Search Results (22)

Meta-heuristic algorithms are computational methods inspired by evolutionary processes, animal or plant behaviors, physical events, and other natural phenomena. Due to their success in solving optimization problems, meta-heuristic algorithms are widely used in the literature, leading to the development of novel variants. In this paper, new swarm-based meta-heuristic algorithms, called Improved Roosters Algorithm (IRA), Bipolar Roosters Algorithm (BRA), and Bipolar Improved Roosters Algorithm (BIRA), which are mainly based on Roosters Algorithm (RA), are presented. First, the new versions of RA (IRA, BRA, and BIRA) were compared in terms of performance, revealing that BIRA achieved significantly better results than the other variants. Then, the performance of the BIRA algorithm was compared with the performances of meta-heuristic algorithms widely used in the literature, Standard Genetic Algorithm (SGA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Grey Wolf Optimizer (GWO), and thus, its success in the literature was tested. Moreover, RA was also included in this test to show that the new version, BIRA, is more successful than the previous one (RA). For all comparisons, 20 well-known benchmark optimization functions, 11 CEC2014 test functions, and 17 CEC2018 test functions, which are also in the CEC2020 test suite, were employed. To validate the significance of the results, Friedman and Wilcoxon Signed Rank statistical tests were conducted. In addition, three commonly used problems in the field of engineering were used to test the success of algorithms in real-life scenarios: pressure vessel, gear train, and tension/compression spring design. The results indicate that the proposed algorithm (BIRA) provides better performance compared to the other meta-heuristic algorithms. Full article

This paper explores a bio-inspired swarm confrontation algorithm specifically designed for complex hilly terrains in the context of electronic games. The novelty of the proposed algorithm lies in its utilization of biologically inspired strategies to facilitate adaptive and efficient decision-making in dynamic environments. Drawing from the collective hunting behaviors of various animal species, this paper distills two key confrontation strategies: focused fire for target selection and flanking encirclement for movement coordination and attack execution. These strategies are embedded into a decentralized swarm decision-making framework, enabling agents to exhibit enhanced responsiveness and coordination in complex gaming landscapes. To validate its effectiveness, extensive experiments were conducted, comparing the proposed approach against three established algorithms. The results demonstrate that this method achieves a confrontation win rate exceeding 80%, outperforming existing techniques in both engagement efficiency and survivability. Additionally, two novel performance indices, namely the average agent quantity loss rate and the average health loss rate, are introduced to provide a more comprehensive assessment of algorithmic effectiveness. Furthermore, the impact of key algorithmic parameters on performance indices is analyzed, offering insights into the adaptability and robustness of the proposed algorithm. Full article

Urban drone applications require efficient path planning to ensure safe and optimal navigation through complex environments. Drawing inspiration from the collective intelligence of animal groups and electoral processes in human societies, this study integrates hierarchical structures and group interaction behaviors into the standard Particle Swarm Optimization algorithm. Specifically, competitive and supportive behaviors are mathematically modeled to enhance particle learning strategies and improve global search capabilities in the mid-optimization phase. To mitigate the risk of convergence to local optima in later stages, a mutation mechanism is introduced to enhance population diversity and overall accuracy. To address the challenges of urban drone path planning, this paper proposes an innovative method that combines a path segmentation and prioritized update algorithm with a cubic B-spline curve algorithm. This method enhances both path optimality and smoothness, ensuring safe and efficient navigation in complex urban settings. Comparative simulations demonstrate the effectiveness of the proposed approach, yielding smoother trajectories and improved real-time performance. Additionally, the method significantly reduces energy consumption and operation time. Overall, this research advances drone path planning technology and broadens its applicability in diverse urban environments. Full article

In this paper, it is revealed that random exploration and attraction of the best (REAB) are two underlying procedures in many swarm intelligence algorithms. This is particularly shown in two of the most known swarm algorithms: the particle swarm optimization (PSO) and gray wolf optimizer (GWO) algorithms. From this observation, it is here proposed that instead of building algorithms based on a narrative derived from observing some animal behavior, it is more convenient to focus on algorithms that perform REAB procedures; that is, to build algorithms to make a wide and efficient explorations of the search space and then gradually make that the best-evaluated search agent to attract the rest of the swarm. Following this general idea, two REAB-based algorithms are proposed; one derived from the PSO and one derived from the GWO, called REAB-PSO and REAB-GWO, respectively. To easily and succinctly express both algorithms, variable-sized open balls are employed. A comparison of proposed procedures in this paper and the original PSO and GWO using a controller tuning problem as a test bench show a significant improvement of the REAB-based algorithms over their original counterparts. Ideas here exposed can be used to derive new swarm intelligence algorithms. Full article

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. Full article

This paper considers the confrontation problem for two tank swarms of equal size and capability in a complex urban scenario. Based on the Unity platform (2022.3.20f1c1), the confrontation scenario is constructed featuring multiple crossing roads. Through the analysis of a substantial amount of biological data and wildlife videos regarding animal behavioral strategies during confrontations for hunting or food competition, two strategies are been utilized to design a novel bio-inspired intelligent swarm confrontation algorithm. The first one is the “fire concentration” strategy, which assigns a target for each tank in a way that the isolated opponent will be preferentially attacked with concentrated firepower. The second one is the “back and forth maneuver” strategy, which makes the tank tactically retreat after firing in order to avoid being hit when the shell is reloading. Two state-of-the-art swarm confrontation algorithms, namely the reinforcement learning algorithm and the assign nearest algorithm, are chosen as the opponents for the bio-inspired swarm confrontation algorithm proposed in this paper. Data of comprehensive confrontation tests show that the bio-inspired swarm confrontation algorithm has significant advantages over its opponents from the aspects of both win rate and efficiency. Moreover, we discuss how vital algorithm parameters would influence the performance indices. Full article

The present review aims to summarize the more recent scientific literature and updated state of the art on the research effort spent in adapting hardware–software tools to understand the true needs of honeybee colonies as a prerequisite for any sustainable management practice. A SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis was also performed with the aim of identifying the key factors that could support or impair the diffusion of precision beekeeping (PB) systems. Honeybee husbandry, or beekeeping, is starting to approach precision livestock farming (PLF), as has already happened in other animal husbandry sectors. A transition from the current paradigm of rational beekeeping to that of precision beekeeping (PB) is thus expected. However, due to the peculiarities of this species and the related farming practices, the PB technological systems (PB systems) are still undergoing a development process that, to some extent, limits their large-scale practical application. Several physical–chemical (weight, temperature, humidity, sound, gases) and behavioral traits (flight activity, swarming) of the hive are reviewed in light of the evolution of sensors, communication systems, and data management approaches. These advanced sensors are equipped with a microprocessor that records data and sends it to a remote server for processing. In this way, through a Wireless Sensor Network (WSN) system, the beekeeper, using specific applications on a personal computer, tablet, or smartphone, can have all the above-mentioned parameters under remote control. In general, weight, temperature, and humidity are the main hive traits monitored by commercial sensors. Surprisingly, flight activity sensors are rarely available as an option in modular PB systems marketed via the web. The SWOT analysis highlights that PB systems have promising strength points and represent great opportunities for the development of beekeeping; however, they have some weaknesses, represented especially by the high purchasing costs and the low preparedness of the addressed operators, and imply some possible threats for beekeeping in terms of unrealistic perception of the apiary status if they applied to some hives only and a possible adverse impact on the honeybees’ colony itself. Even if more research is expected to take place in the next few years, indubitably, the success of commercial PB systems will be measured in terms of return on investment, conditioned especially by the benefits (higher yields, better colonies’ health) that the beekeeper will appraise as a consequence of their use. Full article

Photovoltaic panels present an economical and environmentally friendly renewable energy solution, with advantages such as emission-free operation, low maintenance, and noiseless performance. However, their nonlinear power-voltage curves necessitate efficient operation at the Maximum Power Point (MPP). Various techniques, including Hill Climb algorithms, are commonly employed in the industry due to their simplicity and ease of implementation. Nonetheless, intelligent approaches like Particle Swarm Optimization (PSO) offer enhanced accuracy in tracking efficiency with reduced oscillations. The PSO algorithm, inspired by collective intelligence and animal swarm behavior, stands out as a promising solution due to its efficiency and ease of integration, relying only on standard current and voltage sensors commonly found in these systems, not like most intelligent techniques, which require additional modeling or sensoring, significantly increasing the cost of the installation. The primary contribution of this study lies in the implementation and validation of an advanced control system based on the PSO algorithm for real-time Maximum Power Point Tracking (MPPT) in a commercial photovoltaic system to assess its viability by testing it against the industry-standard controller, Perturbation and Observation (P&O), to highlight its advantages and limitations. Through rigorous experiments and comparisons with other methods, the proposed PSO-based control system’s performance and feasibility have been thoroughly evaluated. A sensitivity analysis of the algorithm’s search dynamics parameters has been conducted to identify the most effective combination for optimal real-time tracking. Notably, experimental comparisons with the P&O algorithm have revealed the PSO algorithm’s remarkable ability to significantly reduce settling time up to threefold under similar conditions, resulting in a substantial decrease in energy losses during transient states from 31.96% with P&O to 9.72% with PSO. Full article

Crystal violet (CV), a triphenylmethane dye, is widely used in the textile, printing, paper, leather, and cosmetics industries. However, due to its higher chemical stability and lower biodegradability, CV has teratogenic and carcinogenic toxic effects on animals and humans. Therefore, the objective of the present study was to investigate whether or not the as-prepared nZVI supported on an ordered mesoporous Zr-Ce-SBA-15 composite (nZVI/Zr-Ce-SBA-15) had more potential for CV removal from simulated wastewater in comparison with Zr-Ce-SBA-15. Meanwhile, the parameters of CV adsorption onto nZVI/Zr-Ce-SBA-15 composites were optimized by a response surface methodology (RSM) and an artificial neural network combined with particle swarm optimization (ANN–PSO). According to XRD, FTIR, SEM, and TEM, N₂ adsorption, and thermogravimetric analyses, nZVI was supported successfully on Zr-Ce-SBA-15 composites, becoming an ordered mesoporous material. The results of RSM indicated that the order of the effects of the four parameters on CV removal was, successively, initial pH, contact time, temperature, and initial CV concentration. ANN–PSO was more suitable, in comparison to RSM, to optimize the experimental parameters for CV removal from simulated wastewater using ordered mesoporous nZVI/Zr-Ce-SBA-15 composites. The optimized removal rate of CV was 93.87% under an initial pH of 3.00, a contact time of 20.00 min, an initial CV concentration of 261.00 mg/L, and a temperature of 45. Pseudo-second-order kinetics can better describe the behavior of CV adsorption onto nZVI/Zr-Ce-SBA-15 composites. The process of CV adsorption onto Zr-Ce-SBA-15 composites was followed by the Langmuir model, and its maximum adsorption capacity was 105 mg/g in 213 K. It was indirectly confirmed that the maximum adsorption capacity of nZVI/Zr-Ce-SBA-15 exceeded this value because the removal efficiency of CV using nZVI/Zr-Ce-SBA-15 was obviously higher than that of using Zr-Ce-SBA-15. The thermodynamics results indicated that CV adsorption onto nZVI/Zr-Ce-SBA-15 was a spontaneous, endothermic, and entropy-driven process. The dissolution of Fe ions and light/dark experiments confirmed nZVI/Zr-Ce-SBA-15 was simultaneously of adsorption and catalysis in the process of CV removal. The effect of removal CV was still maintained in the first four experiments (removal rate > 78%), and our suggestion is that nZVI/Zr-Ce-SBA-15 is a potential adsorbent for CV remediation from wastewater compared to Zr-Ce-SBA-15 and other adsorbents. Full article

The bio-inspired research field has evolved greatly in the last few years due to the large number of novel proposed algorithms and their applications. The sources of inspiration for these novel bio-inspired algorithms are various, ranging from the behavior of groups of animals to the properties of various plants. One problem is the lack of one bio-inspired algorithm which can produce the best global solution for all types of optimization problems. The presented solution considers the proposal of a novel approach for feature selection in classification problems, which is based on a binary version of a novel bio-inspired algorithm. The principal contributions of this article are: (1) the presentation of the main steps of the original Horse Optimization Algorithm (HOA), (2) the adaptation of the HOA to a binary version called the Binary Horse Optimization Algorithm (BHOA), (3) the application of the BHOA in feature selection using nine state-of-the-art datasets from the UCI machine learning repository and the classifiers Random Forest (RF), Support Vector Machines (SVM), Gradient Boosted Trees (GBT), Logistic Regression (LR), K-Nearest Neighbors (K-NN), and Naïve Bayes (NB), and (4) the comparison of the results with the ones obtained using the Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), and Binary Crow Search Algorithm (BCSA). The experiments show that the BHOA is effective and robust, as it returned the best mean accuracy value and the best accuracy value for four and seven datasets, respectively, compared to BGWO, BPSO, and BCSA, which returned the best mean accuracy value for four, two, and two datasets, respectively, and the best accuracy value for eight, seven, and five datasets, respectively. Full article

Bionic algorithms are established by imitating human neural structures and animal social behaviors. As an important part of bionic technology, bionic algorithms are often used to solve the control problems of complex nonlinear systems, such as the rotor aeroelasticity dynamics model used in the helicopter individual blade control (IBC) optimization process. Two control methods based on bionic intelligent algorithms are introduced, respectively. The first method is to combine the fuzzy neural network and the classical PID control together. Compared with traditional PID control, the combined one was able to adjust the PID control parameters automatically by using the learning ability of the fuzzy neural network. The second method is to directly search the optimal control parameters by using the particle swarm algorithm. Both two methods demonstrate higher efficiency and accuracy; according to the results obtained by the algorithms, the vibration level was 80% less than without the applied high order harmonics. This indicates great application prospects for bionic intelligent algorithms in solving complex nonlinear system problems. Full article

Flocking is one of the swarm tasks inspired by animal behavior. A flock involves multiple agents aiming to achieve a goal while maintaining certain characteristics of their formation. In nature, flocks vary in size. Although several studies have focused on the flock controller itself, less research has focused on how the flock size affects flock formation and performance. In this study, we address this problem and develop a simple flock controller for goal-zone-reaching tasks. The developed controller is intended for a two-dimensional environment and can handle obstacles as well as integrate an additional invented feature, called sensing power, in order to simulate the natural dynamics of migratory birds. This controller is simulated using the NetLogo simulation tool. Several experiments were conducted with and without obstacles, accompanied by changes in the flock size. The simulation results demonstrate that the flock controller is able to successfully deliver the flock to the goal zone. In addition, changes in the flock size affect multiple metrics, such as the time required to reach the goal (and, consequently, the time required to complete the flocking task), as well as the number of collisions that occur. Full article

The demands for renewable energy generation are progressively expanding because of environmental safety concerns. Renewable energy is power generated from sources that are constantly replenished. Solar energy is an important renewable energy source and clean energy initiative. Photovoltaic (PV) cells or modules are employed to harvest solar energy, but the accurate modeling of PV cells is confounded by nonlinearity, the presence of huge obscure model parameters, and the nonattendance of a novel strategy. The efficient modeling of PV cells and accurate parameter estimation is becoming more significant for the scientific community. Metaheuristic algorithms are successfully applied for the parameter valuation of PV systems. Particle swarm optimization (PSO) is a metaheuristic algorithm inspired by animal behavior. PSO and derivative algorithms are efficient methods to tackle different optimization issues. Hybrid PSO algorithms were developed to improve the performance of basic ones. This review presents a comprehensive investigation of hybrid PSO algorithms for the parameter assessment of PV cells. This paper presents how much work is conducted in this field, and how much work can additionally be performed to improve this strategy and create more ideal arrangements of an issue. Algorithms are compared on the basis of the used objective function, type of diode model, irradiation conditions, and types of panels. More importantly, the qualitative analysis of algorithms is performed on the basis of computational time, computational complexity, convergence rate, search technique, merits, and demerits. Full article

In this paper, we present a novel implementation of an ecosystem simulation. In our previous work, we implemented a 3D environment based on a predator–prey model, but we found that in most cases, regardless of the choice of starting parameters, the simulation quickly led to extinctions. We wanted to achieve system stabilization, long-term operation, and better simulation of reality by incorporating genetic evolution. Therefore we applied the predator–prey model with an evolutional approach. Using the Unity game engine we created and managed a closed 3D ecosystem environment defined by an artificial or real uploaded map. We present some demonstrative runs while gathering data, observing interesting events (such as extinction, sustainability, and behavior of swarms), and analyzing possible effects on the initial parameters of the system. We found that incorporating genetic evolution into the simulation slightly stabilized the system, thus reducing the likelihood of extinction of different types of objects. The simulation of ecosystems and the analysis of the data generated during the simulations can also be a starting point for further research, especially in relation to sustainability. Our system is publicly available, so anyone can customize and upload their own parameters, maps, objects, and biological species, as well as inheritance and behavioral habits, so they can test their own hypotheses from the data generated during its operation. The goal of this article was not to create and validate a model but to create an IT tool for evolutionary researchers who want to test their own models and to present them, for example, as animated conference presentations. The use of 3D simulation is primarily useful for educational purposes, such as to engage students and to increase their interest in biology. Students can learn in a playful way while observing in the graphical scenery how the ecosystem behaves, how natural selection helps the adaptability and survival of species, and what effects overpopulation and competition can have. Full article

Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets. Full article