Search Results (35)

The rational allocation of tasks is a critical issue in enhancing the mission execution capability of unmanned aerial vehicle (UAV) swarms, which is difficult to solve exactly in polynomial time. Evolutionary-algorithm-based approaches are among the popular methods for addressing this problem. However, existing methods often suffer from insufficiently rigorous constraint settings and a focus on single-objective optimization. To address these limitations, this paper considers multiple types of constraints—including temporal constraints, time window constraints, and task integrity constraints—and establishes a model with optimization objectives comprising task reward, task execution cost, and task execution time. A multi-objective multi-population self-adaptive ant lion optimizer (MMSALO) is proposed to solve the problem. In MMSALO, a sparsity-based selection mechanism replaces roulette wheel selection, effectively enhancing the global search capability. A random boundary strategy is adopted to increase the randomness and diversity of ant movement around antlions, thereby improving population diversity. An adaptive position update strategy is employed to strengthen exploration in the early stages and exploitation in the later stages of the algorithm. Additionally, a preference-based elite selection mechanism is introduced to enhance optimization performance and improve the distribution of solutions. Finally, to handle complex multiple constraints, a double-layer encoding mechanism and an adaptive penalty strategy are implemented. Simulation experiments were conducted to validate the proposed algorithm. The results demonstrate that MMSALO exhibits superior performance in solving multi-task, multi-constraint task-allocation problems for UAV swarms. Full article

Energy management in hybrid fuel cell ship systems faces the dual challenges of optimizing hydrogen consumption and ensuring power quality. This study proposes an Improved Weighted Antlion Optimization (IW-ALO) algorithm for multi-objective problems. The method incorporates a dynamic weight adjustment mechanism and an elite-guided strategy, which significantly enhance global search capability and convergence performance. By integrating IW-ALO with the Equivalent Consumption Minimization Strategy (ECMS), an improved weighted ECMS (IW-ECMS) is developed, enabling real-time optimization of the equivalence factor and ensuring efficient energy sharing between the fuel cell and the lithium-ion battery. To validate the proposed strategy, a system simulation model is established in Matlab/Simulink 2017b. Compared with the rule-based state machine control and optimization-based ECMS methods over a representative 300 s ferry operating cycle, the IW-ECMS achieves a hydrogen consumption reduction of 43.4% and 42.6%, respectively, corresponding to a minimum total usage of 166.6 g under the specified load profile, while maintaining real-time system responsiveness. These reductions reflect the scenario tested, characterized by frequent load variations. Nonetheless, the results highlight the potential of IW-ECMS to enhance the economic performance of ship power systems and offer a novel approach for multi-objective cooperative optimization in complex energy systems. Full article

This research proposes an intelligent modeling and optimization strategy for abrasive waterjet machining (AWJM) of magnesium-based fiber metal laminates (FMLs) reinforced with reduced graphene oxide (r-GO). Experiments were designed using the Box–Behnken method, considering waterjet pressure, stand-off distance, traverse speed, and r-GO content as inputs, while kerf taper (Kt), surface roughness (Ra), and material removal rate (MRR) were evaluated as outputs. Adaptive Neuro-Fuzzy Inference System (ANFIS) models were developed for each response, with their critical optimized hyperparameters such as cluster radius, quash factor, and training data split through the dragonfly optimization (DFO) algorithm. The optimized ANFIS networks yielded a high predictive accuracy, with low RMSE and MAPE values and close agreement between predicted and measured results. Four metaheuristic algorithms including particle swarm optimization (PSO), salp swarm optimization (SSO), whale optimization algorithm (WOA), and the antlion optimizer (ALO) were applied for simultaneous optimization, using a TOPSIS-based single-objective formulation. ALO outperformed the others, identifying 325 MPa waterjet pressure, 2.5 mm stand-off, 800 mm/min traverse speed, and 0.00602 wt% r-GO addition in FMLs as optimal conditions. These settings produced a kerf taper of 2.595°, surface roughness of 8.9897 µm, and material removal rate of 138.13 g/min. The proposed ANFIS-ALO framework demonstrates strong potential for achieving precision and productivity in AWJM of hybrid laminates. Full article

The stable control of the main steam flow rate (MSFR) can effectively improve the waste combustion efficiency and energy utilization, reduce environmental pollution, and is crucial for promoting the sustainable development of municipal solid waste incineration (MSWI). Developed countries benefit from stable municipal solid waste (MSW) composition, enabling advanced automated combustion control. However, in developing countries, fluctuating waste composition and calorific value cause frequent disturbances, limiting the use of foreign control methods. Therefore, MSFR control technologies suited to developing countries are crucial. This study proposes a two-layer intelligent control method, consisting of an optimization setting layer and a loop control layer. The optimization layer uses a steam flow prediction model (OPTICS and RBF) and an improved antlion optimizer (IALO) for manipulated variable setpoints. The control layer applies reinforcement learning (actor–critic) to fine-tune PI controller parameters. Experimental results show that the proposed method adaptively adjusts manipulated variables, ensuring MSFR control within the target range and maintaining efficient, stable MSWI operation. Full article

In this study, a brand-new algorithm called the Comprehensive Adaptive Enterprise Development Optimizer (CAED) is proposed to overcome the drawbacks of the Enterprise Development (ED) algorithm in complex optimization tasks. In particular, it aims to tackle the problems of slow convergence and low precision. To enhance the algorithm’s ability to break free from local optima, a lens imaging reverse learning approach is incorporated. This approach creates reverse solutions by utilizing the concepts of optical imaging. As a result, it expands the search range and boosts the probability of finding superior solutions beyond local optima. Moreover, an environmental sensitivity-driven adaptive inertial weight approach is developed. This approach dynamically modifies the equilibrium between global exploration, which enables the algorithm to search for new promising areas in the solution space, and local development, which is centered on refining the solutions close to the currently best-found areas. To evaluate the efficacy of the CAED, 23 benchmark functions from CEC2005 are chosen for testing. The performance of the CAED is contrasted with that of nine other algorithms, such as the Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), and the Antlion Optimizer (AOA). Experimental findings show that for unimodal functions, the standard deviation of the CAED is almost 0, which reflects its high accuracy and stability. In the case of multimodal functions, the optimal value obtained by the CAED is notably better than those of other algorithms, further emphasizing its outstanding performance. The CAED algorithm is also applied to engineering optimization challenges, like the design of cantilever beams and three-bar trusses. For the cantilever beam problem, the optimal solution achieved by the CAED is 13.3925, with a standard deviation of merely 0.0098. For the three-bar truss problem, the optimal solution is 259.805047, and the standard deviation is an extremely small 1.11 × 10⁻⁷. These results are much better than those achieved by the traditional ED algorithm and the other comparative algorithms. Overall, through the coordinated implementation of multiple optimization strategies, the CAED algorithm exhibits high precision, strong robustness, and rapid convergence when searching in complex solution spaces. As such, it offers an efficient approach for solving various engineering optimization problems. Full article

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 $n_S$ and the maximum number of iterations $n_{MaxIter}$; 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. Full article

Recent developments and the widespread use of IoT-enabled technologies has led to the Research and Development (R&D) efforts in green communication. Traditional dynamic-source routing is one of the well-known protocols that was suggested to solve the information dissemination problem in an IoT environment. However, this protocol suffers from a high level of energy consumption in sensor-enabled device-to-device and device-to-base station communications. As a result, new information dissemination protocols should be developed to overcome the challenge of dynamic-source routing, and other similar protocols regarding green communication. In this context, a new energy-efficient routing protocol (EFRP) is proposed using the hybrid adopted heuristic techniques. In the densely deployed sensor-enabled IoT environment, an optimal information dissemination path for device-to-device and device-to-base station communication was identified using a hybrid genetic algorithm (GA) and the antlion optimization (ALO) algorithms. An objective function is formulated focusing on energy consumption-centric cost minimization. The evaluation results demonstrate that the proposed protocol outperforms the Greedy approach and the DSR protocol in terms of a range of green communication metrics. It was noticed that the number of alive sensor nodes in the experimental network increased by more than 26% compared to the other approaches and lessened energy consumption by about 33%. This leads to a prolonged IoT network lifetime, increased by about 25%. It is evident that the proposed scheme greatly improves the information dissemination efficiency of the IoT network, significantly increasing the network’s throughput. Full article

Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal Kalman filtering and an intelligent bionic antlion optimization algorithm. Firstly, according to the research purpose of the paper and the focus on the accuracy of the establishment of the three degrees of freedom dynamics model, fully considering the road conditions, the paper adopts the Dugoff tire model and finally completes the establishment of the vehicle state estimation model. Secondly, the drive state estimation algorithm is developed utilizing the principles of federal Kalman filtering and volume Kalman filtering. At the same time, robust estimation theory is introduced into the sub-filter, and the antlion optimization module is designed at the lower layer of the main filter to enhance the accuracy of estimates. It is easy to see that the design of the Antlion federal Kalman travel state estimation algorithm has noticeably enhanced accuracy and traceability, according to the result. Thirdly, a joint estimation algorithm of state estimation and road surface adhesion coefficient has been devised to enhance the stability and precision of the estimation process. Finally, the results showed that the joint estimation algorithm has high accuracy in estimating vehicle driving state parameters such as the center of mass lateral deflection angle and road surface adhesion coefficient by simulation. Full article

In the abnormal situation of an aluminum electrolysis cell, the setting of cell voltage is mainly based on manual experience. To obtain a smaller cell voltage and optimize the operating parameters, a multi-objective optimization method for cell voltage based on a comprehensive index evaluation model is proposed. Firstly, a comprehensive judgment model of the cell state based on the energy balance, material balance, and stability of the aluminum electrolysis process is established. Secondly, a fuzzy neural network (FNN) based on the autoregressive moving average (ARMA) model is designed to establish the cell-state prediction model in order to finish the real-time monitoring of the process. Thirdly, the optimization goal of the process is summarized as having been met when the difference between the average cell voltage and the target value reaches the minimum, and the condition of the cell is excellent. And then, the optimization setting model of cell voltage is established under the constraints of the production and operation requirements. Finally, a multi-objective antlion optimization algorithm (MOALO) is used to solve the above model and find a group of optimized values of the electrolysis cell, which is used to realize the optimization control of the cell state. By using actual production data, the above method is validated to be effective. Moreover, optimized operating parameters are used to verify the prediction model of cell voltage, and the cell state is just excellent. The method is also applied to realize the optimization control of the process. It is of guiding significance for stabilizing the electrolytic aluminum production and achieving energy saving and consumption reduction. Full article

Currently, the integration of new energy sources into the power system poses a significant challenge to frequency stability. To address the issue of capacity sizing when utilizing storage battery systems to assist the power grid in frequency control, a capacity optimal allocation model is proposed for the primary frequency regulation of energy storage. Due to the requirement of a large number of actual parameters for the optimal allocation model, a simulation model of energy storage capacity is constructed based on the characteristics of primary frequency control to provide the necessary parameters. Subsequently, the primary frequency modulation output model of energy storage is established by considering the basic action output, the action in the frequency modulation dead zone, and a certain capacity margin. The antlion algorithm is employed to solve the capacity optimal allocation model. Finally, three groups of experiments are designed and compared to demonstrate the effectiveness of the proposed method in setting the capacity margin, which can increase profit to a certain extent. Full article

The parameter identification of a PEMFC is the process of using optimization algorithms to determine the ideal unknown variables suitable for the development of an accurate fuel-cell-performance prediction model. These parameters are not always available from the manufacturer’s datasheet, so they need to be determined to accurately model and predict the fuel cell’s performance. Five optimization methods—bald eagle search (BES) algorithm, equilibrium optimizer (EO), coot (COOT) algorithm, antlion optimizer (ALO), and heap-based optimizer (HBO)—are used to compute seven unknown parameters of a PEMFC. During optimization, these seven parameters are used as decision variables, and the fitness function to be minimized is the sum square error (SSE) between the estimated cell voltage and the actual measured cell voltage. The SSE obtained for the BES algorithm was noted to be 0.035102. The COOT algorithm recorded an SSE of 0.04155, followed by ALO with an SSE of 0.04022 and HBO with an SSE of 0.056021. BES predicted the performance of the fuel cell accurately; hence, it is suitable for the development of a digital twin for fuel-cell applications and control systems for the automotive industry. Furthermore, it was deduced that the convergence speed for BES was faster compared to the other algorithms investigated. This study aims to use metaheuristic algorithms to predict fuel-cell performance for the development and commercialization of digital twins in the automotive industry. Full article

A robust, optimized power system stabilizer (PSS) is crucial for oscillation damping, and thus improving electrical network stability. Additionally, real-time testing methods are required to significantly reduce the likelihood of software failure in a real-world setting at the user location. This paper presents an Antlion-based proportional integral derivative (PID) PSS to improve power system stability during real-time constraints. The Antlion optimization (ALO) is developed with real-time testing methodology, using hardware-in-the-loop (HIL) that can communicate multiple digital control schemes with real-time signals. The dynamic power system model runs on the dSPACE DS1104, and the proposed PSS runs on the field programmable gate arrays (FPGA) (NI SbRIO-9636 board). The optimized PSS performance was compared with a modified particle swarm optimization (MPSO)-based PID-PSS, through different performance indices. The test cases include other step load perturbations and several short circuit faults at various locations. Twelve different test cases have been applied, through real-time constraints, to prove the robustness of the proposed PSS. These include 5 and 10% step changes through 3 different operating conditions and single, double, and triple lines to ground short circuits through 3 different operating conditions, and at various locations of the system transmission lines. The analysis demonstrates the effectiveness of ALO and MPSO in regaining the system’s stability under the three loading conditions. The integral square of the error (ISE), integral absolute of the error (IAE), integral time square of the error (ITSE), and integral time absolute of the error (ITAE) are used as performance indices in the analysis stage. The simulation results demonstrate the effectiveness of the proposed PSS, based on the ALO algorithm. It provides a robust performance, compared to the traditional PSS. Regarding the applied indices, the proposed PSS, based on the ALO algorithm, obtains significant improvement percentages in ISE, IAE, ITSE, and ITAE with 30.919%, 23.295%, 51.073%, and 53.624%, respectively. Full article

The immense application of mathematical modeling for the improvement of bioprocesses determines model development as a topical field. Metaheuristic techniques, especially hybrid algorithms, have become a preferred tool in model parameter identification. In this study, two efficient algorithms, the ant lion optimizer (ALO), inspired by the interaction between antlions and ants in a trap, and the genetic algorithm (GA), influenced by evolution and the process of natural selection, have been hybridized for the first time. The novel ALO-GA hybrid aims to balance exploration and exploitation and significantly improve its global optimization ability. Firstly, to verify the effectiveness and superiority of the proposed work, the ALO-GA is compared with several state-of-the-art hybrid algorithms on a set of classical benchmark functions. Further, the efficiency of the ALO-GA is proved in the parameter identification of a model of an Escherichia coli MC4110 fed-batch cultivation process. The obtained results have been studied in contrast to the results of various metaheuristics employed for the same problem. Hybrids between the GA, the artificial bee colony (ABC) algorithm, the ant colony optimization (ACO) algorithm, and the firefly algorithm (FA) are considered. A series of statistical tests, parametric and nonparametric, are performed. Both numerical and statistical results clearly show that ALO-GA outperforms the other competing algorithms. The ALO-GA hybrid algorithm proposed here has achieved an improvement of 6.5% compared to the GA-ACO model, 7% compared to the ACO-FA model, and 7.8% compared to the ABC-GA model. Full article

Recent advances in electric grid technology have led to sustainable, modern, decentralized, bidirectional microgrids (MGs). The MGs can support energy storage, renewable energy sources (RESs), power electronics converters, and energy management systems. The MG system is less costly and creates less CO₂ than traditional power systems, which have significant operational and fuel expenses. In this paper, the proposed hybrid MG adopts renewable energies, including solar photovoltaic (PV), wind turbines (WT), biomass gasifiers (biogasifier), batteries’ storage energies, and a backup diesel generator. The energy management system of the adopted MG resources is intended to satisfy the load demand of Basra, a city in southern Iraq, considering the city’s real climate and demand data. For optimal sizing of the proposed MG components, a meta-heuristic optimization algorithm (Hybrid Grey Wolf with Cuckoo Search Optimization (GWCSO)) is applied. The simulation results are compared with those achieved using Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), and Antlion Optimization (ALO) to evaluate the optimal sizing results with minimum costs. Since the adopted GWCSO has the lowest deviation, it is more robust than the other algorithms, and their optimal number of component units, annual cost, and Levelized Cost Of Energy (LCOE) are superior to the other ones. According to the optimal annual analysis, LCOE is 0.1192 and the overall system will cost about USD 2.6918 billion. Full article

The antlion optimizer (ALO) algorithm is used in this article for maximum power point tracking (MPPT) of a solar array. The solar array consists of a single module, while there are 20 cells in the module. The voltage and current ratings of each cell are 2 V and 2.5 A, making a 100 W array in ideal condition. However, the voltage and current characteristics of the PV cell are unable to achieve maximum power. Therefore, the ALO was used for MPPT. The results of the ALO are compared with the traditional metaheuristic approaches, perturb and observe ($P\&O$) and flower pollination (FP) algorithms. Comparison of the ALO with the stated algorithms is conducted for two cases: when solar irradiance is 1000 W/m${}^2$ and when it drops to 200 W/m${}^2$ at first then reaches 1000 W/m${}^2$. The change of irradiance is performed to simulate the partial shading condition. The simulation results depict that maximum power for the first case using the ALO reaches 91.3 W in just 0.05 s, while the $P\&O$ and PFA reach 90 W after 0.64 and 2 s, respectively. For the case of partial shading, maximum power using the ALO drops to 55 W when irradiance decreases to 200 W/m${}^2$ and then increases with the increase in irradiance reaching 91.3 W which clearly shows that the ALO outperforms the P&O and FPA. Full article