizing the number of input variables and computational overload to maximize classification performance results. However, identifying the optimal features poses issues due to the high dimensionality of large features and the small sample size of health-related data. The metaheuristics optimization algorithm (MOA) plays an important role in generating the best subset features with exploration and exploitation phases. This study experiments with well-known MOAs and supervised learning from the UC Irvine Machine Learning Repository, PhysioNet, Kent Ridge Bio-Medical Dataset, and MIMIC-III v1.4 Repository with varying feature dimensions. To increase the quality of health-related data, this study proposes missing data imputation based on a deep learning approach, an autoencoder (AE). With AE imputation, the performance results obtain 0.0167 mean squared error (MSE) and 0.129 root mean squared error (RMSE). As a result, MOA shows its excellence in achieving minimal features, but still outstanding performance in low- and high-dimensional data. MOA is successfully applied to varying diverse health-related datasets with low- and high-dimensional data.

Robust Intrusion Detection Systems (IDS) are increasingly necessary in the age of big data due to the growing volume, velocity, and variety of data generated by modern networks. Metaheuristic algorithms offer a promising approach to enhance IDS performance in terms of optimal feature selection. Combining these algorithms along with Machine learning (ML) for the creation of an IDS makes it possible to improve detection accuracy, reduce false positives and negatives, and enhance the efficiency of network monitoring. Our study proposes using metaheuristic algorithms along with machine learning classifiers for feature selection to optimize the number of features from the data set of computer network traffic. We have tested several combinations of algorithms viz., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) along with ML algorithms viz., Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB) and Logistic Regression (LR). The combinations of algorithms have been tested over the NSS-KDD and kddcupdata_10% data sets. We have drawn several insights on feature selection scores with respect to test scores, FI scores, recall and precision for various algorithm combinations. The feature selection time has also been highlighted to showcase the fastest-performing algorithm combinations. Ultimately, we have presented three combinations of algorithms depending on organizational IDS requirements and provided separate solutions for each.

Reconfiguration of a distribution network is one of the main approaches to control and enhance distribution network indices, such as voltage profile and power losses. Distribution network operators perform reconfiguration for long-term or short-term periods based on network equipment and intended objectives. Long-term or static reconfiguration is suitable for traditional and modern networks with conventional switches. On the other hand, modern distribution networks that are equipped with one or more remote control switches can perform reconfigurations within short-term periods, to maximize predefined objectives. This paper presents a comprehensive review of recent literature on network reconfiguration. Reconfiguration methodologies are classified into five groups: classical methods, heuristic methods, metaheuristic methods, hybrid methods, and methods based on machine learning. The paper provides a general definition and comparison of the categories and discusses their application in dynamic and static reconfiguration. The paper introduces dynamic reconfiguration as the future challenges in smart and modern distribution networks and for the first time categorizes various methodologies in dynamic reconfiguration. The paper serves as a guide to assist engineers and researchers in selecting the most suitable methodology based on their system equipment and objectives.

To improve power system operation and management and accomplish modern power system requirements, a new algorithm named two-archive harris hawk optimization (TwoArchHHO) is proposed to solve many-objective optimal power flow (MaOOPF) problems in this work. For modern power systems, only single-objective and multiobjective (2-3 objectives) optimal power flow problems (MOOPF) are inadequate. So, the problems become many-objective (more than 3 objectives) optimal power flow problem which is more complicated to be solved. Although several metaheuristic algorithms have been proposed to solve MOOPF problems, very few algorithms have been introduced to solve MaOOPF problems and high-performance algorithms are still required to solve MaOOPF problems which are more complicated. To solve the complicated MaOOPF problems, TwoArchHHO is proposed by adding the two-archive concepts of the improved two-archive algorithm into the harris hawk optimization (HHO) in order to enhance the searchability and eventually provide superior solutions. The objective functions considered to be minimized include fuel cost, emission, transmission line loss, and voltage deviation to improve power systems in the economic, environmental, and secure aspects. Several sizes of IEEE standard systems, which are IEEE 30-, 57-, and 118-bus systems, are tested to evaluate the performance of the proposed TwoArchHHO. The simulation results comprise Pareto fronts, best-compromised solutions, and hypervolume analysis are generated and compared with results from several algorithms in the literature. The data provided by the experimental trials and the hypervolume performance metric were examined using statistical testing methods. The results indicate that the TwoArchHHO obtained better optimal solutions than those of the compared algorithms including its traditional algorithms, especially in large systems. Based on the best-compromised solutions, the TwoArchHHO provided one best objective aspect among the compared algorithm for most cases. Based on the hypervolume, the TwoArchHHO generated better hypervolume values than those of the compared algorithms around 33.96% to 99.59% in the tested systems.

Homomorphic filtering (HF) is a methodology that separates an image into two components: illumination and reflectance. Through the processing of these components, it is possible to significantly improve the contrast of the low-frequency components while preserving the edges and sharp features of the image. The parameter values of the filter that produces the best possible contrast enhancement depends on the image conditions for each image. However, finding the optimal parameters for the filter can be challenging, often involves a trial-and-error process, and can be prone to errors due to human factors. In this paper, we consider the problem of identifying the filter parameters as an optimization problem. Under such conditions, the cluster chaotic optimization (CCO) method is used to efficiently explore the parameter space by evaluating an objective function that assesses the contrast quality of an enhanced image. The experimental results show that the proposed method produces competitive results in terms of quality, stability, and accuracy compared with other methods on various datasets. Different metrics were evaluated to demonstrate the quality of the results of our method compared with the other algorithms.

In this research, the optimal placement and capacity of battery energy storage systems (BESS) in distribution networks integrated with photovoltaics (PV) and electric vehicles (EVs) have been proposed. The main objective function is to minimize the system costs including installation, replacement, and operation and maintenance costs of the BESS. The replacement cost has been considered over 20 years while the operation and maintenance costs are the costs incurred by transmission line loss, voltage regulation, and peak demand. To solve the problem, three metaheuristic algorithms, namely particle swarm optimization (PSO), african vultures optimization algorithm (AVOA), and salp swarm algorithm (SSA), are employed. The proposed approach is evaluated on the IEEE 33- and 69-bus distribution systems integrated with PV and EVs. The results provided by the considered algorithms are compared in terms of the objective function, system efficiency enhancement, payback period, and statistical analysis. The simulation results show that after the BESS installation, the voltage profile can be improved, transmission loss is reduced, and peak demand is decreased where PSO provides the best objective values and AVOA achieves the fastest payback periods in both systems.

There are 1641 companies listed on the National Stock Exchange of India. It is undoubtedly infeasible for a retail investor to invest in all the stocks. It is a well-known fact that the portfolio’s return is an average return of all its constituent stocks, and risk will be less than or equal to the maximum risk of all the portfolio components. This paper is unique as it elaborates on the entire portfolio selection, optimization, and management process. Portfolio selection is accomplished through the K-Means algorithm. Optimization is achieved utilizing the genetic algorithm, and a sliding window is applied for portfolio management. Four different ways of portfolio calculation, namely, equally-weighted portfolio, global minimum variance portfolio, market cap-weighted portfolio, and maximum Sharpe ratio portfolio, are applied. The results depict that all three optimized portfolios outperform the Nifty index. The dataset for the study is obtained from globaldatafeeds.in.

Several issues have been reported with the expansion of the electric power grid and the increasing use of intermittent power sources, such as the need for expensive transmission lines and the issue of cascading blackouts, which can adversely affect critical infrastructures. Microgrids (MG) have been widely accepted as a viable solution to improve grid reliability and resiliency, ensuring continuous power supply to loads. However, to ensure the effective operation of the Distributed Energy Resources (DER), Microgrids must have Energy Management and Control Systems (EMCS). Therefore, considerable research has been conducted to achieve smooth profiles in grid parameters during operation at optimum running cost. This paper aims to provide a review of EMCS techniques that have evolved in recent years. Firstly, the fundamentals of microgrids are discussed for a general overview of the field. Then, a critical literature review is undertaken for the various methods applied for EM optimization in microgrid applications. Multiple factors have been explored in the objective functions throughout this review, including MG daily operational costs, energy storage degradation, revenue through trading with the grid or other parties, and Greenhouse Gas (GHG) emissions. Control systems have been reviewed by categorizing them based on the different applications in MGs for stable operation. This paper also focuses on IEEE standards related to MG operation and control to facilitate other researchers to build upon a standardized set of rules and to enhance the interoperability of the diverse EMCS techniques.

As a new attempt to design a precise mathematical model for the proton exchange membrane fuel cell (PEMFC), in this paper, three recently-proposed well-established optimizers: horse herding optimization algorithm (HOA), seagull optimization algorithm (SOA) and gradient-based optimizer (GBO) integrated with two newly-proposed effective strategies, namely self-adaptive strategy, and ranking-based updating strategy, have been extensively investigated to accurately estimate the unknown parameters of this model for accomplishing a better output voltage of the simulated PEMFC stacks. Those hybridized algorithms were briefly named HHOA, HSOA, and HGBO. To assess the performance of those proposed algorithms, six common PEMFC stacks were used and their outcomes were extensively compared with the standard algorithms and some of the state-of-the-arts under various performance metrics and the Wilcoxon rank-sum test. The experimental findings show the effectiveness of both HGBO and HSOA in terms of convergence speed and final accuracy; However, HSOA could be more stable. The source code of this study is publicly available at https://drive.matlab.com/sharing/d9263036-9f80-4a40-bad9-ad476ed19c69.

With the emergence of the multicore architecture and the increase of chip operating frequency, power optimization has become a key step of circuit logic synthesis. Aiming at the XNOR/OR circuits, with the goal of minimizing power, construct the optimal polarity fixed-polarity Reed–Muller (FPRM) circuits power optimization scheme. However, the power optimization for FPRM circuits is a multipeak combinatorial optimization problem, we first propose a metaheuristic optimization algorithm (MOA), which includes the global exploration optimizer, local deep exploitation optimizer, and initial population and uses the proposed differential evolution optimization, fierce wolf siege algorithm-based tabu search, and improved skew tent map to make the population evolve. Based on the proposed Huffman tree construction algorithm and MOA, we propose an efficient power optimization approach (EPOA) to find the minimum power FPRM circuit. Experimental results on the benchmark circuits confirm the effectiveness of EPOA. [ABSTRACT FROM AUTHOR]

The capacitated arc routing problem (CARP) is a challenging combinatorial optimization problem abstracted from many real-world applications, such as waste collection, road gritting, and mail delivery. However, few studies considered dynamic changes during the vehicles’ service, which can cause the original schedule infeasible or obsolete. The few existing studies are limited by the dynamic scenarios considered, and by overly complicated algorithms that are unable to benefit from the wealth of contributions provided by the existing CARP literature. In this article, we first provide a mathematical formulation of dynamic CARP (DCARP) and design a simulation system that is able to consider dynamic events while a routing solution is already partially executed. We then propose a novel framework which can benefit from the existing static CARP optimization algorithms so that they could be used to handle DCARP instances. The framework is very flexible. In response to a dynamic event, it can use either a simple restart strategy or a sequence transfer strategy that benefits from the past optimization experience. Empirical studies have been conducted on a wide range of DCARP instances to evaluate our proposed framework. The results show that the proposed framework significantly improves over state-of-the-art dynamic optimization algorithms. [ABSTRACT FROM AUTHOR]

Once deployed, a decentralized blockchain system ensures that it will operate faithfully so that no one can interfere with or manipulate its predefined regulations, such as block size and block creation interval investigated in this paper. However, fixed regulations prevent that system from adapting to the change of the environment, such as increasing the underlying network capacity, and result in sub-optimal performance. For example, Bitcoin remains at 7 TPS (transactions per second), even operating over the current Internet. In this paper, we propose a new paradigm for defining the behavior of a consensus system, named as Meta-Regulation, which allows autonomous evolution of the system behavior. A meta-regulation adjusts the actual behavior of a consensus system in response to the changing capacity of the underlying infrastructure and the community of participants. We demonstrate the effectiveness of the proposed meta-regulation by achieving significantly improved throughput and latency for Bitcoin, adapted to the current capacity of the Internet. Our experimental results show that Meta-Regulation can achieve at least $7\times $ performance improvement over Bitcoin network deployed in 2009, resulting in 49.7 TPS or 68% reduction confirmation latency by fully utilizing the bandwidth and the computing power of average network nodes. [ABSTRACT FROM AUTHOR]

This article proposes a novel and efficient dual-space co-evolutionary memetic algorithm (DCMA) to tackle a practical hybrid differentiation flowshop scheduling problem with limited buffer constraints. In this scheduling problem, jobs are divided into different types and each job consists of multiple parts. The manufacturing of a job involves three stages: 1) parts fabrication on first-stage parallel machines; 2) parts assembly on second-stage single machine; and 3) job differentiation on one of third-stage dedicated machines. Due to the assembly operation and limited buffers between adjacent stages, deadlock and blocking will occur. We formulate the problem and present a deadlock handling policy to guarantee all schedules feasible. Then, we propose the DCMA metaheuristic to approximate the optimal solutions in acceptable time. Global exploration of DCMA is performed by a hybrid of three parts: 1) a continuous optimizer to be executed in continuous search space; 2) a discrete optimizer to be executed in combinatorial solution space; and 3) a meta-Lamarckian learning-based selection mechanism for adaptively determining that which optimizer is more suitable for current global exploration campaign. To balance exploration and exploitation, three problem-special local search strategies are presented, which collaborates with each other and are adaptively started to avoid high computational costs. The effect of parameter setting on DCMA is checked by the Taguchi method of design of experiment. The computational experiments demonstrate the effectiveness of DCMA special designs, and show that DCMA performs better than the existing algorithms for the considered problem. [ABSTRACT FROM AUTHOR]

Satellite observation scheduling plays a significant role in improving the efficiency of Earth observation systems. To solve the large-scale multisatellite observation scheduling problem, this article proposes an ensemble of metaheuristic and exact algorithms based on a divide-and-conquer framework (EHE-DCF), including a task allocation phase and a task scheduling phase. In the task allocation phase, each task is allocated to a proper orbit based on a metaheuristic incorporated with a probabilistic selection and a tabu mechanism derived from ant colony optimization and tabu search, respectively. In the task scheduling phase, we construct a task scheduling model for every single orbit and solve the model by using an exact method (i.e., branch and bound, B&B). The task allocation and task scheduling phases are performed iteratively to obtain a promising solution. To validate the performance of the EHE-DCF, we compare it with B&B, three divide-and-conquer-based metaheuristics, and a state-of-the-art metaheuristic. Experimental results show that the EHE-DCF can obtain higher scheduling profits and complete more tasks compared with existing algorithms. The EHE-DCF is especially efficient for large-scale satellite observation scheduling problems. [ABSTRACT FROM AUTHOR]

Sensory data is only meaningful if correctly associated with originating locations, e.g., in three-dimensional wireless sensor systems inside an aircraft cabin. Received signal strength is a cost-effective option to locate low-cost sensors due to its universal availability, but suffers from coarse ranging accuracy with multiplicative errors. This paper presents a new received signal strength-based approach to locating a large number of low-cost sensors given their possible three-dimensional installation points. Our approach achieves the accuracy which has not been achieved in the literature. The approach first cooperatively locates the sensors in the continuous three-dimensional spaces, then associates the continuous location estimates to the installation points, and finally refines the association with likelihood ascent search. A new convex relaxation-based optimization is designed for cooperative localization in continuous three-dimensional spaces. The Kuhn-Munkres algorithm is generalized for the association. Cramér-Rao Lower Bounds are derived to specify the local three-dimensional regions in which refinement is carried out to improve the final accuracy with little complexity overhead. The proposed approach is validated experimentally with signal strength measurements collected in a Fokker 100 passenger plane, achieves 100% accuracy in all experiments conducted, and outperforms state-of-the-art metaheuristics significantly with much shorter execution time. Note to Practitioners—This paper is motivated by a goal to realize various aircraft automation applications through three-dimensional localization and association of wireless sensors. For example, automatic identification of seat locations can be implemented efficiently with wireless sensors, which enables effortless re-association between control buttons in the seats and the corresponding functions after cabin refurbishment. Meanwhile, automatic detection of missing/misplaced safety equipment can be carried out to improve the efficiency of flight preparation. There currently lacks an received signal strength-based three-dimensional sensor association method in indoor environments. The theoretical framework proposed in this paper aims to utilize the readily available received signal strength measurements to associate Commercial-Off-The-Shelf sensors to their possible installation points. This approach avoids the requirement of labor-intensive fingerprinting effort and high-cost equipment. The proposed approach has been tested and evaluated using both synthetic data and real data collected in a passenger plane as a proof of concept. [ABSTRACT FROM AUTHOR]

This article is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely, decomposition methods and hybridization methods, such as memetic algorithms and local search. In this part, we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multiobjective optimization, constraint handling, overlapping components, the component imbalance issue and benchmarks, and applications. The article also includes a discussion on pitfalls and challenges of the current research and identifies several potential areas of future research. [ABSTRACT FROM AUTHOR]

This article deals with a practical distributed hybrid differentiation flowshop scheduling problem (DHDFSP) for the first time, where manufacturing products to minimize makespan criterion goes through three consecutive stages: 1) job fabrication in first-stage distributed flowshop factories; 2) job-to-product assembly based on specified assembly plan on a second-stage single machine; and 3) product differentiation according to customization on one of the third-stage dedicated machines. Considering the characteristics of multistage and diversified processing technologies of the problem, building new powerful evolutionary algorithm (EA) for DHDFSP is expected. To achieve this, we propose a general EA framework called distributed co-evolutionary memetic algorithm (DCMA). It includes four basic modules: 1) dual population (POP)-based global exploration; 2) elite archive (EAR)-oriented local exploitation; 3) elite knowledge transfer (EKT) among POPs and EAR; and 4) adaptive POP restart. EKT is a general model for information fusion among search agents due to its problem independence. In execution, four modules cooperate with each other and search agents co-evolve in a distributed way. This DCMA evolutionary framework provides some guidance in algorithm construction of different optimization problems. Furthermore, we design each module based on problem knowledge and follow the DCMA framework to propose a specific DCMA metaheuristic for coping with DHDFSP. Computational experiments validate the effectiveness of the DCMA evolutionary framework and its special designs, and show that the proposed DCMA metaheuristic outperforms the compared algorithms. [ABSTRACT FROM AUTHOR]

This article presents a hybrid metaheuristic optimization algorithm that combines particle filter (PF) and particle swarm optimization (PSO) algorithms. The new PF–PSO algorithm consists of two steps: the first generates randomly the particle population;and the second zooms the search domain. An application of this algorithm to the optimal tuning of proportional-integral-fuzzy controllers for the position control of a family of integral-type servo systems is then presented as a second contribution. The reduction in PF–PSO algorithm's cost function allows for reduced energy consumption of the fuzzy control system. A comparison with other metaheuristic algorithms on canonical test functions and experimental results are presented at the end of this article. [ABSTRACT FROM AUTHOR]

Few-shot learning under the $N$ N -way $K$ K -shot setting (i.e., $K$ K annotated samples for each of $N$ N classes) has been widely studied in relation extraction (e.g., FewRel) and image classification (e.g., Mini-ImageNet). Named entity recognition (NER) is typically framed as a sequence labeling problem where the entity classes are inherently entangled together because the entity number and classes in a sentence are not known in advance, leaving the $N$ N -way $K$ K -shot NER problem so far unexplored. In this paper, we first formally define a more suitable $N$ N -way $K$ K -shot setting for NER. Then we propose FewNER, a novel meta-learning approach for few-shot NER. FewNER separates the entire network into a task-independent part and a task-specific part. During training in FewNER, the task-independent part is meta-learned across multiple tasks and the task-specific part is learned for each individual task in a low-dimensional space. At test time, FewNER keeps the task-independent part fixed and adapts to a new task via gradient descent by updating only the task-specific part, resulting in it being less prone to overfitting and more computationally efficient. Compared with pre-trained language models (e.g., BERT and ELMo) which obtain the transferability in an implicit manner (i.e., relying on large-scale corpora), FewNER explicitly optimizes the capability of “learning to adapt quickly” through meta-learning. The results demonstrate that FewNER achieves state-of-the-art performance against nine baseline methods by significant margins on three adaptation experiments (i.e., intra-domain cross-type, cross-domain intra-type and cross-domain cross-type). [ABSTRACT FROM AUTHOR]

This article presents an alternative service of mobile charging stations for the large-scale charging of electric vehicles, which consider the spatiotemporal heterogeneity of charging requests. As charging infrastructure is the key determinant for the large-scale adoption of electric vehicles, state-of-the-art scheduling and control strategies need to be explored. The charging of electric vehicles in a conventional charging station even with the fast dc–dc chargers takes around 30 min, which results in congestion and large waiting queues at public charging stations. To account for this issue, a novel strategy of routing and scheduling mobile charging stations to charge electric vehicles without the constraints of time and space is discussed in detail. Furthermore, the traveling times of mobile charging stations in reality are stochastic in nature. We formulate the optimization problem to minimize the cost of charging and show that the problem formulated is a combination of a bin packing problem and a multicity traveling salesman problem; hence, it is NP-hard and cannot be solved in reasonable CPU time, unless P = NP. We, thus, present modified saving’s heuristic and modified genetic algorithm metaheuristic to solve the optimization problem. Furthermore, numerical simulations show that the proposed scheduling and routing algorithm requires less number of mobile charging stations and can appreciably reduce the cost of charging. [ABSTRACT FROM AUTHOR]

Multi-Agent Task Allocation is an emerging technology that changes the world in the epidemic scenario through its power to serve the needs of any hospital that requires unmanned operation. In this environment, the end user may want to have a better quality of unmanned service at low loss and high efficiency. We defined a new multi-agent task allocation problem (MATAP) in the epidemic scenario, and then MATAP was formulated. This paper presents a novel hybrid discrete approach that is based on the Differential Evolution Algorithm (DE) and Partial Swarm Optimization (PSO), namely D-DEPSO, for handling this problem. First, the initial personal population was handled by “mutation operation”. Modulus operations in the “mutation operation” modify the numerical overflow of a variable. Second, when updating the speed matrix, the speed matrix is discretized using the “round” function we have defined. Then, a random permutation was used to delete repeated numbers and to reinsert integers in the “crossover operation”. The diversity of the population was expanded by introducing the discrete mutation operation of the DE into the PSO and preserving the optimal solution for each generation using the properties of PSO. It can be used for optimizing a single objective function. Experimental results are compared with other existing metaheuristic algorithms, such as discrete DE, discrete PSO, improved discrete DE, improved discrete PSO, and improved discrete genetic algorithm, in terms of running time and loss. The experiments show that the optimal solutions obtained by D-DEPSO are better than those obtained by other five algorithms. For the actual problem, D-DEPSO can generate an optimal solution by optimal parameter setting to allocate tasks rationally. It can achieve a rational distribution of tasks in the prevention of disease.

In many applications, selecting the optimal features is a difficult task. Numerous In optimization problems, e.g., feature selection (FS) problem, have been solved using optimization algorithms. In this paper, the most discriminating features were chosen using a new chaotic gradient-based optimizer (CGBO) that combines chaotic maps with searching iterations of the gradient-based optimizer (GBO). Ten chaotic maps were utilised to update the parameters, eliminate a local optimum and premature convergence, accelerate convergence, and enhance the efficiency of GBO. A classifier was used in FS approaches to determine the best subset of characteristics. The proposed CGBO uses the k-nearest neighbor as an objective function for the classification process in FS. Ten datasets from the UCI machine learning repository were used to validate CGBO. In an experiment, CGBO outperformed five other metaheuristic algorithms: particle swarm optimization(PSO), moth flame optimizer(MFO), sine cosine algorithm(SCA), salp swarm algorithm(SSA), and GBO. The results demonstrated the capability of CGBO to find and select the optimal feature subset, which maximized the classification performance and minimized the number of features selected efficiently compared with other metaheuristic algorithms.

The necessity for clean and sustainable energy has shifted the energy sector’s interest in renewable energy sources. Photovoltaics (PV) is the most popular renewable energy source because the sun is ubiquitous. However, PV’s power transfer efficiency varies with different load’s electrical characteristics, temperatures on PV panels, and insolation conditions. Based on these factors, Maximum Power Point Tracking (MPPT) is a mechanism formulated as an optimization problem adjusting the PV to deliver the maximum power to the load. Under full insolation conditions, varying solar panel temperatures, and different loads MPPT problem is a convex optimization problem. However, when the PV’s surface is partially shaded, multiple power peaks are created in the power versus voltage (P-V) curve making MPPT non-convex. Unfortunately, all optimization strategies for MPPT under partial shading applied in previous works, from traditional techniques to Machine Learning and the recently proposed Nature-inspired algorithms, were either computationally expensive or/and led to extensive power losses. To this end, this work presents an algorithm that builds upon metaheuristic optimization algorithms to reduce their complexity further and mitigate the power losses during power tracking. Our experimental results demonstrated that the proposed algorithm converges faster to maximum power point with lower power losses during tracking compared to two very recently proposed MPPT algorithms under partial shading conditions.

Handball experts agree that the most crucial position in a handball match is that of the goalkeeper. Their performance can be a good predictor of a team’s ranking in tournaments. Despite this, few studies have been conducted on the relevance of every elite goalkeeper’s action to their performance in the match. This paper provides the features or criteria for objectively evaluating a handball goalkeeper based on their actions during a match. For this purpose, the feature-weighting problem is formulated as an optimization problem. The problem is solved using eight metaheuristic algorithms to adjust the weights of the features. Computer experiments using real data from the 2020 Women’s and Men’s European Handball Championships are carried out with these algorithms. The algorithms optimize the weights based on three metrics. The first metric is to identify the best goalkeeper; the second metric is to identify the top five goalkeepers, regardless of order; and the third metric is to identify and order the top five goalkeepers. A case study is carried out with real data from the 2021 Women’s and Men’s World Handball Championships, where the best goalkeeper found in both cases with the optimized weights coincide with the best goalkeeper chosen by the International Handball Federation (IHF). Finally, the paper shows the particularities and specific difficulties involved in evaluating handball goalkeepers.

Single-cell RNA sequencing (scRNA-seq) enables quantification of mRNA expression at the level of individual cells. scRNA-seq uncovers the disparity of cellular heterogeneity giving insights about the expression profiles of distinct cells revealing cellular differentiation. The rapid advancements in scRNA-seq technologies enable researchers to exploit questions regarding cancer heterogeneity and tumor microenvironment. The process of analyzing mainly clustering scRNA-seq data is computationally challenging due to its noisy high dimensionality nature. In this paper, a computational clustering approach is proposed to cluster scRNA-seq data based on consensus clustering using swarm intelligent optimization algorithms to accurately recognize cell subtypes. The proposed approach uses variational auto-encoders to handle the curse of dimensionality, as it operates to create a latent biologically relevant feature space representing the original data. The new latent space is then clustered using Particle Swarm Optimization Algorithm, Multi-Verse Optimization Algorithm and Grey Wolf Optimization Algorithm. A consensus solution is found using solutions returned by the swarm intelligent algorithms. The proposed approach automatically derives the number of clusters without any prior knowledge. To evaluate the performance of the proposed approach a total of four datasets have been used then a comparison against the existing methods in literature has been performed. Experimental results show that the proposed approach performs better than widely most used tools, achieving an adjusted rand index of.95,.75,.88,.9 for Biase, Goolam, Melanoma cancer and Lung cancer datasets respectively.

This article addresses the efficient numerical analysis of sparsely distributed small modifications in a large structure, that is, the sparsely distributed partial modification problem (SDPMP). A hierarchical matrix (H-matrix)-based fast direct solver is adopted to solve the SDPMP in a very short time for each new set of modifications. The main idea of this method is to reuse the parts of the compressed inverse matrix operator that do not change and only recompute the modified parts. In contrast with already existing algorithms designed to tackle a single localized modification, this new approach is still very fast for sparsely distributed small modifications, so it becomes a very attractive option to solve optimization problems efficiently, due to the very cheap analysis of the modified structure at each iteration of the optimization procedure. The efficiency of the solution of SDPMP is demonstrated for several cases involving passive and active metasurfaces and a reconfigurable antenna. As a practical example, a programmable metasurface reflector is optimized with particle swarm optimization to obtain a prescribed reflection pattern. [ABSTRACT FROM AUTHOR]

A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention. The prime objective of this article is to use radiological images as a tool to help in early diagnosis. The interval type 2 fuzzy clustering is blended with the concept of superpixels, and metaheuristics to efficiently segment the radiological images. Despite noise sensitivity of watershed-based approach, it is adopted for superpixel computation owing to its simplicity where the noise problem is handled by the important edge information of the gradient image is preserved with the help of morphological opening and closing based reconstruction operations. The traditional objective function of the fuzzy c-means clustering algorithm is modified to incorporate the spatial information from the neighboring superpixel-based local window. The computational overhead associated with the processing of a huge amount of spatial information is reduced by incorporating the concept of superpixels and the optimal clusters are determined by a modified version of the flower pollination algorithm. Although the proposed approach performs well but should not be considered as an alternative to gold standard detection tests of COVID-19. Experimental results are found to be promising enough to deploy this approach for real-life applications. [ABSTRACT FROM AUTHOR]

Subjective evaluation of tone-mapped images is tedious and time-consuming; therefore, it is desirable to have algorithms for automatic quality assessment. Many full-reference and blind metrics have been developed for this purpose, but their performance is generally evaluated on limited benchmark datasets. This leaves a possibility that the observed performance of the metric could be due to overfitting, and it might indeed not perform well for all scenes. In this work, we propose a novel framework using population-based metaheuristics to evaluate the performance of these metrics without requiring any subjectively evaluated reference dataset. The proposed algorithm does not modify the individual image pixels, instead, the tone-mapping curve is modified to synthesize realistic tone-mapped images for evaluation. Moreover, it is not required to know the underlying model of the evaluated metric, which is treated just like a black box and can be replaced by any other metric seamlessly. Therefore, any new metrics designed in the future can also be easily evaluated by simply replacing just one module in the proposed evaluation framework. We evaluate six existing metrics and synthesize images to which the metrics fail to assign appropriate scores for visual quality. We also propose a method to rank the relative performance of evaluated metrics, through a competition in which each metric tries to find the errors in the scores given by other metrics. [ABSTRACT FROM AUTHOR]

The tremendous progress in artificial intelligence promotes the wide application of machine learning (ML) technology in the field of electronic science. Recently ML-based antenna optimization provides a distinct candidate and attracts considerable attention. However, the large number of training samples generated through time-consuming EM simulations becomes a significant challenge. In this article, an efficient online data-driven enhanced-XGBoost (E-XGBoost) method for antenna optimization is proposed, which is mainly composed of two parts, i.e., an input variable filter module (IVFM) and an antenna optimization module (AOM). Specifically, IVFM serves as a variable sensitivity analyzer, which is accomplished by E-XGBoost to efficiently reduce the dimension of design variable and hence save the training samples. Next, the design variables obtained by IVFM are fed into AOM to find the near-optimal solution. In AOM, an online learning strategy is proposed to train a local E-XGBoost model to evaluate the population in the metaheuristic optimization algorithm (MOA). Compared to the global ML model that can mimic the entire design space, this local E-XGBoost model can further cut down the training samples. To verify the performance of the proposed method, several different antenna examples, i.e., U-slot patch antenna, Fabry–Perot resonant antenna, and dual-polarized cross dipole antenna and 5G MIMO antenna array, are simulated. Numerical results support the proposed method in terms of its superior performance and potential advantage of saving computational overhead. [ABSTRACT FROM AUTHOR]

In the last decade, fuzzy community detection has received increasing attention, since it cannot only uncover the community structure of a network, but also reflect the membership degrees of each node to multiple communities. Although some pioneers proposed a few algorithms for finding fuzzy communities, there is still room for further improvement in the quality of detected fuzzy communities. In this study, a metaheuristic-based modularity optimization algorithm, named symbiotic organisms search fuzzy community detection (SOSFCD) is proposed. On the one hand, an improved bioinspired metaheuristic algorithm, elite symbiotic organisms search, is designed as optimization strategy to improve the global convergence of fuzzy modularity optimization. On the other hand, a neighbor-based membership modification operation is proposed to intensify exploitation ability and speed up convergence, by efficiently utilizing local information (i.e., node neighborhood) of network topology. Experimental results on both of synthetic and real-life networks with different scales and characteristics show that SOSFCD can find max-modularity fuzzy partitions and coverings, and outperforms many state-of-the-art algorithms in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

In this article, two novel approaches for identifying the synchronous generator (SG) parameters are presented. The proposed approaches use an experimentally obtained armature current during the short-circuit test as input data. Both approaches are simple, cost-effective, and practical since they do not require any additional equipment for signal generation nor applying any of the advanced control techniques. The first approach relies on fitting the analytical total armature current waveform with the experimental one, while the second approach relies on matching the observed waveform with an analytical waveform of asymmetrical and alternating components. Instead of traditional graphical methods typically used in standardized procedures, this article also proposes a novel hybrid metaheuristic algorithm to identify the SG's parameters. The experimental verification of the proposed methods is carried out on 100 MVA SG at the Bajina Basta hydropower plant in Serbia. [ABSTRACT FROM AUTHOR]

High spectral dimensionality of hyperspectral image (HSI) has brought great redundancy for data processing. Band selection (BS), as one of the most commonly used dimension reduction (DR) techniques, attempts to remove the redundant spectral bands, while maintaining good classification or detection rate for later applications. Gray wolf optimizer (GWO) algorithm is a meta-heuristic algorithm, and it is used for HSI BS. However, the convergence factor of the basic GWO is linearly decreased, leading to a slower convergence speed and increasing the probability of falling into local optimality. This article proposes a new hybrid gray wolf optimizer (HGWO) algorithm for HSI BS, which uses adaptive decreasing convergence factor instead of linear convergence factor to improve GWO convergence rate and combines category separability for initialization to avoid local optimality. Five nonlinear functions are used to test the convergence of the proposed HGWO algorithm, compared with the state-of-the-art optimization algorithms. Finally, the experimentations are performed on three widely used real hyperspectral datasets for HSI classification, and the experimental results show that band subsets selected by the proposed HGWO algorithm can obtain better classification accuracy compared with other global optimization algorithms. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and coordinated multi-point (CoMP) are two fundamental techniques considered for the fifth generation (5 G) of wireless communications. In this paper, a hybrid satellite-unmanned aerial vehicle (UAV) relay network (HSURN) is proposed where the UAV relays (URs) employ CoMP transmission to serve the terrestrial users (UEs). Furthermore, all UEs associated with the CoMP-URs form a single NOMA cluster. For this model, an optimization problem is formulated subject to the minimum quality of services (QoSs) requirements of the UEs, transmission power budgets and, successive interference cancellation (SIC), to select URs and allocate their transmission powers for the energy efficiency (EE) maximization. With this insight, first, a computationally efficient sub-optimal UR selection scheme is proposed. Then, the powers are allocated to the selected URs via the Lagrange multipliers optimization (LMO) method. Due to the non-convex nature of the considered problem, it is relatively difficult to be solved. Hence, a metaheuristic teaching-learning-based optimization (TLBO) algorithm is employed to achieve an efficient solution. Simulation results are provided to verify the effectiveness of the proposed sub-optimal relay selection scheme and the TLBO-based power allocation method compared to the LMO conventional method. Besides, the obtained results also reveal that the CoMP-NOMA transmission in the proposed scenario significantly improves the spectral efficiency (SE) and outage probability (OP) of the system compared to non-comp NOMA transmission case. [ABSTRACT FROM AUTHOR]

Inspecting large-scale physical infrastructure assets such as powerlines from the air involves capturing data of thousands of features of an asset and its nearby surroundings, which makes inspection flight planning challenging. The objective of this article is to address the problems of automatically generating and optimizing flight paths and mission plans for airborne inspection of large linear infrastructure assets. This article proposes an automated feature-driven flight planning method using metaheuristic optimization with consideration of aircraft dynamic, sensor, terrain, and airfield location constraints. Results for flight planning the inspection of one of the world’s largest above-ground powerline networks are shown. Automated planning can reduce the human planning workload and enable the cost-saving benefits of automated aircraft to be realized through efficient deployment. Note to Practitioners—This work extends previous research and development of an automated aircraft guidance system for infrastructure inspection, by addressing the problem of automated flight planning. Often, deploying manned or automated aircraft reliably and efficiently for large-scale inspection is a challenge. Designing efficient flight paths and missions by hand is complex and time-consuming. Traditional “top-down” planning approaches use region or waypoint-based planning, neglecting the spatial layout of the assets to be inspected, possibly leading to more missions than required, inefficient flight paths, or missed data capture. Since inspecting a large asset with few aircraft can take many days, a solution was sought that can plan across multiple time scales (from second to second path planning to daily mission planning). A solution was designed that can build flight plans automatically from the “bottom-up” while optimizing performance objectives and satisfying constraints. Flight plans can either be used to aid pilots conducting an aerial inspection, as input to an automated line-tracking system for aircraft or UAVs or for estimating the cost of large-scale inspection operations. The presented method and results are motivated by the specific problem of large-scale powerline inspection and the approach may be applied to the inspection of other above-ground infrastructures, such as roads and railways. [ABSTRACT FROM AUTHOR]

A flexible, dual-broadband, and polarization-insensitive coding metasurface is proposed to manipulate electromagnetic (EM) scattering in microwave frequency band. The 1 bit coding units “0” and “1” are formed by the Pancharatnam–Berry (PB) phase based on the same-sized meta-atoms with different orientations. The layout of the coding metasurface is obtained through genetic algorithm (GA). The simulation results indicate that the proposed coding metasurface in this communication can achieve more than 10 dB radar cross section (RCS) reduction in the range of 9.26–12.87 and 14.84–19.35 GHz in the planar case, which is attributed to the reorientation of the reflected energies into different directions by optimizing the coding sequence. The diffuse scattering performance in the dual-wideband is well maintained, while the coding metasurface is conformal on metallic cylinder with diverse curvature radii. Moreover, the scattering characteristics of conformal metasurfaces become better with the decrease of curvature radius under the case of the certain size of the flexible metasurface. A flexible coding metasurface prototype is prepared and measured. The experiment results coincide with the numerical simulation ones, demonstrating the outstanding capacity of RCS reduction. The proposed design has potential application value in the field of antenna and stealth of more complex objects. [ABSTRACT FROM AUTHOR]

One of the most promising technologies to be employed in beyond 5G networks is based on the concept of cell-free (CF) massive MIMO, in which a predetermined set of access points (APs) jointly cooperate in the data transmission and reception to/from the user equipment (UE). In order to efficiently manage radio resources, the CF central processing unit implement uplink power control policies. These policies aim to optimize a given network utility function. In this paper, we investigate the max-min fairness optimization problem, in which the spectral efficiency performance of the UE with the worst channel conditions is prioritized, taking into account a per-UE power constraint and assuming linear maximum ratio combining at APs. Existing solutions have typically relied on second-order cone programming with convex approximations, which exhibit high computational complexity and scalability issues. Therefore, meta-heuristics (MHs) are explored as alternative optimization schemes, capable of providing (near)-optimal solutions with reasonable computational effort. Three MH approaches with different operation principles are compared. Numerical results show that the differential evolution algorithm exhibits the best trade-off between solution quality and run time, being able to reach (near)-optimal solutions faster than the bisection approach while coping with the scalability issues of the geometric programming-based algorithm. [ABSTRACT FROM AUTHOR]

This research presents an original and efficient elite archive-assisted adaptive memetic algorithm (EAMA) to deal with a realistic hybrid differentiation flowshop scheduling problem (HDFSP) with the objective of total completion time minimization. In this scheduling problem, each job consists of multiple parts and the jobs are divided into different types. The manufacturing of a job is comprised of three consecutive stages: 1) parts fabrication on first-stage parallel machines; 2) parts assembly on second-stage single machine; and 3) job differentiation on one of third-stage dedicated machines. We provide a mixed-integer programming model, derive three lower bounds, and further present the EAMA metaheuristic for HDFSP. The EAMA is initialized heuristically, and its global exploration is performed by a differential evolution, which includes three newly designed operators: 1) elite-driven discretized differential mutation; 2) probability crossover; and 3) biased selection. To enhance the local search, an external elite archive is set and evolved in parallel with global exploration by a meta-Lamarckian learning-based adaptive multistage local search and a variable length-based adaptive block-insertion local search. After the global exploration and local exploitation, an elite sharing strategy is used to exchange the excellent information between population and elite archive, and an adaptive restart strategy is used to diversify the population. The influence of parameter setting on EAMA is surveyed by using an improved design-of-experiment. The statistical results from extensive computational experiments demonstrate the effectiveness of the special designs and show that EAMA performs more efficient than the existing algorithms in solving the problem under consideration. [ABSTRACT FROM AUTHOR]

Reliability is an essential factor in distribution networkt expansion planning. However, standard distribution reliability assessment techniques rely on quantifying the impact of a pre-specified set of events on service continuity through the simulation of component outages, one at a time. Due to such a simulation-based nature, the incorporation of reliability into distribution network expansion planning has customarily required the application of heuristic and metaheuristic approaches. Recently, alternative mixed-integer linear programming (MILP) models have been proposed for distribution network expansion planning considering reliability. Nonetheless, such models suffer from either low computational efficiency or over-simplification. To overcome these shortcomings, this paper proposes an enhanced MILP model for multistage reliability-constrained distribution network expansion planning. Leveraging an efficient, yet accurate reliability evaluation model, proposing a customized technique for effectively imposing radial operation, as well as utilizing pragmatic measures to model reliability-related costs are the salient features of this work. In this respect, practical reliability-related costs are considered based on reliability incentive schemes and the revenue lost due to undelivered energy during customer outages. The proposed planning approach is tested on four networks with 24, 54, 86, and 138 nodes to illustrate its efficiency and applicability. [ABSTRACT FROM AUTHOR]

Improving the efficiency and sustainability of distribution networks (DNs) is nowadays a challenging objective both for large networks and microgrids connected to the main grid. In this context, a crucial role is played by the so-called network reconfiguration problem, which aims at determining the optimal DN topology. This process is enabled by properly changing the close/open status of all available branch switches to form an admissible graph connecting network buses. The reconfiguration problem is typically modeled as an NP-hard combinatorial problem with a complex search space due to current and voltage constraints. Even though several metaheuristic algorithms have been used to obtain—without guarantees—the global optimal solution, searching for near-optimal solutions in reasonable time is still a research challenge for the DN reconfiguration problem. Facing this issue, this article proposes a novel effective optimization framework for the reconfiguration problem of modern DNs. The objective of reconfiguration is minimizing the overall power losses while ensuring an enhanced DN voltage profile. A multiple-step resolution procedure is then presented, where the recent Harris hawks optimization (HHO) algorithm constitutes the core part. This optimizer is here intelligently accompanied by appropriate preprocessing (i.e., search space preparation and initial feasible population generation) and postprocessing (i.e., solution refinement) phases aimed at improving the search for near-optimal configurations. The effectiveness of the method is validated through numerical experiments on the IEEE 33-bus, the IEEE 85-bus systems, and an artificial 295-bus system under distributed generation and load variation. Finally, the performance of the proposed HHO-based approach is compared with two related metaheuristic techniques, namely the particle swarm optimization algorithm and the Cuckoo search algorithm. The results show that HHO outperforms the other two optimizers in terms of minimized power losses, enhanced voltage profile, and running time. Note to Practitioners—This article is motivated by the emerging need for effective network reconfiguration approaches in modern power distribution systems, including microgrids. The proposed metaheuristic optimization strategy allows the decision maker (i.e., the distribution system operator) to determine in reasonable time the optimal network topology, minimizing the overall power losses and considering the system operational requirements. The proposed optimization framework is generic and flexible, as it can be applied to different architectures both of large distribution networks (DNs) and microgrids, considering various types of system objectives and technical constraints. The presented strategy can be implemented in any decision support system or engineering software for power grids, providing decision makers with an effective information and communication technology tool for the optimal planning of the energy efficiency and environmental sustainability of DNs. [ABSTRACT FROM AUTHOR]

The task assignment problem (TAP) is concerned with assigning a set of tasks to a set of agents subject to the limited processing and memory capacities of each agent. The objective to be minimized is the total assignment cost and total communication cost. TAP is a relevant model for many practical applications, yet solving the problem is computationally challenging. Most of the current metaheuristic algorithms for TAP adopt population-based search frameworks, whose search behaviors are usually difficult to analyze and understand due to their complex features. In this work, unlike previous population-based solution methods, we concentrate on a single trajectory stochastic local search model to solve TAP. Especially, we consider TAP from the perspective of a grouping problem and introduce the first probability learning-based local search algorithm for the problem. The proposed algorithm relies on a dual probability learning procedure to discover promising search regions and a gain-based neighborhood search procedure to intensively exploit a given search region. We perform extensive computational experiments on a set of 180 benchmark instances with the proposed algorithm and the general mixed integer programming solver CPLEX. We assess the composing ingredients of the proposed algorithm to shed light on their impacts on the performance of the algorithm. Note to Practitioners—This work is motivated by the problem of program modules designing and task allocation in parallel and distribution systems. It can also be applied to deal with job (task) grouping problems in practical industrial applications. This article presents a novel and effective learning-based local search algorithm to obtain high-quality solutions for the considered problem. The results of numerical experiments and comparisons show that our algorithm can achieve good search performances on problem instances of different scales and difficulties. Afterward, we use the proposed solution method to solve a real-life open-order slab assignment problem, which is derived from the production planning of silicon steel for an iron and steel company. The learning techniques of the proposed algorithm are of general interest and can be used in search algorithms for solving other real-life optimization problems with grouping features. For future research, we will design solution methods based on these learning techniques to address other practical optimization problems. [ABSTRACT FROM AUTHOR]

Optical Wireless Communication (OWC) is being explored for application in the next-generation broadcasting networks, where possessing accurately determined user locations becomes increasingly important. Received signal strength (RSS) Visible Light Positioning (VLP)-based localisation systems aim to deliver these centimetre-level location data at a low cost by featuring but a single photodiode (PD). Maximising the VLP accuracy requires optimising the LED transmitter locations, which is missing currently. An evolutionary optimisation algorithm is proposed to determine the optimal LED locations and the associated positioning error values for various configurations. The sensitivity of the planning on the number of VLP-enabled LEDs, the LEDs’ characteristics, the room dimensions and the positioning parameters is investigated. Experimental data, i.e., two datasets with 1572 measurement points each, serve to validate the simulations. [ABSTRACT FROM AUTHOR]

A novel metaheuristic algorithm for global optimization, called the Solar System Algorithm (SSA), is presented. The proposed algorithm imitates the orbiting behavior of some objects found in the solar system: i.e., Sun, planets, moons, stars, and black holes. SSA is used to solve five well-known engineering design problems: three-bar truss, pressure vessel, tension/compression spring, welded beam, and gear train. The obtained results are compared to 16 state-of-the-art metaheuristic algorithms. They show that SSA is very competitive in solving the considered engineering problems. In addition, the performance of SSA is evaluated on the benchmarks CEC 2014 and CEC 2020. The experimental results are compared to 27 (12 for CEC 2014 and 15 for CEC 2020) metaheuristic algorithms. They demonstrate that SSA is very promising in finding efficient solutions.

Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

When subjected to partial shading (PS), photovoltaic (PV) arrays suffer from the significantly reduced output. Although the incorporation of bypass diodes at the output alleviates the effect of PS, such modification results in multiple peaks of output power. Conventional algorithms—such as perturb and observe (P&O) and hill-climbing (HC)—are not suitable to be employed to track the optimal peak due to their convergence to local maxima. To address this issue, various artificial intelligence (AI) based algorithms—such as an artificial neural network (ANN) and fuzzy logic control (FLC)—have been employed to track the maximum power point (MPP). Although these algorithms provide satisfactory results under PS conditions, a very large amount of data is required for their training process, thereby imposing an excessive burden on processor memory. Consequently, this paper proposes a novel optimization algorithm based on stochastic search (random exploration of search space), known as the adaptive jaya (Ajaya) algorithm in which two adaptive coefficients are incorporated for maximum power point tracking (MPPT) with a rapid convergence rate, fewer power fluctuations and high stability. The algorithm successfully eliminates the issues associated with existing conventional and AI-based algorithms. Moreover, the proposed algorithm outperforms other state-of-the-art stochastic search-based techniques in terms of fewer fluctuations, robustness, simplicity, and faster convergence to the optima. Extensive analysis of results obtained from MATLAB® is done to prove the above performance parameters under static insolation conditions (using a three, four and a five-module series-connected PV system), under dynamically varying insolation (using a four-module series connected system), by changing the PV module rating (using a four-module series connected system) and using an IEC standard.

This study integrates a tunicate swarm algorithm (TSA) with a local escaping operator (LEO) for overcoming the weaknesses of the original TSA. The LEO strategy in TSA–LEO prevents searching deflation in TSA and improves the convergence rate and local search efficiency of swarm agents. The efficiency of the proposed TSA–LEO was verified on the CEC’2017 test suite, and its performance was compared with seven metaheuristic algorithms (MAs). The comparisons revealed that LEO significantly helps TSA by improving the quality of its solutions and accelerating the convergence rate. TSA–LEO was further tested on a real-world problem, namely, segmentation based on the objective functions of Otsu and Kapur. A set of well-known evaluation metrics was used to validate the performance and segmentation quality of the proposed TSA–LEO. The proposed TSA-LEO outperforms other MA algorithms in terms of fitness, peak signal-to-noise ratio, structural similarity, feature similarity, and segmentation findings.

Feature selection plays a crucial role in order to mitigate the high dimensional feature space in different classification problems. The computational cost is reduced, and the accuracy of the classification is improved by reducing the dimension of feature space. Hence, in the classification task, finding the optimal subset of features is of utmost importance. Metaheuristic techniques have proved their efficacy in solving many real-world optimization issues. One of the recently introduced physics-inspired optimization methods is Archimedes Optimization Algorithm (AOA). This paper proposes an Enhanced Archimedes Optimization Algorithm (EAOA) by adding a new parameter that depends on the step length of each individual while revising the individual location. The EAOA algorithm is proposed to improve the AOA exploration and exploitation balance and enhance the classification performance for the feature selection issue in real-world data sets. Experiments were performed on twenty-three standard benchmark functions and sixteen real-world data sets to investigate the performance of the proposed EAOA algorithm. The experimental results based on the standard benchmark functions show that the EAOA algorithm provides very competitive results compared to the basic AOA algorithm and five well-known optimization algorithms in terms of improved exploitation, exploration, local optima avoidance, and convergence rate. In addition, the results based on sixteen real-world data sets ascertain that reduced feature subset yields higher classification performance when compared with the other feature selection methods.

In this paper, we study a novel risk measure, which is a copula-based extension of tail value-at-risk (TVaR). This measure is called dependent tail value-at-risk (DTVaR), which is a generalization of TVaR. Moreover, we describe a second conditional tail moment of the tail distribution with the center being the DTVaR itself, which is called the dependent conditional tail variance (DCTV). Both DTVaR and DCTV contain two contraction parameters, which make them much more flexible than some of the more familiar measures of risk, such as TVaR and conditional tail variance (CTV). We derive analytical formulas of the DTVaR and DCTV for exponential risk associated with another risk where their dependence structure is represented by Farlie-Gumbel-Morgenstern (FGM) copula. This paper proposes an optimization method for DTVaR by applying two metaheuristic algorithms: spiral optimization (SpO) and particle swarm optimization (PSO). Furthermore, we perform SpO and PSO by utilizing DCTV and CTV to estimate two contraction parameters that maximize DTVaR. This work presents an application of DTVaR optimization in predicting the DTVaR of energy risk of New York Harbor (NYH) gasoline associated with energy risk of West Texas Intermediate (WTI) crude oil. We find that the values of the objective function using both algorithms converge to zero, which implies that the SpO and PSO algorithms are very suitable for application to DTVaR optimization. However, according to the values of the objective function, we find that the PSO algorithm is more suitable than the SpO algorithm in optimizing DTVaR.

In the era of advanced nanotechnology where billions of transistors are fabricated in a single chip, high-speed operations are challenging due to packaging related issues. In High-Speed Very Large Scale Integration (VLSI) systems, decoupling capacitors are essentially used in power delivery networks to reduce power supply noise and to maintain a low impedance of the power delivery networks. In this paper, the cumulative impedance of a power delivery network is reduced below the target impedance by using state-of-the-art metaheuristic algorithms to choose and place decoupling capacitors optimally. A Matrix-based Evolutionary Computing (MEC) approach is used for efficient usage of metaheuristic algorithms. Two case studies are presented on a practical system to demonstrate the proposed approach. A comparative analysis of the performance of state-of-the-art metaheuristics is presented with the insights of practical implementation. The consistency of results in both the case studies confirms the validity of the proposed appraoch.