Showing papers on "Crossover published in 2020"
TL;DR: The experimental results show that VS-CCPSO has the capability of obtaining good feature subsets, suggesting its competitiveness for tackling FS problems with high dimensionality.
Abstract: Evolutionary feature selection (FS) methods face the challenge of “curse of dimensionality” when dealing with high-dimensional data. Focusing on this challenge, this article studies a variable-size cooperative coevolutionary particle swarm optimization algorithm (VS-CCPSO) for FS. The proposed algorithm employs the idea of “divide and conquer” in cooperative coevolutionary approach, but several new developed problem-guided operators/strategies make it more suitable for FS problems. First, a space division strategy based on the feature importance is presented, which can classify relevant features into the same subspace with a low computational cost. Following that, an adaptive adjustment mechanism of subswarm size is developed to maintain an appropriate size for each subswarm, with the purpose of saving computational cost on evaluating particles. Moreover, a particle deletion strategy based on fitness-guided binary clustering, and a particle generation strategy based on feature importance and crossover both are designed to ensure the quality of particles in the subswarms. We apply VS-CCPSO to 12 typical datasets and compare it with six state-of-the-art methods. The experimental results show that VS-CCPSO has the capability of obtaining good feature subsets, suggesting its competitiveness for tackling FS problems with high dimensionality.
193 citations
170 citations
TL;DR: Enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI, and experimental results indicate that the NS GA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.
149 citations
TL;DR: Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs and the effect of adaptive crossover and mutation probabilities on the optimal solution.
Abstract: In this paper, we study an electric vehicle routing problem while considering the constraints on battery life and battery swapping stations. We first introduce a comprehensive model consisting of speed, load and distance to measure the energy consumption and carbon emissions of electric vehicles. Second, we propose a mixed integer programming model to minimize the total costs related to electric vehicle energy consumption and travel time. To solve this model efficiently, we develop an adaptive genetic algorithm based on hill climbing optimization and neighborhood search. The crossover and mutation probabilities are designed to adaptively adjust with the change of population fitness. The hill climbing search is used to enhance the local search ability of the algorithm. In order to satisfy the constraints of battery life and battery swapping stations, the neighborhood search strategy is applied to obtain the final optimal feasible solution. Finally, we conduct numerical experiments to test the performance of the algorithm. Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs. Moreover, we demonstrate the effect of adaptive crossover and mutation probabilities on the optimal solution.
121 citations
TL;DR: The results show that the proposed self-adaptive discrete particle swarm optimization algorithm using the genetic algorithm (GA) operators can effectively reduce the system cost of off-loading for DNN-based applications over the cloud, edge and end devices relative to the benchmarks.
Abstract: Currently, deep neural networks (DNNs) have achieved a great success in various applications. Traditional deployment for DNNs in the cloud may incur a prohibitively serious delay in transferring input data from the end devices to the cloud. To address this problem, the hybrid computing environments, consisting of the cloud, edge, and end devices, are adopted to offload DNN layers by combining the larger layers (more amount of data) in the cloud and the smaller layers (less amount of data) at the edge and end devices. A key issue in hybrid computing environments is how to minimize the system cost while accomplishing the offloaded layers with their deadline constraints. In this article, a self-adaptive discrete particle swarm optimization (PSO) algorithm using the genetic algorithm (GA) operators is proposed to reduce the system cost caused by data transmission and layer execution. This approach considers the characteristics of DNNs partitioning and layers off-loading over the cloud, edge, and end devices. The mutation operator and crossover operator of GA are adopted to avert the premature convergence of PSO, which distinctly reduces the system cost through enhanced population diversity of PSO. The proposed off-loading strategy is compared with benchmark solutions, and the results show that our strategy can effectively reduce the system cost of off-loading for DNN-based applications over the cloud, edge and end devices relative to the benchmarks.
115 citations
TL;DR: Results and analysis show that SVM-based feature selection technique with the proposed binary GWO optimizer with elite-based crossover scheme has enhanced efficacy in dealing with Arabic text classification problems compared to other peers.
Abstract: Text classification is one of the challenging computational tasks in machine learning community due to the increased amounts of natural language text documents available in the electronic forms. In this process, feature selection (FS) is an essential phase because thousands of possible feature sets may be considered in text classification. This paper proposes an enhanced binary grey wolf optimizer (GWO) within a wrapper FS approach to tackle Arabic text classification problems. The proposed binary GWO is utilized to play the role of a wrapper-based feature selection technique. The performance of the proposed method using different learning models, including decision trees, K-nearest neighbour, Naive Bayes, and SVM classifiers, are investigated. Three Arabic public datasets, namely Alwatan, Akhbar-Alkhaleej, and Al-jazeera-News, are utilized to evaluate the efficacy of different BGWO-based wrapper methods. Results and analysis show that SVM-based feature selection technique with the proposed binary GWO optimizer with elite-based crossover scheme has enhanced efficacy in dealing with Arabic text classification problems compared to other peers.
113 citations
TL;DR: The 2DNLCML system contains good features such as ergodic pseudo-random sequence, less periodic windows in bifurcations and larger range of parameters in chaotic dynamics, which is more suitable for image encryption.
111 citations
TL;DR: The proposed BPR system, based on statistical dependencies between behaviors and respective signal data, has been used to extract statistical features along with acoustic signal features like zero crossing rate to maximize the possibility of getting optimal feature values.
Abstract: Human behavior pattern recognition (BPR) from accelerometer signals is a challenging problem due to variations in signal durations of different behaviors. Analysis of human behaviors provides in depth observations of subject’s routines, energy consumption and muscular stress. Such observations hold key importance for the athletes and physically ailing humans, who are highly sensitive to even minor injuries. A novel idea having variant of genetic algorithm is proposed in this paper to solve complex feature selection and classification problems using sensor data. The proposed BPR system, based on statistical dependencies between behaviors and respective signal data, has been used to extract statistical features along with acoustic signal features like zero crossing rate to maximize the possibility of getting optimal feature values. Then, reweighting of features is introduced in a feature selection phase to facilitate the segregation of behaviors. These reweighted features are further processed by biological operations of crossover and mutation to adapt varying signal patterns for significant accuracy results. Experiments on wearable sensors benchmark datasets HMP, WISDM and self-annotated IMSB datasets have been demonstrated to testify the efficacy of the proposed work over state-of-the-art methods.
103 citations
TL;DR: The experimental results show that the proposed CCNMHHO method not only is very competitive in extracting the unknown parameters of different PV models compared to other state-of-the-art algorithms but also perform well in dealing with the complex outdoor environments such as different temperature and radiance.
98 citations
TL;DR: The proposed Quantum Whale Optimization Algorithm for feature selection enhances the exploratory and exploitation power of the classical WOA, with the use of quantum bit representation of the individuals of the population and the quantum rotation gate operator as a variation operator.
96 citations
01 Jan 2020
TL;DR: This chapter presents the most fundamental concepts, operators, and mathematical models of this algorithm, which mimics Darwinian theory of survival of the fittest in nature.
Abstract: Genetic Algorithm (GA) is one of the most well-regarded evolutionary algorithms in the history. This algorithm mimics Darwinian theory of survival of the fittest in nature. This chapter presents the most fundamental concepts, operators, and mathematical models of this algorithm. The most popular improvements in the main component of this algorithm (selection, crossover, and mutation) are given too. The chapter also investigates the application of this technique in the field of image processing. In fact, the GA algorithm is employed to reconstruct a binary image from a completely random image.
01 Jun 2020
TL;DR: In the proposed MaTEA, an adaptive selection mechanism is proposed to select suitable “assisted” task for a given task by considering the similarity between tasks and the accumulated rewards of knowledge transfer during the evolution.
Abstract: Multi-task optimization is an emerging research topic in computational intelligence community. In this paper, we propose a novel evolutionary framework, many-task evolutionary algorithm (MaTEA), for many-task optimization. In the proposed MaTEA, an adaptive selection mechanism is proposed to select suitable “assisted” task for a given task by considering the similarity between tasks and the accumulated rewards of knowledge transfer during the evolution. Besides, a knowledge transfer schema via crossover is adopted to exchange information among tasks to improve the search efficiency. In addition, to facilitate measuring similarity between tasks and transferring knowledge among tasks that arrive at different time instances, multiple archives are integrated with the proposed MaTEA. Experiments on both single-objective and multi-objective optimization problems have demonstrated that the proposed MaTEA can outperform the state-of-the-art multi-task evolutionary algorithms, in terms of search efficiency and solution accuracy. Besides, the proposed MaTEA is also capable of solving dynamic many-task optimization where tasks arrive at different time instances.
TL;DR: The experimental results show that the evaluation index and convergence speed of the DE-CQPSO algorithm are better than QPSO (Quantum Particle Swarm Optimization) and other algorithms, whether it is single-objective optimization of fuel cost and emissions or multi-objectives optimization considering both optimization objectives.
Abstract: Consumption of traditional fossil energy has promoted rapid economic development and caused effects such as climate warming and environmental degradation. In order to solve the problem of environmental economic dispatch (EED), this paper proposes a DE-CQPSO (Differential Evolution-Crossover Quantum Particle Swarm Optimization) algorithm based on the fast convergence of differential evolution algorithms and the particle diversity of crossover operators of genetic algorithms. In order to obtain better optimization results, a parameter adaptive control method is used to update the crossover probability. And the problem of multi-objective optimization is solved by introducing a penalty factor. The experimental results show that: the evaluation index and convergence speed of the DE-CQPSO algorithm are better than QPSO (Quantum Particle Swarm Optimization) and other algorithms, whether it is single-objective optimization of fuel cost and emissions or multi-objective optimization considering both optimization objectives. A good compromise value is verified, which verifies the effectiveness and robustness of the DE-CQPSO algorithm in solving environmental economic dispatch problems. The study provides a new research direction for solving environmental economic dispatch problems. At the same time, it provides a reference for the reasonable output of the unit to a certain extent.
TL;DR: A non-dominated sorting genetic algorithm-III (NSGA-III) based 4-D chaotic map is designed, and a novel master-slave model for image encryption is designed to improve the computational speed of the proposed approach.
Abstract: Chaotic maps are extensively utilized in the field of image encryption to generate secret keys. However, these maps suffer from hyper-parameters tuning issues. These parameters are generally selected on hit and trial basis. However, inappropriate selection of these parameters may reduce the performance of chaotic maps. Also, these hyper-parameters are not sensitive to input images. Therefore, in this paper, to handle these issues, a non-dominated sorting genetic algorithm-III (NSGA) based 4-D chaotic map is designed. Additionally, to improve the computational speed of the proposed approach, we have designed a novel master-slave model for image encryption. Initially, computationally expensive operations such as mutation and crossover of NSGA-III are identified. Thereafter, NSGA-III parameters are split among two jobs, i.e., master and slave jobs. For communication between master and slave nodes, the message passing interface is used. Extensive experimental results reveal that the proposed image encryption technique outperforms the existing techniques in terms of various performance measures.
TL;DR: The flexible job scheduling problem is solved that incorporates not only processing time but setup time and transportation time as well and an improved genetic algorithm is proposed to solve the problem with the aim of minimizing the makespan time, minimizing total setup time, and minimizing total transportation time.
Abstract: The flexible job shop scheduling problem is a very important problem in factory scheduling. Most of existing researches only consider the processing time of each operation, however, jobs often require transporting to another machine for the next operation while machines often require setup to process the next job. In addition, the times associated with these steps increase the complexity of this problem. In this paper, the flexible job scheduling problem is solved that incorporates not only processing time but setup time and transportation time as well. After presenting the problem, an improved genetic algorithm is proposed to solve the problem, with the aim of minimizing the makespan time, minimizing total setup time, and minimizing total transportation time. In the improved genetic algorithm, initial solutions are generated through three different methods to improve the quality and diversity of the initial population. Then, a crossover method with artificial pairing is adopted to preserve good solutions and improve poor solutions effectively. In addition, an adaptive weight mechanism is applied to alter mutation probability and search ranges dynamically for individuals in the population. By a series of experiments with standard datasets, we demonstrate the validity of our approach and its strong performance.
TL;DR: An evolutionary multi-task optimization algorithm is proposed to extract the parameters of multiple different photovoltaic models simultaneously through the cross-task crossover to improve the performance in terms of solution quality and convergence rate of the population.
TL;DR: In this paper, the BCS-BEC crossover in an ultracold Fermi atomic gas and a neutron superfluid in the low-density crust regime of a neutron star is discussed.
01 Mar 2020
TL;DR: The proposed algorithm is the first unified algorithm to solve the SMT construction under both octagonal and rectilinear architecture and can obtain several topologies of SMT, which is beneficial for optimizing congestion in VLSI global routing stage.
Abstract: The Steiner minimal tree (SMT) problem is an NP-hard problem, which is the best connection model for a multi-terminal net in global routing problem. This paper presents a unified algorithm for octagonal and rectilinear SMT construction based on hybrid transformation strategy (HTS) and self-adapting particle swarm optimization. Firstly, an effective HTS is proposed to enlarge the search space and improve the convergence speed. Secondly, the proposed HTS in the evolutionary process may produce an ineffective solution, and consequently the crossover and mutation operators of genetic algorithm (GA) based on union-find sets is proposed. Thirdly, a self-adapting strategy that can adjust the acceleration coefficients is proposed to further improve the convergence and the quality of the proposed algorithm. Finally, the hybrid transformation can be applied to GA and the proposed algorithm can be applied to rectilinear architecture. To our best knowledge, the proposed algorithm is the first unified algorithm to solve the SMT construction under both octagonal and rectilinear architecture. The experimental results show that the proposed algorithm can efficiently provide a better solution for SMT problem both in octagonal and rectilinear architectures than others. Moreover, the algorithm can obtain several topologies of SMT, which is beneficial for optimizing congestion in VLSI global routing stage.
TL;DR: A hybrid differential evolution algorithm combining modified CIPDE (MCIPDE) with modified JADE (MJADE) called CIJADE is presented, which performs better than the eleven popular state-of-the-art DE variants.
Abstract: CIPDE and JADE are two powerful and effective Differential Evolution (DE) algorithms with strong exploration and exploitation capabilities. In order to take advantage of these two algorithms, we present a hybrid differential evolution algorithm combining modified CIPDE (MCIPDE) with modified JADE (MJADE) called CIJADE. In CIJADE, the population is first partitioned into two subpopulations according to the fitness value, i.e., superior and inferior subpopulations, to maintain the population diversity. The superior subpopulation evolves using the operation defined in MCIPDE. The MCIPDE adds an external archive to the mutation scheme to enhance the population diversity and exploration capability of original CIPDE. While the inferior subpopulation evolves using the operation defined in MJADE. The MJADE modifies the original JADE by adjusting the parameter p in linear decreasing way to balance the exploration and exploitation ability of original JADE. A new crossover operation is designed to original JADE to deal with the problem of stagnation. Furthermore, the parameters CR and F values of CIJADE are updated according to a modified parameter adaptation strategy in each generation. We validate the performance of the proposed CIJADE algorithm over 28 benchmark functions of the CEC2013 benchmark set. The experimental results indicate that the proposed CIJADE performs better than the eleven popular stateof-the-art DE variants. What's more, we apply the proposed CIJADE to deal with Unmanned Combat Aerial Vehicle (UCAV) path planning problem. The simulation results show that the proposed CIJADE can efficiently find the optimal or near optimal flight path for UCAV.
TL;DR: In this paper, a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators is proposed to solve the traveling salesman problem with drone (TSP-D).
Abstract: This paper addresses the traveling salesman problem with drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years reflecting the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms two existing methods in terms of solution quality and improves many best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to investigate their sensitivity to the performance of our method.
TL;DR: This article proposes to use the neuro-fuzzy system to dynamically determine the strength with which crossover and mutation operators used in genetic algorithms will affect the process of finding the optimal solution.
Abstract: The performance of the well-known particle swarm optimization (PSO) method can be improved by minimizing the possibility of premature convergence in a local minimum. We can achieve this by modifying some of the particles with crossover and mutation operators used in genetic algorithms. However, the impact of genetic operators on the optimization process should depend on the current state of the PSO algorithm. In this article, we propose to use the neuro-fuzzy system to dynamically determine the strength with which these operators will affect the process of finding the optimal solution. Results obtained for well-known benchmark functions demonstrate the advance of the proposed method over the original PSO algorithm and its selected modifications.
TL;DR: Different from traditional methods, this work converts the construction of n × n S-box into a process of putting n Boolean functions one by one into a container and proposes a novel genetic algorithm to construct bijective S-boxes with high nonlinearity.
TL;DR: In this article, a deep neural network is used to generate meta-data and calculate derivatives, and the genetic algorithm is then employed to discover the underlying PDEs without the need for a complete candidate library.
TL;DR: In this article, the authors used self-energy diagrammatic determinant Monte Carlo and dynamical cluster approximation methods to show that the long-range AFM correlations drive an extended crossover from Fermi liquid to insulating behavior that precludes a metal-to-insulator transition.
Abstract: The ground state of the Hubbard model with nearest-neighbor hopping on the square lattice at half filling is known to be that of an antiferromagnetic (AFM) band insulator for any on-site repulsion. At finite temperature, the absence of long-range order makes the question of how the interaction-driven insulator is realized nontrivial. We address this problem with controlled accuracy in the thermodynamic limit using self-energy diagrammatic determinant Monte Carlo and dynamical cluster approximation methods and show that development of long-range AFM correlations drives an extended crossover from Fermi liquid to insulating behavior in the parameter regime that precludes a metal-to-insulator transition. The intermediate crossover state is best described as a non-Fermi liquid with a partially gapped Fermi surface.
01 Jun 2020
TL;DR: A differential evolution algorithm with a variable population size, called DEVIPS, for optimizing the UAV's deployment, where the location of each stop point is encoded into an individual, and thus the whole population represents an entire deployment.
Abstract: This paper studies an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection system, where a UAV is employed as a data collection platform for a group of ground IoT devices. Our objective is to minimize the energy consumption of this system by optimizing the UAV's deployment, including the number and locations of stop points of the UAV. When using evolutionary algorithms to solve this UAV's deployment problem, each individual usually represents an entire deployment. Since the number of stop points is unknown a priori , the length of each individual in the population should be varied during the optimization process. Under this condition, the UAV's deployment is a variable-length optimization problem and the traditional fixed-length mutation and crossover operators should be modified. In this paper, we propose a differential evolution algorithm with a variable population size, called DEVIPS, for optimizing the UAV's deployment. In DEVIPS, the location of each stop point is encoded into an individual, and thus the whole population represents an entire deployment. Over the course of evolution, differential evolution is employed to produce offspring. Afterward, we design a strategy to adjust the population size according to the performance improvement. By this strategy, the number of stop points can be increased, reduced, or kept unchanged adaptively. In DEVIPS, since each individual has a fixed length, the UAV's deployment becomes a fixed-length optimization problem and the traditional fixed-length mutation and crossover operators can be used directly. The performance of DEVIPS is compared with that of five algorithms on a set of instances. The experimental studies demonstrate its effectiveness.
TL;DR: An improved version of MOEA/D with problem-specific heuristics, named PH-MOEAD, to solve the hybrid flowshop scheduling (HFS) lot-streaming problems, where the variable sub-lots constraint is considered to minimize four objectives.
Abstract: Recent years, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been researched and applied for numerous optimization problems. In this study, we propose an improved version of MOEA/D with problem-specific heuristics, named PH-MOEAD, to solve the hybrid flowshop scheduling (HFS) lot-streaming problems, where the variable sub-lots constraint is considered to minimize four objectives, i.e., the penalty caused by the average sojourn time, the energy consumption in the last stage, as well as the earliness and the tardiness values. For solving this complex scheduling problem, each solution is coded by a two-vector-based solution representation, i.e., a sub-lot vector and a scheduling vector. Then, a novel mutation heuristic considering the permutations in the sub-lots is proposed, which can improve the exploitation abilities. Next, a problem-specific crossover heuristic is developed, which considered solutions with different sub-lot size, and therefore can make a solution feasible and enhance the exploration abilities of the algorithm as well. Moreover, several problem-specific lemmas are proposed and a right-shift heuristic based on them is subsequently developed, which can further improve the performance of the algorithm. Lastly, a population initialization mechanism is embedded that can assign a fit reference vector for each solution. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several presented algorithms, both in solution quality and population diversity.
TL;DR: An efficient krill herd (EKH) algorithm is proposed to search optimal thresholding values at different level for color images and Kapur’s entropy is found to be more accurate and robust for color image multilevel thresholding segmentation.
TL;DR: An efficient memetic algorithm (EMA) is proposed to solve the DFJSPT with the objectives of minimizing the makespan, maximum workload, and total energy consumption of factories and can obtain better solutions for approximately 90% of the tested benchmark instances compared to the three well-known algorithms.
Abstract: The traditional distributed flexible job shop scheduling problem (DFJSP) assumes that operations of a job cannot be transferred between different factories. However, in real-world production settings, the operations of a job may need to be processed in different factories owing to requirements of economic globalization or complexity of the job. Hence, in this paper, we propose a distributed flexible job shop scheduling problem with transfers (DFJSPT), in which operations of a job can be processed in different factories. An efficient memetic algorithm (EMA) is proposed to solve the DFJSPT with the objectives of minimizing the makespan, maximum workload, and total energy consumption of factories. In the proposed EMA, a well-designed chromosome presentation and initialization methods are presented to obtain a high-quality initial population. Several crossover and mutation operators and three effective neighborhood structures are designed to expand the search space and accelerate the convergence speed of the solution. Forty benchmark instances of the DFJSPT are constructed to evaluate the EMA and facilitate further studies. The Taguchi method of design of experiments is used to obtain the best combination of key EMA parameters. Extensive computational experiments are carried out to compare the EMA with three well-known algorithms from the literature. The computational results show that the EMA can obtain better solutions for approximately 90% of the tested benchmark instances compared to the three well-known algorithms, thereby demonstrating the DFJSPT’s competitive performance and efficiency.
TL;DR: A genetic algorithm based memetic algorithm (MA) for solving resource constrained project scheduling problem (RCPSP) is presented and numerical results, statistical analysis and comparisons with state-of-the-art algorithms demonstrate the effectiveness of the proposed approach.
TL;DR: The GABONST algorithm has the capability of producing good quality solutions and it also has better control of the exploitation and exploration as compared to the conventional GA, EATLBO, Bat, and Bee algorithms in terms of the statistical assessment.
Abstract: The metaheuristic genetic algorithm (GA) is based on the natural selection process that falls under the umbrella category of evolutionary algorithms (EA). Genetic algorithms are typically utilized for generating high-quality solutions for search and optimization problems by depending on bio-oriented operators such as selection, crossover, and mutation. However, the GA still suffers from some downsides and needs to be improved so as to attain greater control of exploitation and exploration concerning creating a new population and randomness involvement happening in the population at the solution initialization. Furthermore, the mutation is imposed upon the new chromosomes and hence prevents the achievement of an optimal solution. Therefore, this study presents a new GA that is centered on the natural selection theory and it aims to improve the control of exploitation and exploration. The proposed algorithm is called genetic algorithm based on natural selection theory (GABONST). Two assessments of the GABONST are carried out via (i) application of fifteen renowned benchmark test functions and the comparison of the results with the conventional GA, enhanced ameliorated teaching learning-based optimization (EATLBO), Bat and Bee algorithms. (ii) Apply the GABONST in language identification (LID) through integrating the GABONST with extreme learning machine (ELM) and named (GABONST-ELM). The ELM is considered as one of the most useful learning models for carrying out classifications and regression analysis. The generation of results is carried out grounded upon the LID dataset, which is derived from eight separate languages. The GABONST algorithm has the capability of producing good quality solutions and it also has better control of the exploitation and exploration as compared to the conventional GA, EATLBO, Bat, and Bee algorithms in terms of the statistical assessment. Additionally, the obtained results indicate that (GABONST-ELM)-LID has an effective performance with accuracy reaching up to 99.38%.