Bio-inspired Metaheuristic Algorithm Research Articles

Nature-Inspired Metaheuristic Optimization for Control Tuning of Complex Systems.

In this contribution, a methodology for the optimal tuning of controllers of complex systems based on meta-heuristic techniques is proposed. Two bio-inspired meta-heuristic optimization algorithms -the Antlion Optimizer (ALO) and the Whale Optimization Algorithm (WOA)- have been applied to two different dynamic systems: the Hoop & Ball electromechanical system, a system where a linearized description is adequate; and to a Wind Turbine-Generator-Rectifier, as an example of a complex non-linear dynamic system. The performance of the ALO and WOA techniques for the tuning of conventional PID controllers is evaluated in relation to the number of agents nS and the maximum number of iterations nMaxIter; given the stochastic nature of both methods, repeatability is also addressed. Finally, the computational effort required for their implementation is considered. By analyzing the obtained metrics, it is observed that both methods provide comparable results for the two systems considered and, therefore, the ALO and WOA techniques can complement each other by exploiting the advantages of each of them in controller tuning.

Optimization of Weight Reduction in Power Transmission Systems Using Bio-Inspired Algorithms and Composite Materials

Traditional optimization approaches for power transmission systems often optimize gear and shaft designs separately, rely on conventional materials, and face challenges handling mixed-variable problems involving discrete and continuous variables. These limitations hinder the potential for significant weight reduction and performance improvements in modern mechanical systems. This research presents an innovative optimization approach for reducing the gear pitch in a power transmission system comprising a shaft with two gears. By employing bio-inspired metaheuristic algorithms and utilizing materials common in additive manufacturing and composite materials, the study aims to minimize system weight while ensuring mechanical integrity. The optimization integrates gear and shaft design variables, accurately models mechanical constraints through penalty functions, and leverages the unique properties of advanced materials to enhance performance. In this work, we developed an integrated optimization model that overcomes the limitations of previous studies by combining advanced materials with bio-inspired algorithms to achieve significant weight reduction in gear systems. This approach effectively addresses the complexities of mixed-variable optimization and mechanical constraints, providing a practical and innovative solution for modern engineering applications.

Starfish optimization algorithm (SFOA): a bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers

Starfish optimization algorithm (SFOA): a bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers

Route Optimization for UVC Disinfection Robot Using Bio-Inspired Metaheuristic Techniques.

The COVID-19 pandemic highlighted the urgent need for effective surface disinfection solutions, which has led to the use of mobile robots equipped with ultraviolet (UVC) lamps as a promising technology. This study aims to optimize the navigation of differential mobile robots equipped with UVC lamps to ensure maximum efficiency in disinfecting complex environments. Bio-inspired metaheuristic algorithms such as the gazelle optimization algorithm, whale optimization algorithm, bat optimization algorithm, and particle swarm optimization are applied. These algorithms mimic behaviors of biological beings such as the evasive maneuvers of gazelles, the spiral hunting patterns of whales, the echolocation of bats, and the collective behavior of flocks of birds or schools of fish to optimize the robot's trajectory. The optimization process adjusts the robot's coordinates and the time it takes to stops at key points to ensure complete disinfection coverage and minimize the risk of excessive UVC exposure. Experimental results show that the proposed algorithms effectively adapt the robot's trajectory to various environments, avoiding obstacles and providing sufficient UVC radiation exposure to deactivate target microorganisms. This approach demonstrates the flexibility and robustness of these solutions, with potential applications extending beyond COVID-19 to other pathogens such as influenza or bacterial contaminants, by tuning the algorithm parameters. The results highlight the potential of bio-inspired metaheuristic algorithms to improve automatic disinfection and achieve safer and healthier environments.

Rabbit algorithm for global optimization

In this paper, a novel bio-inspired meta-heuristic algorithm, namely Rabbit Optimizer, is proposed. It draws inspiration from how rabbits survive in nature through foraging tactics, random hiding and rapid growth. The foraging strategy involves rabbits avoiding grazing near their dens, thus preventing their dens from being discovered. Random hiding refers to cunning rabbits maintaining three dens to reduce the chance of being eliminated. Additionally, rapid growth and strong reproductive abilities enable rabbits to thrive as the fittest species. Rabbit Optimizer employs three subgroups of male, female and child rabbits to conduct an efficient collaborative search under an elite learning strategy, achieving a good balance of exploration and exploitation. Key parameters like distance factor, balance factor and learning factor strike a balance between depth search and width searches, making Rabbit Optimizer versatile and flexible. The performance of Rabbit Optimizer was evaluated by handling the CEC2022 and various dimensions of the CEC05 test suites up to 5000. Using Friedman analysis, Rabbit Optimizer ranked first and was proven to be significantly superior to 13 well-known metaheuristic algorithms. Finally, it addressed ten problems from the CEC2020 to demonstrate its ability to solve real-world engineering optimization problems. Experimental results show that Rabbit Optimizer is effective and efficient for solving complex large-scale optimization problems.

Bottom-up discrete systematic modelling for analysis and prediction of future trends for land-sea environmental pollution systems

Addressing the challenge of marine environmental pollution requires accurate forecasting systems, yet current methods relying on artificial intelligence and big data face limitations due to insufficient and inconsistent marine data. This paper proposes an integrated bottom-up prediction system featuring a grey modeling module and an optimization module. The grey modeling module introduces a novel nonlinear nested systematic grey model, which employs multi-structure discretization to capture intricate and variable system changes by leveraging the dual nonlinearity of cumulative generated sequences and time trends. Nested solutions minimize prediction errors through discrete recursive equations with underlying-upper structures. The optimization module utilizes an efficient bio-inspired meta-heuristic algorithm, the Coati Optimization Algorithm, which balances global and local search strategies. The accuracy and robustness of the proposed model are enhanced by optimizing its nonlinear parameters through comparative analysis with other algorithms and conducting a sensitivity analysis on the number of iterations. The proposed prediction system is validated in representative Chinese provinces (Zhejiang, Shandong, and Hainan), demonstrating high prediction accuracy for both land-based and marine environmental pollution systems, with mean absolute percentage errors below 8% in both training and test sets. Projections indicate that by 2030, land-based source pollution intensities in these provinces will decrease by approximately 5.71%, 0.16%, and 1.64%, respectively, while marine pollution indices are expected to decline by 3.28%, 4.86%, and 2.89%. This study provides a valuable predictive tool for addressing the complexity, variability, and uncertainty of marine environmental pollution, while contributing to the promotion of land-sea integration and the protection of marine ecosystems.

Daily runoff forecasting using novel optimized machine learning methods

Accurate runoff forecasting is crucial for effective water resource management, yet existing models often face challenges due to the complexity of hydrological systems. This study addresses these challenges by introducing a novel bio-inspired metaheuristic algorithm, Artificial Rabbits Optimization (ARO), integrated with various machine learning (ML) models for runoff forecasting in the Carson and Chehalis River basins. The ARO-ML models are rigorously evaluated, demonstrating their superior ability to enhance forecasting accuracy compared to widely used ML techniques, including gradient boosting (GB), multi-layer perceptron (MLP), and K-nearest neighbor regression (KNN). In the Carson River, the GB model achieves the highest forecasting accuracy, which is significantly improved by ARO, resulting in a 24.8 % reduction in root mean square error (RMSE). The MLP model also benefits notably from ARO, with RMSE improvements of 4.8 % and a substantial 48.9 % reduction in mean absolute error (MAE). The ARO-optimized KNN model shows exceptional performance, surpassing the baseline KNN during testing. In the Chehalis River Basin, ARO integration leads to substantial improvements in both GB and MLP models across various performance metrics during training and testing. Crucially, the results indicate that in both river basins, rainfall in preceding days has the most significant influence on streamflow forecasts, followed by air temperature. This study advances hydrological forecasting by validating the efficacy of ARO-ML models, offering a robust framework for enhanced predictive accuracy and providing deeper insights into the key drivers of streamflow in diverse hydrological contexts.

Indirect hazard evaluation by the prediction of backbreak distance in the open pit mine using support vector regression and chicken swarm optimization

Backbreak is one of the undesirable phenomena in open-pit mines and causes several adverse hazards, such as lanslide, rock falling off and bench instability. Backbreak is influenced by many factors, such as rock properties, blasting design and local geology, so it is very difficult to assess and evaluate backbreak accurately. Therefore, controlling and accurate prediction of backbreak distance are crucial tasks to reduce hazards in open-pit mines. For this, soft computing-based techniques are considered to be an effective means, as they can integrate various sophisticated factors into a function to predict and evaluate backbreak distance. So, in this study, support vector regression (SVR) based techniques and three different types of bio-inspired meta-heuristic (BIMH) algorithms, such as chicken swarm optimization (CSO), whale optimization algorithm (WOA) and seagull optimization algorithm (SOA) are used to develop backbreak distance prediction models. The support vector regression is used as a regression tool and BIMH algorithms are used to optimize the hyper-parameters in the support vector regression. Four different types of evaluation metrics are utilized to assess the model performance, namely coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE) and variance account for (VAF). An integrated evaluation system is adopted to provide overall performance for each backbreak prediction scenario. It can be indicated that CSO-SVR based backbreak prediction models can procure the best comprehensive performance and also show the best calculation efficiency. Detailed results include R2, VAF, MSE and MAE equal to 0.99475, 0.034, 99.477 and 0.1553 for a testing set and 0.97450, 0.1633, 97.466, and 0.1914 for a training set which can be said to be an excellent prediction result. By doing this, the hazard risk induced by backbreak can be indirectly assessed. In addition, it is also found that some superior performance can be obtained in some evaluation metrics compared with previous studies which utilized the same backbreak dataset for prediction.

Fossa Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Engineering Applications

Fossa Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Engineering Applications

Enhancing spatial prediction of groundwater-prone areas through optimization of a boosting algorithm with bio-inspired metaheuristic algorithms

Groundwater resources are essential for ensuring a consistent water supply in many regions. Groundwater potential maps (GPMs) can be utilized in many ways to estimate the quantity, quality, and distribution of subsurface water, supporting the decision-making processes of numerous stakeholders. This study contributes to improving the accuracy of GPMs, focusing on implementing Geospatial Artificial Intelligence (GeoAI) models. For this purpose, the accuracy performance of the Extreme Gradient Boosting (XGBoost) algorithm is improved in this study. To do this, two such popular metaheuristic algorithms, i.e., invasive weed optimization (IWO) and biogeography-based optimization (BBO), are integrated into the XGBoost algorithm for modeling and spatial prediction of the areas prone to groundwater. Three models—XGBoost, XGBoost-IWO, and XGBoost-BBO—are implemented within the Python programming environments to execute spatial modeling and generate predictive maps. The evaluation of results unfolds in two stages: model validation and GPM validation. For the training data, the root mean square error (RMSE) and mean absolute error (MAE) indices were 0.165 and 0.121 for XGBoost, 0.13 and 0.087 for XGBoost-IWO, and 0.114 and 0.082 for XGBoost-BBO, respectively. The test data showed similar trends, with XGBoost yielding RMSE and MAE values of 0.424 and 0.295, XGBoost-IWO at 0.416 and 0.287, and XGBoost-BBO at 0.39 and 0.28. XGBoost-BBO, XGBoost-IWO, and XGBoost had a prediction accuracy higher than other models. The respective area under the curve (AUC) of GMPs using receiver operating characteristic (ROC) curves for XGBoost, XGBoost-IWO, and XGBoost-BBO were 81.8 %, 83.1 %, and 83.7 %. Using bio-inspired metaheuristic algorithms, the GPM accuracy rate has improved further. The study of groundwater resources demonstrated how geological feature extraction by GeoAI may help employ advanced techniques.

Bobcat Optimization Algorithm: an effective bio-inspired metaheuristic algorithm for solving supply chain optimization problems

Supply chain efficiency is a major challenge in today’s business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional efficiency strategies often struggle for resources for the complex and dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring to the random search nature of metaheuristic algorithms and emphasizing that no metaheuristic algorithm is the best optimizer for all optimization applications, the No Free Lunch (NFL) theorem encourages researchers to design newer algorithms to be able to provide more effective solutions to optimization problems. Motivated by the NFL theorem, the innovation and novelty of this paper is in designing a new meta-heuristic algorithm called Bobcat Optimization Algorithm (BOA) that imitates the natural behavior of bobcats in the wild. The basic inspiration of BOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. The theory of BOA is stated and then mathematically modeled in two phases (i) exploration based on the simulation of the bobcat’s position change while moving towards the prey and (ii) exploitation based on simulating the bobcat’s position change during the chase process to catch the prey. The performance of BOA is evaluated in optimization to handle the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results show that BOA has a high ability in exploration, exploitation, and balance them during the search process in order to achieve a suitable solution for optimization problems. The results obtained from BOA are compared with the performance of twelve well-known metaheuristic algorithms. The findings show that BOA has been successful in handling the CEC 2017 test suite in 89.65, 79.31, 93.10, and 89.65% of the functions for the problem dimension equal to 10, 30, 50, and 100, respectively. Also, the findings show that in order to handle the CEC 2020 test suite, BOA has been successful in 100% of the functions of this test suite. The statistical analysis confirms that BOA has a significant statistical superiority in the competition with the compared algorithms. Also, in order to analyze the efficiency of BOA in dealing with real world applications, twenty-two constrained optimization problems from CEC 2011 test suite and four engineering design problems have been selected. The findings show that BOA has been successful in 90.90% of CEC2011 test suite optimization problems and in 100% of engineering design problems. In addition, the efficiency of BOA to handle SCM applications has been challenged to solve ten case studies in the field of sustainable lot size optimization. The findings show that BOA has successfully provided superior performance in 100% of the case studies compared to competitor algorithms.

SDO: A novel sled dog-inspired optimizer for solving engineering problems

A new bio-inspired meta-heuristic algorithm, named Sled Dog Optimizer (SDO), is proposed in this paper. The inspiration for SDO is primarily drawn from the various behavioral patterns of sled dogs. It focuses on constructing mathematical models by simulating the process of dog sledding, training and retiring behaviors. The mutual constraints of multiple steps and the variation of several parameters make the exploitation and exploration capabilities of SDO well balanced. To test the ability of SDO, this paper sets up several different sets of experiments to be analyzed from a number of different aspects. First, SDO is qualitatively analyzed through several performance metrics, which include search history, search trajectory and population diversity. Second, SDO is compared with several novel and excellent metaheuristics at CEC 2017 as well as CEC 2020. The results show that SDO has extremely good performance in solving unconstrained optimization problems. SDO is then further applied to the CEC 2020 real-world problem test set to test its ability to solve real-world optimization problems with constraints. Finally, this paper extends and proposes a new path planning model to which SDO is applied. In these problems with constraints, SDO performs well and has a promising application. SDO demonstrates outstanding control of exploration and exploitation capabilities in all experiments. It can be said that SDO promotes the progress of meta-heuristic algorithms and provides another new powerful technology for complex optimization problems in the real world.

A Novel Meta-Heuristic Algorithm Based on Birch Succession in the Optimization of an Electric Drive with a Flexible Shaft

The paper presents the application of a new bio-inspired metaheuristic optimization algorithm. The popularity and usability of different swarm-based metaheuristic algorithms are undeniable. The majority of known algorithms mimic the hunting behavior of animals. However, the current approach does not satisfy the full bio-diversity inspiration among different organisms. Thus, the Birch-inspired Optimization Algorithm (BiOA) is proposed as a powerful and efficient tool based on the pioneering behavior of one of the most common tree species. Birch trees are known for their superiority over other species in overgrowing and spreading across unrestricted terrains. The proposed two-step algorithm reproduces both the seed transport and plant development. A detailed description and the mathematical model of the algorithm are given. The discussion and examination of the influence of the parameters on efficiency are also provided in detail. In order to demonstrate the effectiveness of the proposed algorithm, its application to selecting the parameters of the control structure of a drive system with an elastic connection is shown. A structure with a PI controller and two additional feedbacks on the torque and speed difference between the drive motor and the working machine was selected. A system with rated and variable parameters is considered. The theoretical considerations and the simulation study were verified on a laboratory stand.

Snail Homing and Mating Search algorithm: a novel bio-inspired metaheuristic algorithm

Snail Homing and Mating Search algorithm: a novel bio-inspired metaheuristic algorithm

A novel algorithm MPPT controller based on the herd horse optimization for photovoltaic systems under partial shadow conditions

In recent years, a significant scientific issue has been the creation of maximum power point tracking (MPPT) methods to increase the energy production of PV plants. Moreover, to try to cope with the unparalleled operating conditions of PV plants, many bio-inspired meta-heuristic algorithms have already been suggested in the literature, but their implementation is often complex and difficult. In this sense, we propose a novel algorithm for monitoring the (MPPT), using the newly meta-heuristic approach of herd horse optimization (HHO). A DC/DC boost converter is utilised in the suggested controllers to extract the most power possible from the PV resource. The system is programmed and modelled using the MATLAB/SIMULINK software, which also studies four shadow models and a 3S1P topography of single-junction solar arrays. Considering partial shading conditions (PSC), the success of the power values in the global maximum power point (GMPP) of the proposed method is between 99.64% and 99.07%. Besides the time to capture the GMPP by the proposed algorithm is between 0.396 s and 1.666 s, shorter than that of the CSA and FPA algorithms. Comparison with the CSA and FPA optimizers confirmed the quality of the MPPT-based HHO algorithm for GMPP extraction in different (PSC).

Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization

In this paper, we innovatively propose the Arctic Puffin Optimization (APO), a metaheuristic optimization algorithm inspired by the survival and predation behaviors of the Arctic puffin. The APO consists of an aerial flight (exploration) and an underwater foraging (exploitation) phase. In the exploration phase, the Levy flight and velocity factor mechanisms are introduced to enhance the algorithm's ability to jump out of local optima and improve the convergence speed. In the exploitation phase, strategies such as the synergy and adaptive change factors are used to ensure that the algorithm can effectively utilize the current best solution and guide the search direction. In addition, the dynamic transition between the exploration and development phases is realized through the behavioral conversion factor, which effectively balances global search and local development. In order to verify the advancement and applicability of the APO algorithm, it is compared with nine advanced optimization algorithms. In the three test sets of CEC2017, CEC2019, and CEC2022, the APO algorithm outperforms the other compared algorithms in 72%, 70%, and 75% of the cases, respectively. Meanwhile, the Wilcoxon signed-rank test results and Friedman rank-mean statistically prove the superiority of the APO algorithm. Furthermore, on thirteen real-world engineering problems, APO outperforms the other compared algorithms in 85% of the test cases, demonstrating its potential in solving complex real-world optimization problems. In summary, APO proves its practical value and advantages in solving various complex optimization problems by its excellent performance.

Proposal of a wireless sensor network for footstep localization and optimization of its location using bio-inspired metaheuristics

This paper introduces a proposal for a wireless network of smart sensors designed to capture the vibrations produced by the impact of a person’s footsteps on a dispersive and damped floor. These vibrations are utilized to pinpoint the location of impacts using a heuristic algorithm, enabling operation at a low data transmission/reception rate. Traditionally, the placement of sensors within a room has not been given significant consideration in the context of localization; however, our findings indicate that a detailed analysis of both the number and location of sensors can substantially enhance the accuracy of footstep localization. In this study, we have optimized the arrangement and quantity of sensors through the application of bio-inspired metaheuristics, aiming to minimize localization errors across the room. Upon evaluating various bio-inspired metaheuristic optimization algorithms, we identified the one that yielded the lowest estimation errors for the room as a whole. Our experimental tests demonstrate that such optimization significantly enhances the efficacy of the localization algorithm, resulting in a reduction of localization error ranging from 18.24% to 46.78% across different trajectories.

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

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

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.

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.