International Journal of Bio-Inspired Computation (26 papers in press)

Regular Issues

• Prediction of International Shipping Container Throughput Based on Particle Swarm Optimisation and Grey Wolf Optimisation  
  by Xia Zhao 
  Abstract: In order to improve the prediction accuracy for port container throughput in international shipping, PCA is first used to reduce the dimension of the input SVR indicators, thereby reducing the dimension of the SVR input; Secondly, GWO is used to improve PSO, and a port container throughput prediction model based on GWO-PSO-SVR is constructed, thereby improving the prediction accuracy of SVR for port container throughput. Results show that the improved PSO performs well in the test function; Based on data from Tianjin Port, the SVR prediction results indicate that its MAPE index is the lowest, at 12.96, which is closest to the true value.
  Keywords: Particle Swarm Optimization; Grey Wolf Optimization Algorithm; SVR prediction model; MAPE indicators; PCA.
  DOI: 10.1504/IJBIC.2024.10068325
   
  
• Research on Philosophy Sentence Segmentation Algorithm Based on BERT Pre-training and Deep Learning Network  
  by Dan Cheng 
  Abstract: To improve the accuracy of sentence breaking in ancient philosophy texts, based on BERT pre-training and deep learning network, an ancient philosophy sentence segmentation algorithm with high accuracy is proposed. Based on Lattice model, BERT model is used to encode ancient philosophical texts into word vectors, and Flat-Lattice Transformer (FLT) position coding method is adopted to determine the interaction relationship between vectors. Then, sentence segmentation situation of ancient philosophical texts is predicted. Finally, sentence segmentation accuracy of ancient philosophical texts is improved. The simulation results show that compared with Lattice-LAN, BERT-Bi-LSTM-CRF and Bi-LSTM-CRF models, the proposed algorithm has obvious advantages in the accuracy of ancient philosophy sentence segmentation. Moreover, accuracy, recall, and F-value of the proposed algorithm are 82.82%, 70.71%, and 76.29%, respectively, which improves the sentence segmentation accuracy of ancient philosophical texts and has certain reference value.
  Keywords: philosophy sentence segmentation; BERT pre-training; deep learning; LSTM model; FLT position coding.
  DOI: 10.1504/IJBIC.2024.10068326
   
  
• Research on Bi-Objective Charge Batch Planning Optimisation Method based on Improved -Constraint Framework  
  by Congxin Li, Liangliang Sun 
  Abstract: Charge batch planning (CHBP) is the basis of steelmaking-continuous casting section batch planning (SCCSBP). With the rapid development of the market-oriented demand of steel enterprises in the direction of multi-species, small batch, and just-in-time delivery, the integrated production process of SCCSBP dramatically increases the functional requirements of flexibility, material yield, and time-dynamic balancing of the CHBP. Therefore, the preparation of a high-quality CHBP is of great significance to improve the efficiency of steelmaking production and reduce material and energy consumption. A bi-objective mathematical model is established, and a cooperative optimisation framework combining an improved -constraint method (IECM) with branch-and-cut (B&C) is developed. Employing a bisection method-based heuristic, can rapidly detect the valid number of sub-problems, thus avoiding the computational burden of redundant sub-problems in traditional -constraint method (ECM). Meanwhile, this method can obtain multiple Pareto non-dominated solutions, providing more schemes for synergistic optimisation at each stage. The B&C can acquire high-quality solutions for sub-problems. Finally, simulation experiments with actual production data validate that the proposed method reduces the objective function values by 7.19%, 7.22% and 0.16% compared to the linear weighting method, traditional ECM and multi-objective optimisation method, respectively; and reduces the CPU time by 46%, 80.7% and 73.64%, respectively.
  Keywords: charge batch planning; steelmaking-continuous casting; improved ε-constraint; Pareto.
  DOI: 10.1504/IJBIC.2024.10068460
   
  
• Research on Sports Image Restoration Algorithm based on Deep Learning  
  by Xinjiao Zhang, Xuefei Tao 
  Abstract: To improve the sharpness and realism of sports image restoration, an image restoration method based on deep learning is proposed. The improved GAN (Generative Adversarial Network) is adopted for image denoising, and the improved RAiA-Net is used for image rain removal. The simulation results show that the peak signal-to-noise ratio and structural similarity of the improved GAN network reach 32.56 and 0.91, respectively, which are 3.45 and 0.04 higher than those of GAN network. The peak signal-to-noise ratio and structural similarity of RAiA-Net are 36.87 and 0.98 respectively, and the time consumption is shorter (0.43s), which has certain advantages and practicability. Compared with the standard GAN network, CBM3D network and CNN network, the designed network has better image denoising performance and better performance in peak signal-to-noise ratio and structural similarity indexes.
  Keywords: deep learning; image restoration; image denoising; image rain removal; generative adversarial network.
  DOI: 10.1504/IJBIC.2024.10068652
   
  
• Optimized Dual Temporal Gated Multi-Graph Convolution Network Based Distributed Denial of Service Attack Detection in Cloud Computing  
  by Ramesh Babu Putchanuthala, Gopisetty Naga Rama Devi, Prabha Murugesan, Muniyandy Elangovan, Radhika Rathanasalam, Ramasamy SenthamilSelvan 
  Abstract: Distributed denial of service (DDoS) attacks are growing threat to network security, and existing methods attains higher false positive and false negatives when classifying attack and legitimate data, resulting in reduced accuracy. To overcome this, optimised dual temporal gated multi-graph convolution network based fennec fox optimisation for distributed denial of service attack detection in cloud computing (DTGMGCN-DoS-ADD-CC) is proposed. Initially, data adaptive Gaussian average filtering (DAGAF) pre-processes the CICIDS2017 dataset to correct mismatched values. Then swarm optimisation algorithm (DSOA) selects the transformed features, its used by multiple-graph convolution network with dual temporal gates (DTGMGCN) for precise detection of normal and attacked packet of information (API). The Fennec fox optimisation (FFO) fine-tunes DTGMGCNs weight parameters, further boosting performance. Experimental results show that DTGMGCN-DoS-ADD-CC achieves 99.37% accuracy, 98.9% sensitivity, and 98.95% specificity, outperforming existing methods. The improvement highlights robustness and efficacy of the DTGMGCN-DoS-ADD-CC approach for DDoS attack detection in cloud computing.
  Keywords: Dual Temporal Gated Multi-Graph Convolution Network; Fennec Fox Optimization; Cloud Computing; Distributed Denial of Service Attack Detection; Machine Learning.
  DOI: 10.1504/IJBIC.2024.10069266
   
  
• Contrastive Hypergraph Attention Reinforcement Learning for Asset Portfolio Management in Digital Auditing  
  by Xuan Liu, Xu Cao, Xinying Fang, Nan Gao 
  Abstract: Asset portfolio management (APM) is a critical task within digital auditing, aimed at continuously reallocating funds across a set of assets with the aim of maximising investment returns or suppressing risks. Recent APM methods have learned a policy function to generate appropriate portfolios in a reinforcement learning framework and have achieved encouraging progress. However, these methods ignore the fact that assets in portfolios may be broadly interrelated and affected by each other, and the asset relationship information is highly valuable in improving APM. In this paper, we introduce group-wise relationship information as additional environmental cues to improve portfolio policy learning for APM. We propose a dual-channel hypergraph attention network to jointly capture the heterogeneous fund-holding and industry-belonging relationships via attention-based information aggregation among assets. In addition, we introduce contrastive learning to provide extra supervision signals for higher modelling capacity. Experimental results demonstrate that this method yields significantly greater benefits compared to state-of-the-art methods for APM.
  Keywords: Asset portfolio management; Hypergraph learning; Contrastive learning; Attention;Reinforcement learning.
  DOI: 10.1504/IJBIC.2024.10069679
   
  
• Financial Fraud Detection Based on Distributed L  
  by Hongwei Chen, Jianpeng Wang, Lun Chen, Zexi Chen, Rong Gao, Xia Li 
  Abstract: With the growth of online payments, financial fraud has led to significant economic losses. Accurate fraud detection is therefore crucial for protecting consumer rights and ensuring the security of payment platforms. Current detection methods mainly rely on machine learning algorithms, but their performance is often limited by hyperparameter tuning constraints. To address this, we propose a Spark-based distributed L
  Keywords: fraud detection; sailfish optimiser; Lévy flight; Spark.
  DOI: 10.1504/IJBIC.2025.10069838
   
  
• MOWOA-D based Feature Selection Study for Pneumonia Detection  
  by Leyi Xiao, Yixuan Su, Xia Xie, Chaodong Fan 
  Abstract: AI's application in medical image analysis presents challenges in balancing solution diversity and convergence in high-dimensional datasets for accurate feature selection. To this end, we propose an improved multi-objective evolutionary algorithm (MOWOA-D) based on decomposition and Whale Optimization Algorithm (WOA) learning strategy. This algorithm integrates the WOA learning strategy into the original MOEA/D to optimize the search mechanism, ensuring accuracy and uniformity. Then we design a dynamic neighborhood reference strategy to select reference points based on the population's real-time state, guiding the generation of offspring and enhancing solution quality. We also use an adaptive binary mutation operator that introduce the Hamming distance and adjust mutation probability during iteration to balance exploration and exploitation. Experimental results indicate that MOWOA-D outperforms five multi-objective evolutionary algorithms in efficiency and uniformity. Feature selection experiments on the UCI database show improved classification accuracy and reduced data redundancy. Tests on pneumonia data demonstrate the method's practicability.
  Keywords: Multi-objective optimization; Feature selection; MOEA/D; Pneumonia; Whale optimization; Heuristic method; Dynamic reference strategy; Binary mutation operator; Hamming distance; Machine learning.
  DOI: 10.1504/IJBIC.2025.10070157
   
  
• IoT-Based Plant Disease Detection Using Enhanced Elman Spike Neural Network with Capuchin Search Optimisation Algorithm  
  by D. Karunkuzhali, B. Meenakshi 
  Abstract: In recent years, the Internet of Things (IoT) has gained attention for its transformative role in agriculture. A main challenge in agriculture is early identification of plant disease which is needed to prevent crop loss and ensure food preservative. Typical plant disease detection techniques are often time-consuming and labor-intensive, making it important to replace them with automated systems. Therefore, IoT-Based Plant Disease Detection Using Enhanced Elman Spike Neural Network together with Capuchin Search Optimization Algorithm(IoT-PDD-OEESNN) is proposed in this paper for detecting potato plant. The input data is preprocessed using Altered phase preserving dynamic range compression (APPDRC) filtering model for extracting the leaf region of the image and also eliminates the noise and blur image. The proposed IoT-PDD-OEESNN approach is implemented in Python using certain metrics. The IoT-PDD-OEESNN method attains better accuracy of 30.12%, 26.75% and lower computation time of 27.18%, 26.29%, and 29.56% when analyzed with the existing methods.
  Keywords: Capuchin search optimization algorithm; Enhanced Elman Spike Neural Network; Gray-Level Co-Occurrence Matrix; Internet of Things; Plant village dataset and Variation Density Peaks Clustering.
  DOI: 10.1504/IJBIC.2025.10070546
   
  
• Efficient Data Retrieval Model based on Semantic Similarity Analysis using Chiroptera Buzzard Optimisation Tuned Deep CNN  
  by Ankush Raosaheb Deshmukh, Premchand B. Ambhore 
  Abstract: To extract meaningful insights, integrating the data classification and semantic text summarization is essential, aiding in the identification of contextually significant content. Most of the existing techniques encounter multiple challenges from the perspective of machine understanding, especially for languages with limited resources, and fail to learn the sequence of correlations effectively. Nevertheless, there is still much space for enhancing the speed of data retrieved because current approaches fail to take the spatial and semantic aspects into account. To tackle this issue, this research presents an efficient data retrieval model utilising Chiroptera Buzzard optimization adapted deep Convolutional Neural Network (CBO adapted deep CNN) for semantic similarity analysis. Specifically, the Chiroptera buzzard optimisation is utilised for feature selection and fine-tuning the hyperparameters of DCNN that improves the classification accuracy. Hence, the proposed model reduces the computational complexity and provides remarkable performance in terms of metrics attaining 99.98% accuracy, 99.53% recall, 99.93% precision, 99.84%Fbeta, 99.52%Cohen kappa, and 99.52% F1-score for 90% of training.
  Keywords: Data retrieval model; Convolutional Neural Network; Chiroptera buzzard optimization; Semantic Similarity Analysis; and Text summarization.
  DOI: 10.1504/IJBIC.2025.10070716
   
  
• SenseNet: Satellite Image Enhancement using Optimised Deep Denoiser for Cloud Removal  
  by Renuka Sandeep Gound, Sudeep D. Thepade 
  Abstract: The research focuses on devising a Hybrid CFO deep denoiser model to eliminate the blurring edges of cloud-covered boundaries in the RS image The need for reconstructing the high-quality satellite image is elaborated in this research article for which a proposed Hybrid CFO deep denoiser is developed The optimized learning of deep denoiser increases the reconstruction ability, which is the main focus of the research The satellite images are pre-processed and exposed to the reconstruction in such a way that the proposed Hybrid CFO deep denoiser reconstructs the high-quality satellite image without the influence of cloud The experimental results also demonstrate that the CFO-based deep denoiser exhibits higher performance in terms of PSNR, SSIM, and MSE, while compared with the existing denoiser The performance improvement of 2 165dB, 1 436% and 0 816% is obtained by the Hybrid CFO-deep denoiser concerning existing denoisers in terms of PSNR by maintaining the K-fold at 5.
  Keywords: Image enhancement; Satellite Image; Deep learning; Optimization; Cloud Removal.
  DOI: 10.1504/IJBIC.2025.10070740
   
  
• Student Performance Prediction (SPP)Model using HLRO-DMN  
  by L. Srinivasan, D. Kalaivani, C. Nalini, I. Gugan 
  Abstract: This research introduced the proposed hybrid leader remora optimisation algorithm with deep maxout network (HLRO_DMN) for accurately predicting the students performance. Initially, the input data acquired from the dataset is transformed into a suitable format using Yeo-Johnsons transformation. Then, the dice coefficient is employed for selecting optimal features, which combines the feature score obtained from the Fisher score and the Tversky index. In addition, the data augmentation is completed by the bootstrapping method, and the performance prediction is carried out by the DMN, wherein the weight of the DMN is tuned by the HLRO algorithm. Besides, the experimentation of HLRO_DMN attained the best result using certain metrics, like mean square error (MSE), Root mean square error (RMSE), and mean absolute error (MAE), and the accuracy of the corresponding values noted by the devised scheme are 5.4032, 0.175, 0.4444, and 91.314, respectively.
  Keywords: Remora Optimization algorithm; Deep Maxout network; Hybrid leader based optimization; Yeo-Johnson's transformation; Dice coefficient.
  DOI: 10.1504/IJBIC.2025.10071048
   
  
• Spatial and Temporal Distribution of Ecosystem Service Values in the Northeast Tiger and Leopard National Park and Analysis of Driving Factors based on the Geographical Detector and Remote Sensing Classification  
  by Zhihan Wan, Hongxun Li 
  Abstract: This study provides a basis for research on the value of ecosystem services of Northeast Tiger and Leopard National Park (NTLNP) after the implementation of the natural protection project by analyzing the change of land use, temporal and spatial changes of ecosystem service value, and driving factors from 2000 to 2022. The results showed that the area of broad-leaved forest in NTLNP was the largest and the land use types changed from broad-leaved forest to mixed coniferous and broad-leaved forest and coniferous forest from 2000 to 2022; the ecosystem service value of NTLNP showed a trend of first decreasing and then increasing, with a total increase of 464.99 million yuan. The interaction between elevation and climate factors has a great influence on the value of ecosystem services, and natural factors have a binding effect on the spatial differentiation of ecosystem service value.
  Keywords: Northeast Tiger and Leopard National Park; ecosystem services value; driving factors; geographical detector.
  DOI: 10.1504/IJBIC.2025.10071729
   
  
• Research on Regional Low-Carbon Economy Analysis Model based on Improved SVM  
  by Na Xiao, Yan Liu 
  Abstract: In order to further grasp the level of low-carbon economic development in the region and provide feasible suggestions for regional development, this paper proposes a regional low-carbon economy analysis and prediction model based on multiple strategies improved sparrow search algorithm optimised SVM (ISSA-SVM). Where, support vector machine (SVM) is used as the basic prediction method, and sparrow search optimisation algorithm improved based on multi strategy and combine with PLS component extraction is introduced to optimise the parameters of SVM and further enhance its predictive performance. Simulation results show that the designed prediction model based on ISSA-SVM has good prediction performance. Furthermore, the prediction error rate of this model has always been stable below 2.5%, and its precision is high. Therefore, the proposed model can be used to analyse and predict the development level of low-carbon economy in practice, which has certain feasibility.
  Keywords: low-carbon economy; support vector machine; sparrow search algorithm; prediction model.
  DOI: 10.1504/IJBIC.2025.10072382
   
  
• Classification of Malaria Disease using Optimal Fire Hawks based Capsule Convolutional Vision Transformer Network  
  by Pallavi Bhanudas Salunkhe, Pravin Sahebrao Patil 
  Abstract: Malaria is a widespread harmful disease caused by parasites transmitted through infected mosquitoes. The primary challenge addressed in this paper is the low performance, high error rates, and poor segmentation accuracy in malaria disease classification using existing machine learning (ML) and deep learning (DL) techniques. To overcome these limitations, this study proposes an advanced malaria classification model using the optimal fire Hawks-based capsule convolutional vision transformer network (OFH_C2ViTNet). The input image was segmented using feature concatenation-based U-Net (FC-UNet) based on red blood cells, white blood cells, and malaria parasites. Finally, the suggested optimal fire Hawks-based capsule convolutional vision transformer network (OFH_C2ViTNet) was used to categorise malaria into difficult, trophozoite, gametocyte, ring, and schizont kinds. From the input images, the proposed model provides an efficient result for identifying the disease at an early stage. Experimental evaluation demonstrates that the proposed model achieves superior performance, with an accuracy of 97.42% and a precision of 98.68%, outperforming existing state-of-the-art methods in malaria detection.
  Keywords: Median Filtering; Contrast Enhancement; Feature Concatenation; Hawks Optimizer; Vision Transformer Network; Malaria Parasites.
  DOI: 10.1504/IJBIC.2025.10072835
   
  
• Self-Improved Optimisation Model On Energy-Aware Resource Deployment Algorithm for Cloud Data Centres  
  by Prabha B, Kalangi Ruth Ramya, Charanjeet Singh 
  Abstract: The high processing demands of business, social, web, and scientific applications are driving a sharp rise in the demand for cloud computing. Most of the current cloud data centre resource management algorithms take CPU utilisation as the main consideration and increase CPU utilisation through virtual machine integration to reduce the energy consumption of cloud data centres. This work intends to propose a new cloud resource deployment model that includes three major aspects. Clustering is the initial phase, where clustering takes place by the Improved FCM-based clustering. Here, it is used to group the physical machines under the consideration of Energy and Distance. The process of deployment of the virtual machine is handled by Optimisation assisted Bi-GRU. Thereby, the proposed optimisation problem will be solved by Updated SSA with Baker Map Evaluation. The improved FCM with enhanced Jensen-Shannon distance has achieved a high accuracy value of 0.944.
  Keywords: Cloud Computing; Cloud Data Centers; Improved Fuzzy C Means based clustering; Resource Deployment; Updated Squirrel Search Algorithm with Baker Map Evaluation Algorithm.
  DOI: 10.1504/IJBIC.2025.10073095
   
  
• Attraction-Repulsion based Balance of Exploration and Exploitation in Many-Objective Optimisation with Application to Water Resources Allocation  
  by Yingnan Ma, Junlong Shen, Di Zhu, Renbin Xiao 
  Abstract: This paper addresses the critical issue of water resource allocation in the Yellow River Basin, a region characterized by severe water scarcity and heterogeneous spatial distribution. A novel many-objective evolutionary algorithm, MaOEA/AROA, is proposed, integrating attraction-repulsion mechanisms and co-evolution strategies to effectively balance exploration and exploitation in optimisation. From the Environmental, Social, and Governance (ESG) perspective, the model aims to minimize pollution, maximize equity, and enhance economic benefits, addressing the industrial, agricultural, and domestic water demands across nine provinces. The algorithm extends the single-objective attraction-repulsion optimisation paradigm to a many-objective framework, leveraging its strong development capability while maintaining diversity through a dual-population co-evolution approach. Experimental results demonstrate that MaOEA/AROA generates a diverse set of Pareto optimal solutions, offering flexible strategies to balance conflicting objectives and promote sustainable development. The algorithm's performance is validated on standard benchmark problems and applied to the Yellow River Basin, showcasing its practical utility in complex water resource allocation scenarios. Future work will focus on enhancing the model's robustness and incorporating dynamic water cycle considerations to further improve its applicability in real-world water resource management.
  Keywords: Many-objective optimisation; Attraction-repulsion optimisation; Indicator-based MaOEA; Co-evolution; Water resource allocation.
  DOI: 10.1504/IJBIC.2025.10073274
   
  
• A Hyper-spherical Surface Search Optimisation Algorithm for Practical Cellular Wireless Network  
  by Wenxiang Wang, Zhiping Tan, Lanlan Kang, Yanxin Lai, Jialin Li 
  Abstract: The optimisation of practical cellular wireless networks is a complex mixed-variable large-scale multi-objective optimisation problem. Traditional multi-objective algorithms encounter problems such as the curse of dimensionality, slow convergence speed, and quantisation errors when solving such problems. To address this problem, a large-scale multi-objective hyper-spherical surface search optimisation algorithm, named HSSOF-BHA is proposed in this paper. Firstly, a hyper-spherical surface search method is used to compress the high-dimensional search space. Secondly, a micro-hyper-spherical surface local search mechanism is performed to enhance the coverage performance. Thirdly, a mixed-variable evolutionary operator is employed to overcome quantisation errors. The experiments in the practical cellular wireless network optimisation model show that the proposed algorithm has significant advantages over the other four state-of-the-art comparison algorithms, and the indicators can increase RSRP by 5.72 dBm, SINR by 3.15 dB, and reduce OCR by 9.56% compared to manual optimisation indicators of the current network.
  Keywords: Hyper-spherical surface; Large-scale optimization; Mixed-variable optimization; Evolutionary algorithm; Wireless network optimization.
  DOI: 10.1504/IJBIC.2025.10073614
   
  
• MaOEA/APP-PBI: A Many-objective Evolutionary Algorithm based on Adaptive Projection Plane and PBI Function  
  by Weiwei Yu, Jing Wang, Yanxiang Deng 
  Abstract: Many-objective optimisation challenges traditional Pareto-based evolutionary algorithms. This work introduces a self-adjusting dominance relation using an adaptive projection plane, enhancing convergence by leveraging distances to the plane and between projection points without extra parameters. It also proposes a PBI-function-based method to balance convergence and diversity in solution screening. These techniques are integrated into the NSGA-II framework, creating the MaOEA/APP-PBI algorithm. Evaluated on 5-, 10-, 15-, and 20-objective DTLZ and WFG problems using IGD and HV metrics, MaOEA/APP-PBI outperforms six leading algorithms. Results demonstrate its significantly superior convergence and diversity across various objectives, highlighting its effectiveness for many-objective optimisation.
  Keywords: many-objective optimization problem; many-objective evolutionary algorithm; dominant relationship; convergence; diversity.
  DOI: 10.1504/IJBIC.2025.10074183
   
  
• Modified African Vulture Optimisation Algorithm using Inertia Weight and Clerc-Kennedy Formula  
  by Saleh Altbawi, Saifulnizam Bin Abd. Khalid, Rayan Hamza Alsisi, Touqeer Ahmed Juman, Zeeshan Arfeen 
  Abstract: This study introduces the Modified African Vulture Optimization Algorithm (MAVOA) as an enhancement of the existing African Vulture Optimization algorithm (AVOA). MAVOA addresses several limitations of AVOA, including suboptimal solutions and premature convergence due to demographic homogeneity. The MAVOA involve incorporating the Clerc-Kennedy formula and adjusting the inertia weight, which leads to faster convergence and better-quality hyper parameter approximation. One notable feature of MAVOA is its flexibility, allowing it to dynamically regulate its search strategy to balance local exploitation and global exploration. Comparative evaluations against other algorithms demonstrate the superior performance of MAVOA. This study also showcases MAVOA's effectiveness through real-world engineering challenges, highlighting its potential for various optimization applications.
  Keywords: African Vulture Optimisation Algorithm; metaheuristic; Optimization; Benchmark; Economic Load Dispatch.
  DOI: 10.1504/IJBIC.2025.10074201
   
  
• A Hybrid Genetic Algorithm Based Method for Smart Beef Farming  
  by Kangshun Li, JunHao Chen, ZiHeng Chen, WenYan Lin 
  Abstract: To enhance cost efficiency in the cattle industry, particularly through feed formulation optimisation, we propose a novel feed encoding method that accurately and simply expresses the proportions between different feeds. Building upon this encoding method, we introduce ASAGA (Adaptive Simulated Annealing Genetic Algorithm), a hybrid genetic algorithm designed to optimise feed costs. ASAGA cleverly combines the powerful global search capability of genetic algorithms with the effective local optimisation ability of simulated annealing. It incorporates an elite pool strategy to retain high-potential individuals during population evolution and utilises adaptive crossover and mutation strategies to improve adaptability and resolution efficiency. Furthermore, we introduce three different neighbourhood structure strategies to enhance exploration of the solution space. Experimental results have demonstrated the effectiveness of ASAGA in optimising feed costs for smart cattle farming.
  Keywords: Adaptive genetic algorithm; Simulated Annealing Algorithm; Smart Cattle Farming.
  DOI: 10.1504/IJBIC.2025.10074233
   
  
• Impacts of fractional plasticity on chaotic resonance in small-world networks  
  by Hao Yin, Zibo Yu, Siya Yao, Qi Kang 
  Abstract: Building on prior research introducing the Discrete Fractional-Order Izhikevich Neuron (DFOIN) model, this study extends its application to small-world networks to explore the influence of fractional plasticity on chaotic resonance. Fourier coefficients are used to measure the system's ability to perceive weak signals, and the influence of small-world network rewiring probability and fractional-order dynamics on chaotic resonance performance is analysed. A fractional-order plasticity mechanism is introduced to address phase errors between pacemaker neurons and the small-world network caused by communication delays. The results suggest that fractional plasticity improves signal detection and enhances network adaptability by modulating fractional orders. This study underscores the powerful synergy between small-world networks and fractional plasticity, offering a robust framework for real-time optimization of network behaviours with potential applications in neuromorphic computing and biologically inspired network designs.
  Keywords: Fractional-order systems; small-world network; discrete Izhikevich model; chaotic resonance; plasticity.
  DOI: 10.1504/IJBIC.2025.10074354
   
  
• Robust Adaptive Iterative Learning Control for Nonlinear Discrete-Time System in Fading Environments with Joint Multiplicative-Additive Effects  
  by Yunshan Wei, Chengxi Liang, Kai Wan, Xingfeng Cai, Xinru Liu 
  Abstract: This study investigates the robustness of discrete-time adaptive iterative learning control (AILC) under fading channels for nonlinear dynamical systems with multiplicative and additive channel noises. The output and the input fading channel are considered integral components of the system during the analysis process, and thus, the system is reconstructed. Each component of system outputs suffers different multiplicative noise. The framework jointly addresses stochastic multiplicative and additive randomness effects in signal propagation. The variable tracking targets and error dead zones is taken into account in AILC design. The robustness characteristics of the developed AILC methodology are systematically examined. The validity of the developed approach is confirmed through systematic numerical simulations.
  Keywords: Adaptive iterative learning control; Fading channels; Robustness; Discrete-time systems; Dead-zone.
  DOI: 10.1504/IJBIC.2025.10074473
   
  
• Research on Parallelisation Prediction for the Size of Industrial Economies based on FA-SSA Optimisation SVM  
  by Xiaofang Shi 
  Abstract: Aiming at the problems of slow prediction speed and low accuracy of industrial economy scale, this paper proposes a prediction model based on SVM (Support Vector Machine) and parallel framework. Firstly, SSA (Sparrow Search Algorithm) algorithm improved by elite inverse strategy and firefly disturbance strategy is adopted to optimize the kernel function parameter and penalty factor C of SVM model, and the optimal parameters of SVM model are obtained by using the MapReduce parallel computing framework, which improves the prediction speed and accuracy of industrial economic scale. The results reveal that the mean absolute error, mean square error and explained variance of the proposed method are 0.31, 0.17 and 0.85, respectively. Therefore, the proposed model can be applied to the actual industrial economy scale prediction.
  Keywords: size of industrial economies; SVM model; sparrow search algorithm; parallel framework.
  DOI: 10.1504/IJBIC.2025.10074479
   
  
• A Multisensory Direction Cues Integration Model based on Continuous Attractor Neural Networks  
  by Jinhan Yan, Naigong Yu 
  Abstract: Accurate heading perception is essential for spatial navigation. The mammalian brain integrates self-motion cues from multiple sensory modalities in a Bayesian manner to achieve more reliable spatial localisation, providing important inspiration for robot navigation. This paper proposes a multisensory integration model based on continuous attractor neural networks (CANNs) to fuse directional cues from visual optical flow and vestibular system. Prior to integration, we perform a decoupling analysis of optical flow induced by self-motion and design a simple method to estimate visual angular velocity quickly. Separate CANNs are then constructed to perform angular path integration for both visual and vestibular inputs, followed by multisensory integration. Experiments on simulation and real-world datasets demonstrate that the proposed model achieves accurate and reliable direction estimation while maintaining low computational cost and strong noise resistance, outperforming several advanced methods. This work provides a brain-inspired solution for efficient and reliable navigation.
  Keywords: Bio-inspired navigation; head direction cells; continuous attractor neural network; multisensory integration; angular velocity.
  DOI: 10.1504/IJBIC.2025.10074549
   
  
• Enhanced Water Body Segmentation Using Optimized DeepLabV3+ and YOLOv7 on High-Resolution Satellite Imagery  
  by A. Kalaiselvi, T. Jarin, P. Sreeja, Rajesh V 
  Abstract: Efficient segmentation of water bodies from satellite images is crucial for environmental monitoring, resource management, and flood prediction. Traditional machine learning methods face challenges such as manual feature extraction and complex spectral analysis. This work introduces an advanced model that integrates optimised DeepLabV3+ and YOLOv7 to enhance the segmentation of water bodies using high-resolution satellite imagery. The model employs DeepLabV3+ enhanced with an improved mantis search algorithm (IMWSA) to better delineate water boundaries. Subsequently, YOLOv7 assists in localising and classifying water regions. This combination harnesses the robust segmentation capabilities of DeepLabV3+ with the rapid detection potential of YOLOv7, resulting in improved accuracy and efficiency. Comparative analysis with two benchmark datasets demonstrates the models superiority, achieving accuracies of 99.5% and 99.02%. This optimised approach is pivotal for sustainable water management and disaster mitigation, providing a significant advancement in water body segmentation and monitoring
  Keywords: satellite images; segmentation of water bodies; Optimized DeepLabV3+; YOLOv7.
  DOI: 10.1504/IJBIC.2025.10074607