Metaheuristic-based Algorithms Research Articles

Image-based severity analysis of Asphalt pavement bleeding using a metaheuristic-boosted fuzzy classifier

Pavement management systems play a vital role in maintaining transportation infrastructures by evaluating pavement distress to perform maintenance tasks efficiently. Severity analysis is an important step in this process. With an increasing focus on automating the pavement distress inspection, challenges persist, including limited attention to severity analysis of texture-based distresses and lack of applying fuzzy systems in this analysis despite the linguistic and qualitative description of severity levels. Accordingly, this paper presents a methodology leveraging computational intelligence frameworks such as fuzzy logic and metaheuristic optimization to develop a reliable system for severity analysis, particularly focusing on asphalt pavement bleeding. Employing GLSM and statistical feature extraction in conjunction with a fuzzy classifier, optimized with metaheuristic-based algorithms like GA, HBA, GWA, ARA, and SSA, the proposed boosted fuzzy classifier achieves an impressive accuracy of 93% and notable improvements in performance metrics, underscoring its superiority over classic fuzzy classifiers.

Systematic Review: Particle Swarm Optimization (PSO) based Load Balancing for Cloud Computing

In this modern era cloud computing and widespread use of it have led to a massive spike in users' requests. The infrastructure's resources would either not be able to handle the increased volume of traffic or the scenario would lead to some resources being over or underutilized. Due to the unpredictable spike in traffic the system’s performance eventually grades. An optimal load distribution is therefore the area of concern for controlling traffic so that all available resources are fully used if possible in order to increase system efficiency. Static, heuristics-based, traditional dynamic and metaheuristic-based algorithms are ways that have been used for effective load balancing. The issue is challenging since there isn't a single best fit approach. The objective of this systematic review is to study the utilization of Particle Swarm Optimization method along with its proposed variations to distribute the incoming traffic evenly with efficient resource utilization.

A metaheuristic-based algorithm for optimizing node deployment in wireless sensor network

A metaheuristic-based algorithm for optimizing node deployment in wireless sensor network

AdaL-PSO A New Adaptive Algorithm for the Multi-Skilled Resource-Constrained Project Scheduling Problem

MS-RCPSP is a combinatorial optimization problem that has many practical applications, this problem has been proven to belong to the NP-hard class, the approach to solving this problem is to use algorithms to find approximate solution. This paper proposed a New Adaptive Local Particle Swarm Optimization algorithm for the MS-RCPSP problem. The solution for the class of NP-Hard problems is to find approximate solutions using metaheuristic algorithms. However, most metaheuristic-based algorithms have a weakness that can be fallen into local extreme after a number of evolution generations. In this paper, we adopted a new adaptive nonlinear weight update strategy based on fitness value and new neighborhood topology for Particle Swarm Optimization algorithm, thereby helping to prevent PSO from falling into local extremes. The new algorithm is called AdaL-PSO. A numerical analysis is carried out using iMOPSE benchmark dataset and is compared with some other early algorithms. Results presented suggest the prospect of our proposed algorithm.

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Optimum Cost Prediction of Reinforced Concrete Cantilever Retaining Walls

Reinforced concrete cantilever retaining walls (RCCRWs) are widely used in civil engineering projects as a common type of retaining structure. The design of these structures focuses on ensuring safety against various failure scenarios and compliance with standard building code requirements. This research aims to enhance the design process of RCCRWs by developing a specific code and optimizing it through a metaheuristic-based algorithm. In this study, the cost prediction of RCCRWs is also investigated through a parametric study involving key variables such as wall height, seismic zone, backfill material properties, and backfill inclination angle. To achieve this, non-linear regression analysis is employed to establish an empirical correlation, enabling cost estimation for optimized RCCRWs. The resulting prediction equation is simple to use, requiring only limited inputs. Therefore, it can be applied during the initial stages of a project, making a valuable contribution in determining approximate costs for RCCRW projects.

Metaheuristic-Based Algorithms for Optimizing Fractional-Order Controllers—A Recent, Systematic, and Comprehensive Review

Metaheuristic optimization algorithms (MHA) play a significant role in obtaining the best (optimal) values of the system’s parameters to improve its performance. This role is significantly apparent when dealing with systems where the classical analytical methods fail. Fractional-order (FO) systems have not yet shown an easy procedure to deal with the determination of their optimal parameters through traditional methods. In this paper, a recent, systematic. And comprehensive review is presented to highlight the role of MHA in obtaining the best set of gains and orders for FO controllers. The systematic review starts by exploring the most relevant publications related to the MHA and the FO controllers. The study is focused on the most popular controllers such as the FO-PI, FO-PID, FO Type-1 fuzzy-PID, and FO Type-2 fuzzy-PID. The time domain is restricted in the articles published through the last decade (2014:2023) in the most reputed databases such as Scopus, Web of Science, Science Direct, and Google Scholar. The identified number of papers, from the entire databases, has reached 850 articles. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was applied to the initial set of articles to be screened and filtered to end up with a final list that contains 82 articles. Then, a thorough and comprehensive study was applied to the final list. The results showed that Particle Swarm Optimization (PSO) is the most attractive optimizer to the researchers to be used in the optimal parameters identification of the FO controllers as it attains about 25% of the published papers. In addition, the papers that used PSO as an optimizer have gained a high citation number despite the fact that the Chaotic Atom Search Optimization (ChASO) is the highest one, but it is used only once. Furthermore, the Integral of the Time-Weighted Absolute Error (ITAE) is the best nominated cost function. Based on our comprehensive literature review, this appears to be the first review paper that systematically and comprehensively addresses the optimization of the parameters of the fractional-order PI, PID, Type-1, and Type-2 fuzzy controllers with the use of MHAs. Therefore, the work in this paper can be used as a guide for researchers who are interested in working in this field.

An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system

Conventionally, researchers have favored the model-based control scheme for controlling gantry crane systems. However, this method necessitates a substantial investment of time and resources in order to develop an accurate mathematical model of the complex crane system. Recognizing this challenge, the current paper introduces a novel data-driven control scheme that relies exclusively on input and output data. Undertaking a couple of modifications to the conventional marine predators algorithm (MPA), random average marine predators algorithm (RAMPA) with tunable adaptive coefficient to control the step size ( CF) has been proposed in this paper as an enhanced alternative towards fine-tuning data-driven multiple-node hormone regulation neuroendocrine-PID (MnHR-NEPID) controller parameters for the multi-input–multi-output (MIMO) gantry crane system. First modification involved a random average location calculation within the algorithm’s updating mechanism to solve the local optima issue. The second modification then introduced tunable CF that enhanced search capacity by enabling users’ resilience towards attaining an offsetting level of exploration and exploitation phases. Effectiveness of the proposed method is evaluated based on the convergence curve and statistical analysis of the fitness function, the total norms of error and input, Wilcoxon’s rank test, time response analysis, and robustness analysis under the influence of external disturbance. Comparative findings alongside other existing metaheuristic-based algorithms confirmed excellence of the proposed method through its superior performance against the conventional MPA, particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), salp-swarm algorithm (SSA), slime mould algorithm (SMA), flow direction algorithm (FDA), and the formally published adaptive safe experimentation dynamics (ASED)-based methods.

Sand cat swarm optimization-based feedback controller design for nonlinear systems

The control of the open loop unstable systems with nonlinear structure is challenging work. In this paper, for the first time, we present a sand cat swarm optimization (SCSO) algorithm-based state feedback controller design for open-loop unstable systems. The SCSO algorithm is a newly proposed metaheuristic algorithm with an easy-to-implement structure that can efficiently find the optimal solution for optimization problems. The proposed SCSO-based state feedback controller can successfully optimize the control parameters with efficient convergence curve speed. In order to show the performance of the proposed method, three different nonlinear control systems such as an Inverted pendulum, a Furuta pendulum, and an Acrobat robot arm are considered. The control and optimization performances of the proposed SCSO algorithm are compared with well-known metaheuristic algorithms. The simulation results show that the proposed control method can either outperform the compared metaheuristic-based algorithms or have competitive results.

Availability Capacity Evaluation and Reliability Assessment of Integrated Systems Using Metaheuristic Algorithm

Contemporarily, the development of distributed generations (DGs) technologies is fetching more, and their deployment in power systems is becoming broad and diverse. Consequently, several glitches are found in the recent studies due to the inappropriate/inadequate penetrations. This work aims to improve the reliable operation of the power system employing reliability indices using a metaheuristic-based algorithm before and after DGs penetration with feeder system. The assessment procedure is carried out using MATLAB software and Modified Salp Swarm Algorithm (MSSA) that helps assess the Reliability indices of the proposed integrated IEEE RTS79 system for seven different configurations. This algorithm modifies two control parameters of the actual SSA algorithm and offers a perfect balance between the exploration and exploitation. Further, the effectiveness of the proposed schemes is assessed using various reliability indices. Also, the available capacity of the extended system is computed for the best configuration of the considered system. The results confirm the level of reliable operation of the extended DGs along with the standard RTS system. Specifically, the overall reliability of the system displays superior performance when the tie lines 1 and 2 of the DG connected with buses 9 and 10, respectively. The reliability indices of this case namely SAIFI, SAIDI, CAIDI, ASAI, AUSI, EUE, and AEUE shows enhancement about 12.5%, 4.32%, 7.28%, 1.09%, 4.53%, 12.00%, and 0.19%, respectively. Also, a probability of available capacity at the low voltage bus side is accomplished a good scale about 212.07 times/year.

A survey on multi-objective hyperparameter optimization algorithms for machine learning

Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed to perform HPO; most of these are focused on optimizing one performance measure (usually an error-based measure), and the literature on such single-objective HPO problems is vast. Recently, though, algorithms have appeared that focus on optimizing multiple conflicting objectives simultaneously. This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms, distinguishing between metaheuristic-based algorithms, metamodel-based algorithms and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.

Metaheuristic based Task Scheduling for Load Balancing in the Cloud Computing Environment

Cloud computing has emerged as a leading technology that handles allocation of tasks in a dynamic manner. Task Scheduling highly contributes to load balancing, and scheduling tasks much adheres to the Service Level Agreement (SLA) requirements, which is a document offered by cloud service providers to the users. Load balancing serves as one of the key concepts of cloud computing that avoids the hectic situation where certain nodes are overloaded while the others are having a very smaller number of tasks and certain not at all loaded. The performance of QoS metrics such as makespan, throughput, performance, response time etc. are having some amount of impact to the degree of imbalance. Cat Swarm Optimization (CSO) and Spider Monkey Optimization (SMO) metaheuristic algorithms are integrated and CSO-SMO-LB has been proposed in Cloud Computing inorder to balance the scheduling of tasks to the available resources. This proposed metaheuristic algorithm aims to optimize resources and improve Load Balancing in terms of the Quality of Service (QoS) task parameters, the priority of Virtual Machine (VM), makespan and resource utilization. The performance of metaheuristic-based algorithms, are analysed by performing simulation and better results were produced.

Normalized Mutual Information-based equilibrium optimizer with chaotic maps for wrapper-filter feature selection

The problem of feature selection involves selecting the most informative subset of features from a data item which have the most impact in the context of classification. It is an optimization problem that requires discarding irrelevant and redundant data without compromising the classification accuracy rate of the learning algorithm. Due to the complexity of the problem, various stochastic methods have been employed to generate promising results. In this work, we propose Normalized Mutual Information-based equilibrium optimizer (NMIEO), a novel variant of the classical Equilibrium Optimizer as a wrapper-filter framework for feature selection. NMIEO incorporates a novel local search strategy based on Normalized Mutual Information to enhance the exploitation capability. Additionally, for improving the diversity of the solutions, Chaotic maps are utilized for better population initialization. The proposed method is evaluated on 14 challenging high-dimensional datasets selected from diverse domains. The results are tallied against eight well-known metaheuristic-based algorithms proposed recently in the literature for solving the feature selection problem. The experimental results and further analysis illustrate the superior performance of the proposed NMIEO relative to other wrapper-based algorithms that are currently in use.Furthermore, the Wilcoxon’s Signed Rank Test demonstrates the statistical superiority of NMIEO over other competitive methods across all selected metrics, confirming the success of the proposed method.

Feature Selection by Hybrid Brain Storm Optimization Algorithm for COVID-19 Classification.

A large number of features lead to very high-dimensional data. The feature selection method reduces the dimension of data, increases the performance of prediction, and reduces the computation time. Feature selection is the process of selecting the optimal set of input features from a given data set in order to reduce the noise in data and keep the relevant features. The optimal feature subset contains all useful and relevant features and excludes any irrelevant feature that allows machine learning models to understand better and differentiate efficiently the patterns in data sets. In this article, we propose a binary hybrid metaheuristic-based algorithm for selecting the optimal feature subset. Concretely, the brain storm optimization algorithm is hybridized by the firefly algorithm and adopted as a wrapper method for feature selection problems on classification data sets. The proposed algorithm is evaluated on 21 data sets and compared with 11 metaheuristic algorithms. In addition, the proposed method is adopted for the coronavirus disease data set. The obtained experimental results substantiate the robustness of the proposed hybrid algorithm. It efficiently reduces and selects the feature subset and at the same time results in higher classification accuracy than other methods in the literature.

An Artificial Intelligence Approach for Solving Stochastic Transportation Problems

Recent years witness a great deal of interest in artificial intelligence (AI) tools in the area of optimization. AI has developed a large number of tools to solve the most difficult search-and-optimization problems in computer science and operations research. Indeed, metaheuristic-based algorithms are a sub-field of AI. This study presents the use of the metaheuristic algorithm, that is, water cycle algorithm (WCA), in the transportation problem. A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull distribution. Since the parameters are stochastic, the corresponding constraints are probabilistic. They are converted into deterministic constraints using the stochastic programming approach. In this study, we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization problems. WCA is influenced by the water cycle process of how streams and rivers flow toward the sea (optimal solution). WCA is applied to the stochastic transportation problem, and obtained results are compared with that of the new metaheuristic optimization algorithm, namely the neural network algorithm which is inspired by the biological nervous system. It is concluded that WCA presents better results when compared with the neural network algorithm.

Parameter identification of thermal model of vanadium redox batteries by metaheuristic algorithms

Typically, the battery stack temperature assumes constant in electrochemical or equivalent circuit models of the battery. However, experiments on a Vanadium Redox Flow Battery (VRFB) unit show that the temperature varies significantly during charge and discharge processes and considerably affects the value of the State of Charge (SoC) and other battery internal parameters. Therefore, monitoring and modeling the battery temperature is essential. An electrochemical based thermal model of VRFBs is adapted to the experimental data of a nine-cell VRFB unit in this study to find the optimal values of the coefficients and parameters of the VRFB's electrochemical and thermal models. The electrochemical model of VRFB and its thermal model include many unknown parameters and coefficients that need to be estimated. The objective of the current study is to determine the optimal value of all of these unknown parameters. According to the literature, the empirical methods are not sufficient to accurately determine the model's coefficients and parameters. An Optimization framework is defined to identify these coefficients by minimizing the mean square error between the measured experimental data of VRFB and the electrochemical model-based terminal voltage and electrolyte temperature. Moreover, a VRFB cell's electrochemical and thermal models are highly nonlinear; thus, excellent optimization techniques are needed to find these coefficients and parameters. A few metaheuristic optimizers are tested on the proposed optimization framework, where three of these algorithms have shown consistent and able to converge to reliable solutions. These algorithms are Genetic Algorithm (GA), Particle Swarm Optimizer (PSO), and Ant Lion Optimizer (ALO). The low RMS errors of the estimations by the metaheuristic-based algorithms compared to the measured data, show the accuracy of the proposed parameter identification approaches of VRFB's electrochemical and thermal model.

Improving the performance of hierarchical wireless sensor networks using the metaheuristic algorithms: efficient cluster head selection

Purpose Efficient resource utilization in wireless sensor networks is an important issue. Clustering structure has an important effect on the efficient use of energy, which is one of the most critical resources. However, it is extremely vital to choose efficient and suitable cluster head (CH) elements in these structures to harness their benefits. Selecting appropriate CHs and finding optimal coefficients for each parameter of a relevant fitness function in CHs election is a non-deterministic polynomial-time (NP-hard) problem that requires additional processing. Therefore, the purpose of this paper is to propose efficient solutions to achieve the main goal by addressing the related issues. Design/methodology/approach This paper draws inspiration from three metaheuristic-based algorithms; gray wolf optimizer (GWO), incremental GWO and expanded GWO. These methods perform various complex processes very efficiently and much faster. They consist of cluster setup and data transmission phases. The first phase focuses on clusters formation and CHs election, and the second phase tries to find routes for data transmission. The CH selection is obtained using a new fitness function. This function focuses on four parameters, i.e. energy of each node, energy of its neighbors, number of neighbors and its distance from the base station. Findings The results obtained from the proposed methods have been compared with HEEL, EESTDC, iABC and NR-LEACH algorithms and are found to be successful using various analysis parameters. Particularly, I-HEELEx-GWO method has provided the best results. Originality/value This paper proposes three new methods to elect optimal CH that prolong the networks lifetime, save energy, improve overhead along with packet delivery ratio.

Website Quality Analytics Using Metaheuristic Based Optimization

Background:: Studies are indicative of the fact that high-quality websites get better rankings on the search engines. A good website is the one which provides reliable content, has good design and user interface and can address global audience. But the end-users struggle with the predicament of selecting qualitative websites. Although “Quality” is fairly a subjective term, there is an obvious need for a useful and valid model which evaluates the quality attributes of a website. “A Website quality model essentially consists of a set of criteria used to determine if a website reaches certain levels of fineness”. Objective:: The quality of a website must be assured in terms of technicality, the accuracy of information, response time, design of website, ease of use, and many more. The aim is to identify features of a website that determines its quality and build an automatic website quality prediction model. Method:: We conduct an empirical study on 700 websites and run 6 baseline classifiers to categorize websites into good, average and poor using quality attributes. Subsequently, metaheuristic-based algorithms (Particle Swarm Optimization, Elephant Search Algorithm and Wolf Search Algorithm) for optimal feature selection have been implemented to get an optimal subset of quality attributes that is able to predict the quality of websites more accurately. Result:: The study confirms that the proposed implementation of metaheuristics for feature selection in website quality classification improves the performance of the supervised learning algorithms. An average 12.74% improvement in accuracy was observed using the features selected by Particle Swarm Optimization, 5.56% average improvement in accuracy using Elephant Search Algorithm for feature selection while an average improvement of 5.77% was observed using Wolf Search Algorithm for feature selection. Conclusion:: The study validates that Particle Swarm Optimization for feature selection in website quality analytics task outperforms Wolf Search Algorithm and Elephant Search Algorithm.

Adapted-RRT: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithms

Adapted-RRT: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithms

Single-based and Population-Based Metaheuristics Algorithms Performances in Solving NP-hard Problems

Metaheuristics is one of the most well-known field of researches uses to find optimum solution for Non-deterministic polynomial hard problems (NP-Hard), that are difficult to find an optimal solution in a polynomial time. Over time many algorithms have been developed based on the heuristics to solve difficult real-life problems, this paper will introduce Metaheuristic-based algorithms and its classifications, Non-deterministic polynomial hard problems. It also will compare the performance two metaheuristic-based algorithms (Elephant Herding optimization algorithm and Tabu Search) to solve Traveling Salesman Problem (TSP), which is one of the most known problem belongs to Non-deterministic polynomial hard problem and widely used in the performance evaluations for different metaheuristics-based optimization algorithms. the experimental results of the paper compare the results of EHO and TS for solving 10 different problems from the TSPLIB95.