International Journal of Bio-Inspired Computation (12 papers in press)

Regular Issues

• Determinate Node Selection for Semi-supervised Classification Oriented Graph Convolutional Networks  
  by Yao Xiao, Ji Xu, Yang Jing, Li Shaobo, Guoying Wang 
  Abstract: Graph convolutional networks (GCNs) have been proved successful in the field of semi-supervised node classification by extracting structural information from graph data. However, the random selection of labelled nodes used in GCNs may lead to unstable generalisation performance of GCNs. In this paper, we propose an efficient method for the deterministic selection of labelled nodes: the determinate node selection (DNS) algorithm. The DNS algorithm identifies two categories of representative nodes in the graph through structural analysis of the leading tree information granules: typical nodes and divergent nodes. These labelled nodes are selected by exploring the structure of the graph and determining the ability of the nodes to represent the distribution of data within the graph. The DNS algorithm can be applied quite simply on GCNs, and a wide range of semi-supervised graph neural network models for node classification tasks. Through extensive experimentation, we have demonstrated that the incorporation of the DNS algorithm leads to a remarkable improvement in the average accuracy of the model and a significant decrease in the standard deviation simultaneously, as compared to the vanilla method without a DNS module.
  Keywords: graph convolutional networks; granular computing; semi-supervised learning; node classification.
  DOI: 10.1504/IJBIC.2024.10062817
   
  
• Fitness Function with Two Rankings Embedded in a Push and Pull Search Framework for Constrained Multi-objective Optimisation Problems  
  by Kangshun Li, Jiaxin Xu, Shumin Xie, Hui Wang 
  Abstract: Optimizing objectives and satisfying constraints present significant challenges in solving constrained multi-objective optimisation problems. In this paper, we propose an algorithm that incorporates the push-and-pull search framework and a two-ranking fitness function named ToR-PPS. The algorithm is divided into three stages: the push stage, transitional stage, and pull stage. In the push stage, the population is directed toward the unconstrained Pareto front, without consideration of constraints. In the transitional stage, a diversity expansion strategy is proposed to optimise the diversity of the population. In the pull stage, the fitness function with two rankings is utilised to pull the population toward the constrained Pareto front. Experiments are conducted to compare the algorithm with five state-of-the-art constrained multi-objective optimisation evolutionary algorithms on three benchmark suites. The results clearly illustrate the superiority and efficiency of the algorithm.
  Keywords: constrained multi-objective optimization problems; evolutionary algorithms; push and pull search framework; transitional stage; diversity.
  DOI: 10.1504/IJBIC.2024.10062987
   
  
• Improved Harmonic Spectral Envelop Extraction for Singer Classification with Hybridised Model  
  by Balachandra Kumaraswamy  
  Abstract: The singing voice has an effect on humans with the addition of expressions, lyrics, and instruments. It is easier for human beings to distinguish the singing tone of voice from a specified auditory clip owing to an individual's perceptual tools and audible physiology. On the other, without human intervention, it is not simpler to identify non-vocal portions, vocal portions, feelings, and singers from the related signal owing to intrinsic complications. This proposed a new singer classification mechanism with four stages: pre-processing, vocal segmentation, feature extraction, and classification. Initially, first stage, an improved convolutional neural network (CNN) is deployed for the segmentation of the vocal part. Further, features like zero crossing rate (ZCR), Mel-frequency cepstral coefficients (MFCCs), vibration estimation and improved harmonic spectral envelope are derived to bidirectional gated recurrent unit (BI-GRU) and long short-term memory (LSTM). The results from LSTM and BI-GRU are median and the final result is attained.
  Keywords: singer classification; zero crossing rate; ZCR; convolutional neural network; improved CNN; bidirectional gated recurrent unit; BI-GRU; long short-term memory; LSTM.
  DOI: 10.1504/IJBIC.2023.10063534
   
  
• Bio-inspired Optimization Algorithms in Medical Image Segmentation: A Review  
  by Tian Zhang, Ping Zhou, Shenghan Zhang, Shi Cheng, Lianbo Ma, Huiyan Jiang, Yu-Dong Yao 
  Abstract: Medical image segmentation (MIS) is a primary task in medical image processing, with a great application prospect in medical image analysis and clinical diagnosis and treatment. However, MIS becomes a challenge due to the noisy imaging process of medical imaging devices and the complexity of medical images. Against this backdrop, the broad success of bio-inspired optimisation algorithms (BIOAs) has prompted the development of new MIS approaches leveraging BIOAs. As the first review of BIOAs for MIS applications, we present a comprehensive review of this recent literature, including genetic algorithm, particle swarm optimisation, ant colony optimisation, and artificial bee colony for blood vessel, organ, and tumour segmentation. We investigate the image modality and datasets that are used, discuss the application status of the four algorithms in MIS and address further research directions considering the advantages and disadvantages of each algorithm.
  Keywords: bio-inspired optimisation; genetic algorithm; particle swarm optimisation; PSO; ant colony optimisation; ACO; artificial bee colony; ABC; medical image segmentation; MIS.
  DOI: 10.1504/IJBIC.2024.10064036
   
  
• Research on estimation of permeability coefficients in microbial geotechnical soils based on data-driven models  
  by Mayao Cheng, Linsheng Chen 
  Abstract: Microbial geotechnical soil permeability coefficient estimation prediction is extremely valuable for the development of soil engineering. The study proposes an integrated data-driven model combining three base learners, RVM, ANFIS and iTLBO-ELM, assigning corresponding weights to each base learner through the PLS integrated model combination model, and applying the model to the prediction of microbial geotechnical soil permeability coefficient estimation. The MAE of the proposed integrated model has lower values compared to the single model, but the two metrics MAPE and RMSE are not the lowest; however, the integrated model outperforms both iTLBO-ELM and RVM in terms of MAPE, and the estimated predictions of permeability coefficients for November data are better than those for May. For iTLBO-ELM and RVM, the MAPE of PLS decreased by 5.51% and 1.56% respectively in May and 3.46% and 1.24% respectively in October. The integrated data-driven model proposed in the study can effectively achieve the estimated prediction of microbial geotechnical soil permeability coefficients and facilitate the intelligent acquisition of engineering permeability coefficients.
  Keywords: data-driven models; microorganisms; geotechnical land; permeability coefficients.
  DOI: 10.1504/IJBIC.2024.10064267
   
  
• Adaptive bald eagle search algorithm with elite swarm guiding and population memory crossover  
  by Dongmei Zhang, Xiaolei Liang, Yulian Cao, Mengdi Zhang, Di Xiao 
  Abstract: Bald eagle search (BES) algorithm is easy to fall into local optimums and the population diversity declines rapidly. To address the problems, an adaptive bald eagle search (ABES) algorithm with elite swarm guiding and population memory crossover is proposed. At the selecting stage, an elite population information is provided to replace a single information to guide location updating. At the searching stage, a population memory crossover strategy is proposed. The swooping stage is deleted in ABES due to search redundancy. Furthermore, the key parameters of ABES are designed to adjust adaptively to improve the population search ability. Finally, the test suite of CEC-2013 and three real-world engineering design problems are taken to test the performance of ABES, compared with seven state-of-art algorithms. The experimental results show that ABES has a much better comprehensive performance on convergence and local search capability than other selected algorithms.
  Keywords: bald eagle search algorithm; elite swarm guidance; population memory; crossover recombination; adaptive.
  DOI: 10.1504/IJBIC.2024.10064329
   
  
• Improved Dirichlet Mixture Model Clustering Algorithm for Medical Data Anomaly detection  
  by Lili Wu, Majid Khan Majahar Ali, F.A.M. Peishan, Tian Ying, Tao Li 
  Abstract: In order to address the issue of identifying over-diagnosis and anomaly expenses in the healthcare service process, a local outlier mining clustering algorithm (ILOF-DPMM) is proposed by combining the clustering-based local outlier factor (CBLOF) algorithm with Dirichlet mixture model (DPMM). By extracting the patient's hospitalisation records from the medical record homepage, the influencing factors of hospitalisation costs for different disease types are classified, and the random forest method is used to reduce the feature dimension by disease type. The feature extraction and dimensionality reduction methods adopted by this algorithm effectively cluster medical insurance expense data. When calculating the LOF value of data, using a weighted calculation method based on the similarity of discrete and continuous features can more accurately detect abnormal data points in the data set, and has the ability to detect new data in real time, thus improving detection accuracy and efficiency.
  Keywords: over-diagnosis; anomaly expenses; anomaly detection; DPMM; CBLOF.
  DOI: 10.1504/IJBIC.2024.10064803
   
  
• A Genetic Optimisation Model for Energy Conservation of Circulating Water Pump Station with Variable Speed Pumps  
  by Yirun He, Siqi Wu, Qianyu Cheng, ChenYu Tian, Qi Deng, Qi Kang 
  Abstract: As vital energy-consuming equipment of the industrial cooling circulating water system, scientific scheduling of the circulating water pump station (CWPS) is crucial for energy conservation. In this paper, we propose a genetic optimisation model. An optimal scheduling model is established to minimise power consumption, considering the production demand and pumps’ high-efficiency area constraints. Branch water pipe characteristic curves are introduced to determine the accurate pump operating condition. A multi-strategy genetic algorithm (MSGA) is proposed for the strict production demand constraints and the deficiency of complex constraint processing techniques. The MSGA screens feasible solutions by simply judging and achieves infeasible region information utilisation and search strategy adaptive adjustment by the sequence-based fitness construction, multi-mutation and adaptive control parameters strategies. The case results show that the proposed model can significantly reduce power consumption while improving pump efficiency over the original operation scheme of CWPS.
  Keywords: circulating water pump station; CWPS; energy conservation; genetic optimisation; optimal scheduling; multi-strategy genetic algorithm; MSGA.
  DOI: 10.1504/IJBIC.2024.10064901
   
  
• Design of a Bioinspired Augmented Model for Prediction of LD Students via Online Behaviour Analysis  
  by Masooda Modak, Prachi G, Sasi M 
  Abstract: Identification of students with learning disabilities (LD) requires analysis of various parameters, including students' analytical quotient, logical reasoning, mathematical analysis and Language processing capabilities, etc. Models like convolutional neural networks (CNN), recurrent NNs (RNNs), etc. are unable to identify micro patterns from the input dataset, which limits their context-specific performance and applicability. To overcome this limitation, a gamification-based analysis model using augmented bioinspired computing is proposed. The proposed model initially collects a large dataset from both LD and non-LD students to train a genetic algorithm (GA)-based feature selection model. These features were evaluated for multiple logical and analytical categories. Based on this analysis, a particle swarm optimiser (PSO) was deployed, assisting in the selection of classifier configurations to identify LD students. The model was tested on a custom manual dataset of over 1,000 students in grades 6th, 7th, 8th. Its accuracy performance was observed to be 6.5% better, while precision and recall were observed to be 5.4% and 3.9% better when compared with various state-of-the-art methods respectively.
  Keywords: learning disability; genetic algorithm; particle swarm optimiser; PSO; identification; research design and utilisation; convolutional neural networks; CNN.
  DOI: 10.1504/IJBIC.2024.10065089
   
  
• Pheromone-Inspired Multiple Moving Targets Search Method for Swarm Unmanned Aerial Vehicles in Environments with Unknown Obstacles  
  by Mao Wang, Shaowu Zhou, Hongqiang Zhang, Lianghong Wu 
  Abstract: A multiple moving targets search problem for swarm UAVs in environments with unknown obstacles is studied. The search is divided into roaming search and collaborative search; the multitarget search algorithm consists of task allocation, roaming search, collaborative search and obstacle avoidance. To convert between collaborative search and roaming search, a distance-based dynamic task allocation strategy is proposed. A confidence area pheromone for roaming search is proposed to reduce the repeated search times conducted in the same areas. Probabilistic finite PSO is proposed to adapt to search for moving targets in collaborative search. Furthermore, a boundary scanning-based obstacle avoidance strategy is improved to achieve efficient obstacle avoidance for UAVs in a grid environment. Based on the above, a multiple moving-target search algorithm mode is constructed. This mode shows better performance than existing methods as verified through simulation experiments, and provides a helpful alternative in postdisaster search, and other search fields.
  Keywords: swarm unmanned aerial vehicles; multiple moving targets search; confidence area pheromone; probabilistic finite particle swarm optimisation; PFPSO.
  DOI: 10.1504/IJBIC.2024.10065092
   
  
• QGE-VMC: A QoS-and-Global-energy aware VM Consolidation Strategy for Data Centers  
  by Hao Feng, Tianqin Zhou 
  Abstract: Traditional energy consumption optimisation strategies that do not consider the quality of service (QoS) may lead to customer loss and large profit loss. To solve the above problems, we propose a QoS-and-global-energy aware virtual machine (VM) consolidation strategy (QGE-VMC) for data centres. First, during the overload detection phase, we dynamically set overload thresholds for servers based on the historical utilisation of VMs to prevent host overload, which effectively reduces service level agreement (SLA) violations and improves QoS. Second, in the VM selection phase, we considered reducing both the time and number of VM migration, which reduces the time when servers are overloaded. Third, during the target host selection phase, we use a heuristic algorithm to make the selected servers ensure that the global energy consumption of the data centre rises to the minimum and will not be overloaded, thereby ensuring QoS while reducing data centre energy consumption.
  Keywords: data centre; virtual machine consolidation; service level agreement violations; energy optimisation.
  DOI: 10.1504/IJBIC.2024.10065093
   
  
• Emergence Mechanism of Lion Pride Cooperative Behaviours for Task Allocation Problem and its Implementation  
  by Bowen Wu, Renbin Xiao, Zhenhui Feng 
  Abstract: Task allocation is a key challenge in various fields due to increasing tasks and UAVs, leading to high computational costs. To tackle this, we employ swarm intelligence inspired by cooperative behaviours in lions, which is decentralised and adaptive. Initially, we abstract lions' cooperation behaviours and establish a formal model based on attraction-repulsion mechanisms. Then, we use multi-agent technology to simulate these behaviours, exploring parameter effects on rationality, scalability, and adaptation. We apply this model to UAV task allocation, proposing a distributed self-organising algorithm validated through simulations. Finally, we analyse factors influencing lions' cooperative behaviours, demonstrating the efficacy of the attraction-repulsion mechanism in task allocation.
  Keywords: attraction-repulsion; collective intelligence; cooperative behaviour; emergence mechanism; lions; task allocation; UAVs.
  DOI: 10.1504/IJBIC.2024.10065516