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Electricity generation in Islanded Urban Microgrids (IUMG) now relies heavily on a diverse range of Renewable Energy Sources (RES). However, the dependable utilization of these sources hinges upon efficient Electrical Energy Storage Systems (EESs). As the intermittent nature of RES output and the low inertia of IUMGs often lead to significant frequency fluctuations, the role of EESs becomes pivotal. While these storage systems effectively mitigate frequency deviations, their high costs and elevated power density requirements necessitate alternative strategies to balance power supply and demand. In recent years, substantial attention has turned towards harnessing Electric Vehicle (EV) batteries as Mobile EV Energy Storage (MEVES) units to counteract frequency variations in IUMGs. Integrating MEVES into the IUMG infrastructure introduces complexity and demands a robust control mechanism for optimal operation. Therefore, this paper introduces a robust, high-order degree of freedom cascade controller known as the 1PD-3DOF-PID (1 + Proportional + Derivative—Three Degrees Of Freedom Proportional-Integral-Derivative) controller for Load Frequency Control (LFC) in IUMGs integrated with MEVES. The controller’s parameters are meticulously optimized using the Coati Optimization Algorithm (COA) which mimics coati behavior in nature, marking its debut in LFC of IUMG applications. Comparative evaluations against classical controllers and algorithms, such as 3DOF-PID, PID, Reptile Search Algorithm, and White Shark Optimizer, are conducted under diverse IUMG operating scenarios. The testbed comprises various renewable energy sources, including wind turbines, photovoltaics, Diesel Engine Generators (DEGs), Fuel Cells (FCs), and both Mobile and Fixed energy storage units. Managing power balance in this entirely renewable environment presents a formidable challenge, prompting an examination of the influence of MEVES, DEG, and FC as controllable units to mitigate active power imbalances. Metaheuristic algorithms in MATLAB-SIMULINK platforms are employed to identify the controller’s gains across all case studies, ensuring the maintenance of IUMG system frequency within predefined limits. Simulation results convincingly establish the superiority of the proposed controller over other counterparts. Furthermore, the controller’s robustness is rigorously tested under ± 25% variations in specific IUMG parameters, affirming its resilience. Statistical analyses reinforce the robust performance of the COA-based 1PD-3DOF-PID control method. This work highlights the potential of the COA Technique-optimized 1PD-3DOF-PID controller for IUMG control, marking its debut application in the LFC domain for IUMGs. This comprehensive study contributes valuable insights into enhancing the reliability and stability of Islanded Urban Microgrids while integrating Mobile EV Energy Storage, marking a significant advancement in the field of Load-Frequency Control.

In this study, two novel hybrid intelligent models were developed to evaluate the short-term rockburst using the random forest (RF) method and two meta-heuristic algorithms, whale optimization algorithm (WOA) and coati optimization algorithm (COA), for hyperparameter tuning. Real-time predictive models of this phenomenon were created using a database comprising 93 case histories, taking into account various microseismic parameters. The results indicated that the WOA achieved the highest overall performance in hyperparameter tuning for the RF model, outperforming the COA. RF-WOA model accurately predicted the occurrence of this phenomenon with an accuracy of 0.944. Additionally, for this model, precision, recall and F1-score were obtained as 0.950, 0.944 and 0.943, respectively, indicating that the proposed model is robust in predicting damage severity of rockburst in deep underground projects. Subsequently, the Shapley additive explanations (SHAP) method was employed to interpret and explain the prediction process and assess the influence of input features based on RF-WOA model. The results showed that three parameters including cumulative seismic energy, cumulative microseismic events, and cumulative apparent volume have the greatest impact on the occurrence of rockburst events. This study provides an interpretable and transparent resource for accurately predicting rockburst events in real time. It can facilitate estimating project costs, selecting a suitable support system, and identifying essential ways to limit the danger of rockburst.

The multi-robot path planning problem is an NP-hard problem. The coati optimization algorithm (COA) is a novel metaheuristic algorithm and has been successfully applied in many fields. To solve multi-robot path planning optimization problems, we embed two differential evolution (DE) strategies into COA, a self-adaptive differential evolution-based coati optimization algorithm (SDECOA) is proposed. Among these strategies, the proposed algorithm adaptively selects more suitable strategies for different problems, effectively balancing global and local search capabilities. To validate the algorithm’s effectiveness, we tested it on CEC2020 benchmark functions and 48 CEC2020 real-world constrained optimization problems. In the latter’s experiments, the algorithm proposed in this paper achieved the best overall results compared to the top five algorithms that won in the CEC2020 competition. Finally, we applied SDECOA to optimization multi-robot online path planning problem. Facing extreme environments with multiple static and dynamic obstacles of varying sizes, the SDECOA algorithm consistently outperformed some classical and state-of-the-art algorithms. Compared to DE and COA, the proposed algorithm achieved an average improvement of 46% and 50%, respectively. Through extensive experimental testing, it was confirmed that our proposed algorithm is highly competitive. The source code of the algorithm is accessible at: https://ww2.mathworks.cn/matlabcentral/fileexchange/164876-HDECOA.

Meta-heuristic algorithms are among the technologies that have good performance in multilevel threshold image segmentation by obtaining optimal thresholds. However, most studies in the literature consider either a single objective function or images of a single type or low threshold levels, due to the drawbacks of poor ability to balance global and local search, premature convergence in high dimension, or low convergence efficiency of existing work in handling multi-task image segmentation. This paper aims to address these drawbacks and to develop search mechanisms and an enhanced optimizer for multilevel threshold image segmentation considering simultaneously different objective functions, both grayscale and color images, and both low and high threshold levels. More precisely, to improve the capability of balancing between global exploration and local exploitation, firstly a novel search mechanism ASSM inspired by the salp swarm optimization algorithm (SSA) is proposed, which is shown to have universality in improving a class of swarm intelligence optimization algorithms called DP-algorithms. Then, by proposing hierarchical vertical-horizontal search (HVHS) strategy and combining it with improved circle chaotic mapping initialization, lens opposition-based learning, and Lévy flight strategy, a multi-strategy collaborative ENCOA framework is constructed to prevent premature convergence in high-dimensional solution space. To evaluate the performance of the ENCOA, comparison experiments are implemented on CEC2017 benchmark suite and four engineering problems. Finally, the ENCOA is applied to multilevel threshold image segmentation on 6 grayscale images and 4 color images, by taking both Kapur’s entropy and Otsu between-class variance as the objective functions, and under threshold levels ranging from 4 to 32. It is shown that the ENCOA outperforms other recent-related algorithms in terms of both convergence accuracy and segmentation quality, especially when dealing with high threshold segmentation.

In response to the challenges faced by the Coati Optimization Algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility to local optima, and low convergence accuracy, this paper introduces an enhanced variant termed the Adaptive Coati Optimization Algorithm (ACOA). ACOA achieves a balanced exploration–exploitation trade-off through refined exploration strategies and developmental methodologies. It integrates chaos mapping to enhance randomness and global search capabilities and incorporates a dynamic antagonistic learning approach employing random protons to mitigate premature convergence, thereby enhancing algorithmic robustness. Additionally, to prevent entrapment in local optima, ACOA introduces an Adaptive Levy Flight strategy to maintain population diversity, thereby improving convergence accuracy. Furthermore, underperforming individuals are eliminated using a cosine disturbance-based differential evolution strategy to enhance the overall quality of the population. The efficacy of ACOA is assessed across four dimensions: population diversity, exploration–exploitation balance, convergence characteristics, and diverse strategy variations. Ablation experiments further validate the effectiveness of individual strategy modules. Experimental results on CEC-2017 and CEC-2022 benchmarks, along with Wilcoxon rank-sum tests, demonstrate superior performance of ACOA compared to COA and other state-of-the-art optimization algorithms. Finally, ACOA’s applicability and superiority are reaffirmed through experimentation on five real-world engineering challenges and a complex urban three-dimensional unmanned aerial vehicle (UAV) path planning problem.

The primary objective when operating a distribution network is to minimize operating costs while taking technical constraints into account. Minimizing the operational costs is difficult when there is a high penetration of renewable resources and variability of loads, which introduces uncertainty. In this paper, a flexible, dynamic reconfiguration model is developed that enables a distribution network to minimize operating costs on an hourly basis. The model fitness function is to minimize the system costs, including power loss, voltage deviation, purchased power from the upstream network, renewable generation, and switching costs. The uncertainty of the load and generation from renewable energies is planned to use their probability density functions via a scenario-based approach. The suggested optimization problem is solved using a metaheuristic approach based on the coati optimization algorithm (COA) due to the nonlinearity and non-convexity of the problem. To evaluate the performance of the presented approach, it is validated on the IEEE 33-bus radial system and TPC 83-bus real system. The simulation results show the impact of dynamic reconfiguration on reducing operation costs. It is found that dynamic reconfiguration is an efficient solution for reducing power losses and total energy drawn from the upstream network by increasing the number of switching operations.

The recently proposed Coati Optimization Algorithm (COA) is one of the swarm-based intelligence algorithms. In this study, the current COA algorithm is developed and Enhanced COA (ECOA) is proposed. There is an imbalance between the exploitation and exploration capabilities of the COA. To balance the exploration and exploitation capabilities of COA in the search space, the algorithm has been improved with two modifications. These modifications are those that preserve population diversity for a longer period of time during local and global searches. Thus, some of the drawbacks of COA in search strategies are eliminated. The achievements of COA and ECOA were tested in four different test groups. COA and ECOA were first compared on twenty-three classic CEC functions in three different dimensions (10, 20, and 30). Later, ECOA was tested on CEC-2017 with twenty-nine functions and on CEC-2020 with ten functions, and its success was demonstrated in different dimensions (5, 10, and 30). Finally, ECOA has been shown to be successful in different cycles (300, 500, and 1000) on Big Data Optimization Problems (BOP), which have high dimensions. Friedman and Wilcoxon tests were performed on the results, and the obtained results were analyzed in detail. According to the results, ECOA outperformed COA in all comparisons performed. In order to prove the success of ECOA, seven newly proposed algorithms (EMA, FHO, SHO, HBA, SMA, SOA, and JAYA) were selected from the literature in the last few years and compared with ECOA and COA. In the classical test functions, ECOA achieved the best results, surpassing all other algorithms when compared. It achieved the second-best results in CEC-2020 test functions and entered the top four in CEC-2017 and BOP test functions. According to the results, ECOA can be used as an alternative algorithm for solving small, medium, and large-scale continuous optimization problems.

In recent times, there have been notable advancements in solar energy and other renewable sources, underscoring their vital contribution to environmental conservation. Solar cells play a crucial role in converting sunlight into electricity, providing a sustainable energy alternative. Despite their significance, effectively optimizing photovoltaic system parameters remains a challenge. To tackle this issue, this study introduces a new optimization approach based on the coati optimization algorithm (COA), which integrates opposition-based learning and chaos theory. Unlike existing methods, the COA aims to maximize power output by integrating solar system parameters efficiently. This strategy represents a significant improvement over traditional algorithms, as evidenced by experimental findings demonstrating improved parameter setting accuracy and a substantial increase in the Friedman rating. As global energy demand continues to rise due to industrial expansion and population growth, the importance of sustainable energy sources becomes increasingly evident. Solar energy, characterized by its renewable nature, presents a promising solution to combat environmental pollution and lessen dependence on fossil fuels. This research emphasizes the critical role of COA-based optimization in advancing solar energy utilization and underscores the necessity for ongoing development in this field.

Load balancing (LB) is a critical aspect of Cloud Computing (CC), enabling efficient access to virtualized resources over the internet. It ensures optimal resource utilization and smooth system operation by distributing workloads across multiple servers, preventing any server from being overburdened or underutilized. This process enhances system reliability, resource efficiency, and overall performance. As cloud computing expands, effective resource management becomes increasingly important, particularly in distributed environments. This study proposes a novel approach to resource prediction for cloud network load balancing, incorporating federated learning within a blockchain framework for secure and distributed management. The model leverages Dilated and Attention-based 1-Dimensional Convolutional Neural Networks with bidirectional long short-term memory (DA-DBL) to predict resource needs based on factors such as processing time, reaction time, and resource availability. The integration of the Random Opposition Coati Optimization Algorithm (RO-COA) enables flexible and efficient load distribution in response to real-time network changes. The proposed method is evaluated on various metrics, including active servers, makespan, Quality of Service (QoS), resource utilization, and power consumption, outperforming existing approaches. The results demonstrate that the combination of federated learning and the RO-COA-based load balancing method offers a robust solution for enhancing cloud resource management.

[This corrects the article DOI: 10.3389/fdgth.2025.1706660.].