Artificial Bee Colony Algorithm Research Articles

Design and Optimization of Higher Education Online English Teaching Utilizing Wireless Network Technology in the Context of 5G

The global demand for wireless communication networks is a result of the widespread use of wireless devices and the related surge of wireless service providers. Data transmission speed, coverage areas, cost-effectiveness, resource utilization, security, scalability, and adaptability are all elements expected to be improved in future 5G wireless networks. Since this rapid development of network communication technology affects the delivery of English education in tertiary contexts, English teachers in colleges and universities should make use of network resources—especially the communication tools embedded in the 5G network. File transfer protocol can utilize the advanced artificial bee colony optimization algorithm (AABCOA) to realize classification, and the system proposed in this article is simulated and tested in Matrix Laboratory (MATLAB). The research results indicate that students are unsatisfied with traditional methods of English pedagogy in higher education and are keen to explore the new learning possibilities provided by 5G integrated wireless network technology.

Profit enhancement and imbalance cost reduction with solar PV-fuel cell-EV hybrid system in a wind-integrated day-ahead power market

Abstract In a wind-integrated deregulated network, the wind farm must intimate the power-generating capacity bids to the market controller at least one day before it begins operations. The wind farm's bid submissions are based on the estimated wind speed (EWS); nevertheless, slight differences between the real wind speed (RWS) and the EWS will result in penalties or incentives imposed by the Independent System Operator (ISO). This occurrence is known as the power market imbalance cost, and it has a direct influence on the system's profitability. To mitigate this effect, solar PV and fuel cell storage technologies are used with the wind farm to enhance system profit by offsetting the negative effects of the imbalance cost. Here, solar PV and fuel cells are used to function in the required time i.e. operate in the charging mode when the RWS is greater than the EWS and in discharging mode when the EWS is greater than the RWS to balance the power supply in the grid as to fulfill the power bidding conditions. Furthermore, the study focuses on minimizing potential system risks, which have been assessed using several risk assessment tools such as Value-at-Risk (VaR) and Cumulative Value-at-Risk (CVaR). The work was conducted using an IEEE 14 bus test system. Initially, the solar PV-fuel cell system supplies power to meet local needs, and the remaining energy is sent to the grid to maximize the system's profit. Electric vehicles (EVs) have also been incorporated to maximize the system economy in more quantities and to reduce the system risk further as compared to the solar PV-fuel cell operation. Three different optimization methods, i.e. AGTO (Artificial Gorilla Troops Optimizer Algorithm), ABC (Artificial Bee Colony Algorithm), and SQP (Sequential Quadratic Programming), were used in a comparative analysis to assess the effectiveness of the proposed approach.

A reinforcement learning-based approach for solving multi-agent job shop scheduling problem

Wafer processing is the most expensive, time-consuming, and complex stage in semiconductor manufacturing. It varies significantly based on orders of customers (agents). Optimising the wafer processing flow in a multi-agent scenario can meet customised requirements, speed up delivery, and reduce costs. This work models wafer processing as a multi-agent job shop scheduling problem (MAJSP) with release dates. The objective is to minimise the total weighted makespan of agents. To address both dynamic and static scheduling scenarios in the MAJSP context, two deep reinforcement learning-based (DRL) methods are proposed. In a dynamic scheduling scenario, the statuses of orders and production resources can change at any moment. A DRL method called Graph Transformer Network (GTN) is proposed to rapidly generate high-quality solutions. In a static scheduling scenario, the production plan can be formulated based on predetermined demand and resource conditions. A novel hybrid method (GTN-DABC) that combines GTN with the discrete artificial bee colony algorithm (DABC) is proposed to provide high-quality production plans for manufacturers within an acceptable computation time. Experimental results demonstrate that the proposed GTN outperforms existing heuristics, and the well-designed GTN-DABC is more competitive than other meta-heuristics.

Adaptive Exploration Artificial Bee Colony for Mathematical Optimization

The artificial bee colony (ABC) algorithm is a famous swarm intelligence method utilized across various disciplines due to its robustness. However, it exhibits limitations in exploration mechanisms, particularly in high-dimensional or complex landscapes. This article introduces the adaptive exploration artificial bee colony (AEABC), a novel variant that reinspires the ABC algorithm based on real-world phenomena. AEABC incorporates new distance-based parameters and mechanisms to correct the original design, enhancing its robustness. The performance of AEABC was evaluated against 33 state-of-the-art metaheuristics across twenty-five benchmark functions and an engineering application. AEABC consistently outperformed its counterparts, demonstrating superior efficiency and accuracy. In a variable-sized problem (n = 10), the traditional ABC algorithm converged to 3.086 × 106, while AEABC achieved a convergence of 2.0596 × 10−255, highlighting its robust performance. By addressing the shortcomings of the traditional ABC algorithm, AEABC significantly advances mathematical optimization, especially in engineering applications. This work underscores the significance of the inspiration of the traditional ABC algorithm in enhancing the capabilities of swarm intelligence.

Generating a Landslide Susceptibility Map Using Integrated Meta-Heuristic Optimization and Machine Learning Models

A landslide susceptibility assessment is one of the critical steps in planning for landslide disaster prevention. Advanced machine learning methods can be used as data-driven approaches for landslide susceptibility zonation with several landslide conditioning factors. Despite there being a number of studies on landslide susceptibility assessment, the literature is limited in several contexts, such as parameter optimization, an examination of the factors in detail, and study area. This study addresses these lacks in the literature and aims to develop a landslide susceptibility map of Kentucky, US. Four machine learning methods, namely artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), and stochastic gradient boosting (SGB), were used to train the dataset comprising sixteen landslide conditioning factors after pre-processing the data in terms of data encoding, data scaling, and dimension reduction. The hyperparameters of the machine learning methods were optimized using a state-of-the-art artificial bee colony (ABC) algorithm. The permutation importance and Shapley additive explanations (SHAP) methods were employed to reduce the dimension of the dataset and examine the contributions of each landslide conditioning factor to the output variable, respectively. The findings show that the ABC-SGB hybrid model achieved the highest prediction performance. The SHAP summary plot developed using the ABC-SGB model shows that intense precipitation, distance to faults, and slope were the most significant factors affecting landslide susceptibility. The SHAP analysis further underlines that increases in intense precipitation, distance to faults, and slope are associated with an increase in the probability of landslide incidents. The findings attained in this study can be used by decision makers to develop the most effective resource allocation plan for preventing landslides and minimizing related damages.

A novel energy efficient QoS secure routing algorithm for WSNs

Quality of Service (QoS) routing protocol is a hot topic in the research field of wireless sensor networks (WSNs). However, the task of identifying an optimal path that simultaneously meets multiple QoS constraints is acknowledged as an NP-hard problem, with its complexity intensifying in proportion to the network’s nodal count. Therefore, a novel heuristic multi-objective trust routing method, the Levy Chaos Adaptive Snake Optimization-based Multi-Trust Routing Method (LCASO-MTRM), is proposed, aiming to enhance link bandwidth while simultaneously reducing latency, packet loss, and energy consumption. The proposed method incorporates innovative chaos and adaptive operators within the LCASO framework. The chaos operator enhances population diversity, expands the solution space, and accelerates the search process. Meanwhile, the adaptive operator improves convergence, enhances robustness, and effectively prevents stagnation. Additionally, this paper introduces a novel multi-objective QoS routing model that integrates a link trust mechanism, allowing for a more accurate assessment of link trust levels and a precise reflection of the current link status. The effectiveness of LCASO-MTRM is demonstrated through simulation comparisons with the Improved Particle Swarm Optimization (IPSO), Improved Artificial Bee Colony Algorithm (IABC), and Cloned Whale Optimization Algorithm (CWOA). Simulation results demonstrate that LCASO-MTRM significantly reduces energy consumption by 49.53%, latency by 22.56%, and packet loss by 40.21%, while increasing bandwidth by 6.13%, outperforming the other algorithms.

Optimized Watermarking Using Artificial Bee Colony Algorithm and 6-D Hyperchaotic Map Based Image Cryptographic Scheme

Optimized Watermarking Using Artificial Bee Colony Algorithm and 6-D Hyperchaotic Map Based Image Cryptographic Scheme

Design of Routing Algorithm for Communication of Power Wireless Sensor Networks Based on Improved Harmony Search

In this paper, a routing algorithm for the communication of power wireless sensor networks based on improved harmony search is proposed to reduce the communication routing delay and energy consumption of power wireless sensor networks. Firstly, according to the composition of communication routing delay and energy consumption of power wireless sensor networks, the delay model and energy consumption model are constructed respectively, and the objective function is constructed by assigning different weights. Then, artificial bee colony algorithm and Levy flight mechanism improved harmony search algorithm are adopted for solving the objective function. Experiment results reveal that the algorithm can meet different users’ different demands on delay and energy consumption. If the user needs low delay, set the low delay weight to 1 and the power consumption weight to 0, then the average delay is 7s, and the average power consumption is 3203J. When user demand is low energy consumption, set the delay weight to 0 and the energy consumption weight to 1, so that the average energy consumption is 2667J and the average delay is 15s, which can meet the needs of users.

Application of sparrow search swarm intelligence optimization algorithm in identifying the critical surface in slope-stability

The use of nature-inspired meta-heuristics to tackle complex optimization problems is steadily gaining popularity within a rapidly evolving world. Swarm intelligence (SI) optimization motivated by behavior of community-based organisms of flocks of birds, schools of fish, colonies of ants and bees performs the search through agents whose trajectories are primarily adjusted stochastically and sporadically deterministically, by golden rules drawn from Mother Nature. Each entity within the swarm is swayed by its individual ‘best’ and group’s ‘best’ position while moving randomly to converge to optimal through competition and cooperation. The sparrow search algorithm (SSA), developed by Xuea and Shen (Xue and Shen in Syst Sci Cont Eng 8:22–34, 2020) is a very recent SI approach that adopts the sparrow producer–scrounger model metaphorically for designing optimum searching strategies, inspired by the foraging, anti-predation behavior, and the overall group wisdom of sparrows. SSA has been experimented on some hard benchmark test functions to test its effectiveness and thereafter, applied in slope-stability problems in searching the critical failure surface. The objective function to be optimized is the factor of safety against failure given by Bishop's (Bishop in Geotechnique 5:7–17, 1955) simplified method. Results show SSA is a strong contender to bio-inspired methods like genetic algorithms, big-bang big-crunch, simulated annealing, and artificial bee colony algorithms. The study illustrates the flexibility, efficiency, and robustness of the methodology in function optimization.

Optimal trajectories for UAV three-dimensional path planning using a hybrid ABC-RRT* algorithm

Path planning is critical to the effective operation of unmanned aerial vehicles (UAVs) in complex environments. It is crucial to quickly determine the best path for UAV navigation. In this paper, a novel approach for unmanned aerial vehicle (UAV) path planning is presented by combining the robust artificial bee colony (ABC) algorithm with the flexible rapidly exploring random tree star (RRT*) algorithm. The main objective of this approach is to ensure effective obstacle avoidance. The proposed hybrid algorithm, ABC-RRT*, is compared with several current approaches, namely Improved ABC (IABC), ABC, Particle Swarm Optimization (PSO), ABC-PSO and RRT. ABC-RRT* combines the exploration capabilities of ABC with the route optimization capabilities of RRT* to efficiently determine the best trajectories for UAVs by balancing exploration and exploitation. ABC integration increases the effectiveness of exploration, while RRT* excels at identifying shorter paths and adapting to different conditions. ABC-RRT* has been shown to outperform other methods in terms of path length and flexibility when traversing complicated terrain with many obstacles. This was proven by extensive simulations in various situations. The results demonstrate the effectiveness, fast convergence and robustness of ABC-RRT* and make it a viable solution for obstacle avoidance of UAVs in practice. The fusion of ABC and RRT* for obstacle avoidance shows great potential for UAV route planning and offers remarkable advantages compared to conventional approaches.

Open shop scheduling with group and transportation operations by learning-driven hyper-heuristic algorithms

Open shop scheduling problems (OSSPs) are complex scheduling problems, which have been extensively studied in the literature. Group and transportation activities are two important aspects of OSSPs that still need attention. This work considers an OSSP with group and transportation operations to minimize maximum completion time by solving three key sub-problems: job assignment among groups, job sequence in groups and group sequence on machines. Firstly, an integer programming model is formulized to define the problem. Secondly, a learning-driven hyper-heuristic algorithm is developed by incorporating a Q-learning method and four meta-heuristics, i.e., genetic algorithm, artificial bee colony optimization, variable neighborhood search method and Jaya algorithm. The Q-learning method is devised to select the most promising meta-heuristic for performing at each iteration. Three neighborhood structures are designed by integrating critical machines and critical paths. Finally, the developed model is verified by an exact solver CPLEX, and the comparison results exhibit that CPLEX is effective for instances with ten jobs. For the instances with more than ten jobs, the developed algorithm wins CPLEX in terms of computation accuracy and efficiency, signifying its excellent performance in finding better solutions. Furthermore, four meta-heuristics mentioned above and three state-of-the-art meta-heuristics are employed for comparisons in solving a set of benchmark test instances. The results confirm that the formulated model and algorithm have stronger competitiveness in handling the considered problems.

Power quality improvement of grid‐connected solar power plant systems using a novel fractional order proportional integral derivative controller technique

AbstractRecently, there has been a push to integrate renewable energy system (RES) into grid‐connected load system in enhancing reliability and reducing losses. However, integrating these systems introduces power quality (PQ) issues, especially with non‐linear, critical, and imbalanced loads. Addressing this, a hybrid mantis search‐reptile search algorithm (HMS‐RSA) combined with a unified power quality conditioner (UPQC) to mitigate PQ problems related to current and voltages in RES systems. In other words, the UPQC, enhanced by fractional order proportional integral derivative controller parameters tuned using the proposed HMS‐RSA assists in enhancing the power quality. The approach has been validated by connecting a non‐linear load to the system, which typically creates PQ issues. The proposed method is implemented in MATLAB/Simulink and their performance is analysed in three scenarios, such as sag, swell, and disturbance, and the total harmonic distortion is evaluated to quantify improvements in PQ. Finally, the proposed method is compared with existing approaches, such as ant colony optimization (ACO), artificial bee colony optimization (ABC), and bacterial foraging optimization (BFO). The method also outperforms ACO, ABC, and BFO in terms of convergence speed and effectiveness in mitigating PQ issues.

Adaptive Position Control of Electrohydraulic Servo Systems with Parameter Uncertainty using Artificial Bee Colony Optimization Algorithm

In this paper, we present a robust adaptive backstepping-based controller for precise positioning of the spool valve in an Electro-Hydraulic Servo System (EHSS) under conditions of parameter fluctuations. Classical control strategies, such as PID and linear controllers, often struggle with the nonlinearities and parameter uncertainties inherent in EHSS, leading to poor tracking performance and instability. To overcome these limitations, we employ the Artificial Bee Colony (ABC) algorithm to optimize the controller parameters, minimizing both the tracking error and control signal. The proposed controller ensures uniform ultimate boundedness of the error and control signal by utilizing a Lyapunov-based stability criterion, which guarantees that errors do not exceed a predefined bound despite uncertainties and disturbances. Simulation results validate the robustness and effectiveness of the control scheme, even in the presence of parameter variations. Additionally, a comparative analysis with sliding mode control highlights the superior performance of the proposed method, particularly in providing smoother control signals and reducing chattering while ensuring stability.

Solving large-scale instances of the urban transit routing problem with a parallel artificial bee colony-hill climbing optimization algorithm

Swarm Intelligence simulates the collective behavior of decentralized and self-organized swarms. One of the main relevant methods is the Artificial Bee Colony (ABC) algorithm which simulates the foraging behavior of bee swarms in a colony to produce efficient solutions to various problems. The Urban Transit Routing Problem (UTRP) involves finding an efficient set of routes in a transit network to satisfy travel demand subject to operational and budget constraints. It is a complex, NP-Hard problem, in which otherwise correct solutions can be rejected because of impracticability. In this study, a hybrid algorithm consisting of a parallel ABC and Hill Climbing was used to find quality solutions to the UTRP. Thorough comparative results on Mandl’s well-known instance and Mumford’s large-scale instances demonstrate that the proposed algorithm outperforms existing techniques, achieving high levels of direct trip coverage in small computational times. Remarkably, when applied to the most extensive benchmark comprising over 6 million trips and 60 bus routes, the proposed algorithm demonstrates an impressive 11 % enhancement in direct coverage over the previously best-reported results, allowing the design of real-world bus networks in under 3 hours.

Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP

The blasting block size of open-pit mines is influenced by many factors, and the influencing factors have a very complex nonlinear relationship. Traditional empirical formulas and a single neural network model cannot meet the requirements of modern blasting safety. To improve the prediction accuracy of blasting block size, the measured data of Beskuduk open-pit coal mine is used as training and testing samples. Seven factors including rock tensile strength, rock compressive strength, and blast hole spacing are selected as input variables of the prediction model. The average size of blasting fragmentation X50 is used as the output variable of the prediction model. The kernel principal component analysis (KPCA) is adopted to reduce the dimensionality of the input variables. The beetle antennae search algorithm (BAS) is selected to optimize the parameters of the initial weights and thresholds of the back propagation (BP) neural network. Finally, prediction model of blasting fragmentation in open-pit coal mine based on KPCA-BAS-BP is established. The results show that the average relative error of the model is 1.77%, and the root mean square error is 1.52%. Compared with the unoptimized BP neural network and the BP neural network optimized by the artificial bee colony algorithm (ABC) model, this model has higher prediction accuracy and is more suitable for predicting the blasting block size of open-pit coal mines, it provides a new method for predicting the fragmentation of blasting under the influence of multiple factors, filling the gap in related theoretical research, and has certain practical application value.

RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

ProblemBreast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.AimThis study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity.MethodsWe introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.ResultsThe proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.ConclusionOur findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection

With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter–wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27% on average compared to various multi-objective feature selection methods, while reducing the number of selected features by 88.76% on average. Meanwhile, IMOABC shows an average improvement of 4.24% in classification accuracy across all datasets, with an average reduction of 76.73% in the number of selected features compared to various traditional methods.

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network

Cyberattacks are increasingly becoming more complex, which makes intrusion detection extremely difficult. Several intrusion detection approaches have been developed in the literature and utilized to tackle computer security intrusions. Implementing machine learning and deep learning models for network intrusion detection has been a topic of active research in cybersecurity. In this study, artificial neural networks (ANNs), a type of machine learning algorithm, are employed to determine optimal network weight sets during the training phase. Conventional training algorithms, such as back-propagation, may encounter challenges in optimization due to being entrapped within local minima during the iterative optimization process; global search strategies can be slow at locating global minima, and they may suffer from a low detection rate. In the ANN training, the Artificial Bee Colony (ABC) algorithm enables the avoidance of local minimum solutions by conducting a high-performance search in the solution space but it needs some modifications. To address these challenges, this work suggests a Deep Autoencoder (DAE)-based, vectorized, and parallelized ABC algorithm for training feed-forward artificial neural networks, which is tested on the UNSW-NB15 and NF-UNSW-NB15-v2 datasets. Our experimental results demonstrate that the proposed DAE-based parallel ABC-ANN outperforms existing metaheuristics, showing notable improvements in network intrusion detection. The experimental results reveal a notable improvement in network intrusion detection through this proposed approach, exhibiting an increase in detection rate (DR) by 0.76 to 0.81 and a reduction in false alarm rate (FAR) by 0.016 to 0.005 compared to the ANN-BP algorithm on the UNSW-NB15 dataset. Furthermore, there is a reduction in FAR by 0.006 to 0.0003 compared to the ANN-BP algorithm on the NF-UNSW-NB15-v2 dataset. These findings underscore the effectiveness of our proposed approach in enhancing network security against network intrusions.

Research on surface integrity of titanium alloy in V-groove micro-textured ball-end milling cutter

In order to realize chip guiding control in titanium alloy machining, reduce the negative influence of unstable chip flow direction on the milling environment, and improve the performance and reliability of parts, this article adopts the V-groove micro-texture carbide tool as the research object and takes the laser preparation of the V-groove micro-texture as a breakthrough point, analyzes the influence of laser parameters on the aspect ratio of V-groove micro-texture, and optimizes the laser parameters. The titanium alloy milling test platform was built to analyze the influence of the micro-texture parameters of the V-groove on the surface integrity of the workpiece, reveal the action mechanism, and optimize the relevant parameters based on the artificial bee colony algorithm. The results show that laser power has the largest influence on the aspect ratio of the groove. The power determines the melt content in the groove and influences the width and depth of the micro-texture. The optimized laser combination parameters are: laser power 42.5 W, scanning speed 10 mm/s scanning times nine times, scanning frequency 30 kHz, and spot diameter 30 μm. The V-groove micro-texture on the tool surface can change the chip flow direction, reduce the contact area of the chip, accommodate the cutting particles, and improve heat conduction, reducing cutting resistance and friction, thereby optimizing cutting control distance, thereby improving the surface integrity of the workpiece becomes. The optimized V-groove micro-texture parameters are as follows: V-groove angle X = 46.2°, V-groove height H = 175 μm, radial spacing L = 259.4 μm, circumferential angle J = 2.1°, and distance from the edge K = 120 μm.

Prediction of permeability characteristics of mine slurries and sediments using finite-strain framework by optimisation algorithms

Determining the permeability characteristics of mine tailing slurries through laboratory finite-strain consolidation tests is costly due to the extensive testing required. An alternative approach involves back-predicting hydraulic conductivities from settling column test data. This study employed settlement and excess pore pressure data as inputs in optimising the permeability parameters using the finite-strain consolidation (FSC) framework. The proposed approach used the finite difference method to solve the governing equation for FSC in the forward analysis. Two novel variants of the Artificial Bee Colony (ABC) algorithm, namely the Modified Artificial Bee Colony (MABC) and the Hybrid Artificial Bee Colony (HABC) algorithms, were used to back-predict the hydraulic conductivity function parameters from the FSC test data. These models predicted the permeability characteristics using the temporal variation of the settlements for six slurries and excess pore pressure dissipation data for four slurries. The performance of the proposed algorithms along with Hybrid Particle Swarm Optimisation (HPSO) was compared with the conventional optimisation algorithms, viz., PSO, ABC, and Quantum PSO (QPSO). The HABC and HPSO exhibited remarkable stability, consistently converging to identical solution sets across multiple iterations, thereby outperforming other algorithms in this study. Despite its higher time complexity by HABC relative to the other evaluated methods, this complexity is warranted for enhanced robustness. The current study is helpful in brining practical and reliable methodologies for estimating the hydraulic conductivity function from field-scale conductivity (FSC) data, providing critical insights for the safe and efficient management of slurry wastes.