Bio-inspired Algorithms Research Articles

A novel multi-strategy ameliorated quasi-oppositional chaotic tunicate swarm algorithm for global optimization and constrained engineering applications

Over the last few decades, a number of prominent meta-heuristic algorithms have been put forth to address complex optimization problems. However, there is a critical need to enhance these existing meta-heuristics by employing a variety of evolutionary techniques to tackle the emerging challenges in engineering applications. As a result, this study attempts to boost the efficiency of the recently introduced bio-inspired algorithm, the Tunicate Swarm Algorithm (TSA), which is motivated by the foraging and swarming behaviour of bioluminescent tunicates residing in the deep sea. Like other algorithms, the TSA has certain limitations, including getting trapped in the local optimal values and a lack of exploration ability, resulting in premature convergence when dealing with highly challenging optimization problems. To overcome these shortcomings, a novel multi-strategy ameliorated TSA, termed the Quasi-Oppositional Chaotic TSA (QOCTSA), has been proposed as an enhanced variant of TSA. This enhanced method contributes the simultaneous incorporation of the Quasi-Oppositional Based Learning (QOBL) and Chaotic Local Search (CLS) mechanisms to effectively balance exploration and exploitation. The implementation of QOBL improves convergence accuracy and exploration rate, while the inclusion of a CLS strategy with ten chaotic maps improves exploitation by enhancing local search ability around the most prospective regions. Thus, the QOCTSA significantly enhances convergence accuracy while maintaining TSA diversification. The experimentations are conducted on a set of thirty-three diverse functions: CEC2005 and CEC2019 test functions, as well as several real-world engineering problems. The statistical and graphical outcomes indicate that QOCTSA is superior to TSA and exhibits a faster rate of convergence. Furthermore, the statistical tests, specifically the Wilcoxon rank-sum test and t-test, reveal that the QOCTSA method outperforms the other competing algorithms in the domain of real-world engineering design problems.

Development of a solution search method using artificial intelligence

The object of the study is decision support systems. The problem of increasing decision-making efficiency in conditions of uncertainty and a set of different parameters was solved using a bio-inspired algorithm. The subject of the study is the decision-making process in management problems using the heron flock algorithm, the improved genetic algorithm and evolving artificial neural networks. A solution search method using the improved heron flock algorithm is proposed. The study is based on the heron flock algorithm to find a solution regarding the object state. Evolving artificial neural networks are used to train the heron flock algorithm, and an advanced genetic algorithm is used to select the best individuals of the heron flock. The method has the following sequence of actions: – input of initial data; – setting agents on the search plane; – numbering heron agents in the flock; – setting the initial velocity of heron agents; – waiting strategy for heron agents; – aggressive strategy; – checking the discriminatory condition; – selection of the best individuals from the heron flock; – ranking and sorting the obtained solutions; – training heron knowledge bases; – determining the amount of necessary computing resources of the intelligent decision support system. The originality of the proposed method consists in setting heron agents taking into account the uncertainty of the initial data, the noise degree of data about the analysis object state. The method makes it possible to reduce the time for decision-making at the level of 22–26 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interest of solving national security problems

Evalution of neurodiagnostic insights for enhanced evaluation and optimization of badminton players' physical function via data mining technique

This research presents a novel method for objectively evaluating college badminton players' physical function levels. It examines current evaluation methods before proposing a novel model that combines Particle Swarm Optimization (PSO) with Backpropagation (BP) neural networks and data mining. The model establishes an evaluation index system that considers physical form, function, quality, and neural mechanisms. The study uses PSO-BP neural networks to adjust indicator weights for more accurate ratings. This recurrent improvement reduces errors while increasing prediction ability, resulting in accurate assessments of athletes' physical talents and neurological insights. The model's efficiency is proved by low mistakes and high accuracy results, which are critical for training optimization and injury avoidance. The combination of PSO optimization and BP neural networks offers robustness across various athlete profiles and training scenarios. This method improves physical function evaluation in badminton and has wider implications for sports science and performance analytics. This study uses bio-inspired computing and machine learning to emphasize the relevance of data-driven techniques in enhancing athlete assessments for better training outcomes and general well-being.

Biological plausible algorithm for seizure detection: Toward AI-enabled electroceuticals at the edge

Nearly 1% of people worldwide suffer from epilepsy. Electroencephalogram (EEG)-based diagnostics and monitoring tools, such as scalp EEG, subscalp EEG, stereo EEG, or sub/epi-dural EEG recordings [also known as electrocorticography (ECoG)], are widely used in different settings as the gold standard techniques to perform seizure identification, localization, and more primarily in epilepsy or suspected epilepsy in patients. Techniques such as subscalp EEG and ECoG offer long-term brain interaction, potentially replacing traditional electroceuticals with smart closed-loop therapies. However, these systems require continuous on-device training due to real-time demands and high power consumption. Inspired by the brain architecture, biologically plausible algorithms, such as some neuromorphic computing, show promise in addressing these challenges. In our research, we utilized liquid time-constant spiking neural networks with forward propagation through time to detect seizures in scalp-EEG. We trained and validated our model on the Temple University Hospital dataset and tested its generalization on out-of-sample data from the Royal Prince Alfred Hospital (RPAH) and EPILEPSIAE datasets. Our model achieved high area under the receiver operating characteristic curve (AUROC) scores of 0.83 in both datasets. We assessed the robustness by decreasing the memory size by 90% and obtained an overall AUROC of 0.82 in the RPAH dataset and 0.83 in the EPILEPSIAE dataset. Our model showed outstanding results of 3.1 μJ power consumption per inference and a 20% firing rate during training. This allows for incorporating bio-inspired efficient algorithms for on-device training, tackling challenges such as memory, power consumption, and efficiency.

Cleaner fish optimization algorithm: a new bio-inspired meta-heuristic optimization algorithm

Cleaner fish optimization algorithm: a new bio-inspired meta-heuristic optimization algorithm

Particle swarm optimization of electric power generation via green hydrogen considering the performance of the electrolyzer cell

The replacement of energy generation models, from fossil fuels to carbon neutral ones, requires a significant transformation in the generation and consumption of electricity. This work highlights Green Hydrogen as an essential potential to intensify the energy generation process based on renewable sources. The article presents two studies on energy generation models, via Green Hydrogen: Case (i), study of the behavior of the Electric Activation Voltage with ohmic loss influenced by the control variables Electric Current and Operating Temperature. Case (ii), study of the behavior of the Electric Activation Voltage with ohmic loss influenced by the control variables Electric Current and Operating Pressure. For both cases, a computational model stands out, the Proton Exchange Membrane electrolyzer cells, indicated for the generation of renewable energy, from Green Hydrogen. For both cases, a Bioinspired Computing methodology was applied in order to optimize the process. The Optimization technique means, among the various definitions, determining the best set of results, considering the restrictions of the studied phenomenon. Studies indicate that this strategy is increasingly intrinsic to our reality. This paper advocates Particle Swarm Optimization as a mechanism capable of finding the best set of solutions for mathematical models related to Energy Systems. The Swarm Algorithm can be represented as a set of particles that navigate through a space of solutions, where the main element of this method is the exchange of information between the particles of the population, which allows collective mobilization towards the optimal solution. In this case specifically, the values of Electrical Voltage, Electrical Current, Operating Temperature and Pressure form the Particle Examination. The results were exceptional for the Green Hydrogen energy generation system studied. The Particle Swarm Optimization Algorithm was applied to the objective function of multiple variables and satisfactorily incorporated the restrictions and conditions of the electrolysis system. The measurements obtained highlight that the Optimal Values are lower than the Average Values, for each variable analyzed in energy generation, in an electrolyzer. The solution achieved will be of great applicability in the study of energy efficiency in structures linked to this relevant category of renewable energy generation.

Optimization based on the smart behavior of plants with its engineering applications: Ivy algorithm

This study presents a powerful and new modeling variant of bio-inspired algorithms, namely, the Ivy algorithm (IVYA), drawn from the growth patterns of Ivy plants. The algorithm simulates the coordinated and ordered population growth and the spreading and evolution of Ivy plants. The growth rate of Ivy plants is modeled using a differential equation and a data-intensive experimental process. The algorithm utilizes the knowledge of nearby Ivy plants to determine the direction of growth. Additionally, the algorithm mimics the behavior of Ivy plants in nature by choosing the closest and most vital neighbor for self-improvement. The IVYA's unique characteristics of preserving population diversity and its simplicity and flexibility allow for easy modification and extension, thus enabling researchers and practitioners to explore various modifications and techniques to enhance its performance and capabilities. These are the basic needs in optimizing engineering problems. The IVYA is compared with ten other algorithms on 26 classical test functions, demonstrating superior performance. Furthermore, the effectiveness of IVYA is shown by solving 12 engineering optimization problems and comparing the results with various optimization algorithms. The experimental results highlight the efficacy and competitiveness of the IVYA in solving optimization problems. Consider that the source code of the IVYA is publicly available at https://www.optim-app.com/projects/ivya.

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q-learning is a reinforcement learning technique that combines Q-learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of the Orca predator algorithm to identify promising regions nearby in the search space, complemented by the fine-tuning capabilities of deep Q-learning to navigate these areas precisely. The practical application of this approach is evaluated using the high-dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy’s capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of the Orca predator algorithm against its native version and a set of state-of-the-art algorithms.

Bio-inspired algorithms for feature engineering: analysis, applications and future research directions

Purpose Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains. Design/methodology/approach Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022. Findings The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research. Originality/value The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

ENHANCED SURFACE QUALITY AND STRENGTH OF FDMed SPECIMENS USING BBD AND BIO-INSPIRED ALGORITHMS

This research investigated and optimized the parameters of the FDM process by employing bio-inspired algorithms for determining the optimal parameter settings in terms of surface quality and mechanical performance. Four important process parameters including layer thickness (0.11–0.33[Formula: see text]mm), part orientation (0–90∘), raster width (0.2–0.56[Formula: see text]mm), and the raster angle (0–60∘) at three variation levels were selected for fabricating the specimens (ABS material P430) using the statistical Box–Behnken design. ANOVA analysis and multiple regression analysis were used to fit the experimental data to a second-order polynomial equation. Through, the RSM analysis, the layer thickness is the key important factor that accounts for all of the responses. The fracture behavior of specimens was examined using a scanning electron microscope (SEM). From the SEM analysis, a substantial amount of plastic deformation on the fracture surface indicative of craze cracking is visible from a 0∘ orientation, indicating a totally ductile fracture mechanism. Then, three swarm intelligence algorithms such as Tasmanian Devil Optimization (TDO), Remora Optimization Algorithm (ROA), Tuna Swarm Optimization (TSO) were implemented to optimize the input parameters that would lead to minimum surface roughness and maximum tensile strength. Experimental data and predicted values varied between 1.64% and 1.84%, as shown by verification experiments.

Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization

This study proposes a novel artificial protozoa optimizer (APO) that is inspired by protozoa in nature. The APO mimics the survival mechanisms of protozoa by simulating their foraging, dormancy, and reproductive behaviors. The APO was mathematically modeled and implemented to perform the optimization processes of metaheuristic algorithms. The performance of the APO was verified via experimental simulations and compared with 32 state-of-the-art algorithms. Wilcoxon signed-rank test was performed for pairwise comparisons of the proposed APO with the state-of-the-art algorithms, and Friedman test was used for multiple comparisons. First, the APO was tested using 12 functions of the 2022 IEEE Congress on Evolutionary Computation benchmark. Considering practicality, the proposed APO was used to solve five popular engineering design problems in a continuous space with constraints. Moreover, the APO was applied to solve a multilevel image segmentation task in a discrete space with constraints. The experiments confirmed that the APO could provide highly competitive results for optimization problems. The source codes of Artificial Protozoa Optimizer are publicly available at https://seyedalimirjalili.com/projects and https://ww2.mathworks.cn/matlabcentral/fileexchange/162656-artificial-protozoa-optimizer.

Innovative Exploration of a Bio-Inspired Sensor Fusion Algorithm: Enhancing Micro Satellite Functionality through Touretsky's Decentralized Neural Networks

Insect-inspired sensor fusion algorithms have presented a promising avenue in the development of robust and efficient systems, owing to the insects' ability to process numerous streams of noisy sensory data. The ring attractor neural network architecture has been identified as a noteworthy model for the optimal integration of diverse insect sensors. Expanding on this, our research presents an innovative bio-inspired ring attractor neural network architecture designed to augment the performance of microsatellite attitude determination systems through the fusion of data from multiple gyroscopic sensors.Extensive simulations using a nonlinear model of the microsatellite, while incorporating specific navigational disturbances, have been conducted to ascertain the viability and effectiveness of this approach. The results obtained have been superior to those of alternative methodologies, thus highlighting the potential of our proposed bio-inspired fusion technique. The findings indicate that this approach could significantly improve the accuracy and robustness of microsatellite systems across a wide range of applications.

Optimal hydroelectric energy utilization with ATDOA: a case study of the Bumbuna Dam

Abstract Given that hydroelectric energy is regarded as the tertiary means of electricity production and furthermore the most crucial renewable energy producer globally, the imperative of maximizing the utilization of the vast and costly water resource, which depletion is increasingly conspicuous each passing day, is more pressing than before. In this study, the Advanced Tasmanian Devil Optimization Algorithm (ATDOA) is employed to optimize the utilization of hydroelectric energy derived from the Bumbuna Dam reservoir. This reservoir is situated in the Seli River, located in the Northern Province of Sierra Leone. The research period includes 138 months, covering the duration from October 2010 to April 2022. The Tasmanian Devil Optimization Algorithm, which is bio-inspired algorithm, has been shown to be an effective optimization technique in multiple scholarly researches. The decision variables in the hydroelectric energy optimization model from the reservoir are the optimal release values of the monthly hydroelectric output from the reservoirs of the dams. After verifying the correctness of the ATDOA algorithm by using several standard benchmark functions, a model was developed for optimal hydroelectric utilization of the Bumbuna Dam.

Multi-Fault Diagnosis Based on Hybrid Bio-Inspired Algorithm ACO-GA

The fault detection and isolation (FDI) procedure increases the assurance of quality, reliability, and safety of industrial systems. Several faults may appear simultaneously, and the purpose of multi-fault diagnosis is to identify and locate these multiple faults. This work is particularly interested in the diagnosis based on the structural analysis of the system; residuals can be generated and used as fault indicators by model-based fault detection techniques. The isolation is dependent on the structure of the fault signature matrix. A new fault signature that represents the superposition of the fault is produced by simultaneous fault effects, resulting in an additional column in an extended signature matrix. This remedy is rather combinatorial. This research focuses on two methods to isolate multiple faults: (1) A modified enumerative method; (2) A hybrid ant colony optimization algorithm-genetic algorithm (Hybrid ACO-GA) is adapted to the MFD problem which has the advantage of a better research as well as the hybridization with GA.

Bio-Inspired Antagonistic Differential Polarization Algorithm for Heading Determination in Underwater Low-Light Environments

Bio-Inspired Antagonistic Differential Polarization Algorithm for Heading Determination in Underwater Low-Light Environments

Unmanned aerial vehicle path planning in a 3D environment using a hybrid algorithm

The optimal unmanned aerial vehicle (UAV) path planning using bio-inspired algorithms requires high computation and low convergence in a complex 3D environment. To solve this problem, a hybrid A*-FPA algorithm was proposed that combines the A* algorithm with a flower pollination algorithm (FPA). The main idea of this algorithm is to balance the high speed of the A* exploration ability with the FPA exploitation ability to find an optimal 3D UAV path. At first, the algorithm starts by finding the locally optimal path based on a grid map, and the result is a set of path nodes. The algorithm will select three discovered nodes and set the FPA's initial population. Finally, the FPA is applied to obtain the optimal path. The proposed algorithm's performance was compared with the A*, FPA, genetic algorithm (GA), and partical swarm optimization (PSO) algorithms, where the comparison is done based on four factors: the best path, mean path, standard deviation, and worst path length. The simulation results showed that the proposed algorithm outperformed all previously mentioned algorithms in finding the optimal path in all scenarios, significantly improving the best path length and mean path length of 79.3% and 147.8%, respectively.

Enhancing MANET Performance: A Novel Approach Through Nature-Inspired Scheduling Algorithms

In the rapidly evolving landscape of Mobile Ad Hoc Networks (MANETs), the quest for optimal performance through efficient scheduling remains paramount. This paper, titled "Enhancing MANET Performance: A Novel Approach Through Nature-Inspired Scheduling Algorithms," introduces an innovative methodology that leverages the intricate mechanisms of nature to address the complex challenges inherent in MANET scheduling. Drawing inspiration from the adaptive behaviors observed in natural systems, we have developed a suite of nature-inspired algorithms—encompassing the genetic algorithm, particle swarm optimization, and ant colony optimization—tailored specifically to enhance the scheduling efficiency within the dynamic and decentralized context of MANETs.
 Our research undertakes a comprehensive evaluation of these algorithms against traditional scheduling methods, showcasing a notable improvement in key performance metrics such as network throughput, latency, and energy consumption. The novel contribution of this study lies in the adaptation and optimization of these bio-inspired algorithms for the unique demands of MANET scheduling, culminating in the development of a proprietary analytical model that significantly outperforms existing strategies. Through a rigorous comparative analysis, our results illuminate the superiority of our approach, demonstrating not only enhanced network efficiency but also improved scalability and reliability under varying conditions.
 The implications of this research extend beyond the immediate enhancements to MANET performance, proposing a paradigm shift in how scheduling challenges are approached within ad hoc networks. By bridging the gap between biological principles and technological applications, this study paves the way for future innovations in network management, offering a blueprint for the development of more adaptive, robust, and efficient networking solutions.

Artificial rabbits optimization algorithm based tuning of PID controller parameters for improving voltage profile in AVR system using IoT

The power system is mainly affected by transient situations caused by switching heavy loads. The system may become unstable when transient situations occur. The power system should be able to perform continuous operation to maintain its voltage within acceptable limits. To achieve more stability and increase its speed of response, an Automatic Voltage Regulator (AVR) system requires the inclusion of a controller. The AVR system in the generating station uses the PID controller to adjust the abnormal voltage caused by transient conditions. To maintain the nominal voltage level under all the load conditions in the system, a bio-inspired meta-heuristic algorithm called Artificial Rabbit Optimization (ARO) algorithm is proposed to tune the PID controller gain parameters and obtain the optimal gain, thereby the AVR system adjusts the generator terminal voltage to nominal levels. The ARO algorithm inspires natural survival techniques to improve the AVR performance by reducing errors. To maintain a stable voltage profile in a power system, this research mathematically models survival techniques using the Internet of Things (IoT) to obtain an optimal solution. As a result, all devices connected to the power network receive a stable voltage that ensures their voltage reliability. The effectiveness of the proposed algorithm for the AVR system is verified with the MATLAB R2022a model, and the statistics functions are implemented in the module of Pandas, Scipy and mathematical investigations done in Numpy. The proposed ARO algorithm achieves a better voltage profile with less than 12.63% maximum peak overshoot during the system's transient response. The proposed algorithm provides the fastest response and highest stability comparable to other optimisation algorithms.

Recommendation system using bio-inspired algorithms for urban orchards

According to the Food and Agriculture Organization of the United Nations (FAO), climate change is exponentially affecting agricultural production worldwide, with food prices expected to increase by up to 90 percent by 2030 and hunger and malnutrition rates to rise by 2050. This paper presents the development of a platform based on the Internet of Things (IoT) for monitoring urban gardens as a strategy to mitigate hunger, promote food sovereignty and circular economy in areas of food shortage. To this end, an Internet of Things (IoT) architecture is proposed and implemented that involves a social design layer that allows an effective transfer of knowledge to communities and a recommendation system based on evolutionary computation to optimize and maximize the productivity of urban orchards, and thus contribute to the 2030 agenda of the Sustainable Development Goals (SDGs). Finally, three experiments in urban gardens are shown to validate evolutionary computation and artificial intelligence models, such as multiple linear regression, genetic algorithms, ant colony algorithms and spatial estimation and inference algorithms such as the Kriging algorithm. The productivity of urban lettuce orchards is increased between 25 and 45%.

Oxide Ionic Neuro-Transistors for Bio-inspired Computing.

Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is much lower, but the human brain performs much better in many tasks such as pattern recognition and decision-making. Recently, ionic dynamics in oxide electrolyte-gated transistors have attracted increasing attention in the field of neuromorphic computing, which is more similar to the computing modality in the biological brain. In this review article, we start with the introduction of some ionic processes in biological brain computing. Then, electrolyte-gated ionic transistors, especially oxide ionic transistors, are briefly introduced. Later, we review the state-of-the-art progress in oxide electrolyte-gated transistors for ionic neuromorphic computing including dynamic synaptic plasticity emulation, spatiotemporal information processing, and artificial sensory neuron function implementation. Finally, we will address the current challenges and offer recommendations along with potential research directions.