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This paper presents a robust multi-objective optimization approach—the multi-objective starfish optimization algorithm (MOSFOA)—designed to address complex challenges in engineering design and optimal power flow analysis. As an advanced extension of the starfish optimization algorithm (SFOA), MOSFOA leverages biological inspiration from starfish behaviors such as exploration, predation, and regeneration to balance global exploration and local exploitation. The proposed MOSFOA employs elitist non-dominated sorting (NDS) and crowding distance (CD) mechanisms to preserve solution diversity and guide convergence toward the Pareto-optimal front. The effectiveness of MOSFOA is validated on standard ZDT and DTLZ benchmark suites and further demonstrated on real-world applications, including engineering design tasks and the IEEE 30-bus power system. Performance comparisons with ten state-of-the-art multi-objective algorithms, using metrics such as inverted generational distance (IGD) and hypervolume (HV), confirm the strength of MOSFOA in achieving a well-balanced trade-off between convergence and diversity. Additionally, the KKT proximity metric (KKTPM) is employed to assess convergence. The results demonstrate that MOSFOA significantly outperforms its counterparts in terms of both IGD and HV, achieving superior convergence and diversity performance. These findings underscore MOSFOA’s robustness, scalability, and stability across runs. Moreover, its strong performance in handling constrained engineering problems highlights its practical potential for real-world decision-making and optimization tasks in power systems and complex design optimization, making MOSFOA a promising tool for both theoretical research and industrial applications. Source code of MOSFOA are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/183090-mosfoa-multi-objective-starfish-optimization-algorithm .

This work presents the starfish optimization algorithm (SFOA), a novel bio-inspired metaheuristic for solving optimization problems, which simulates behaviors of starfish, including exploration, preying, and regeneration. SFOA consists of two main phases of exploration and exploitation. The exploration phase mimics the explorative behavior of starfish by the hybrid search pattern of combining with the five-dimensional and unidimensional search patterns to increase the computational efficiency and ensure the search capacity. The exploitation phase simulates the preying and regeneration behaviors of starfish, with a two-directional search strategy and special movement, to ensure convergence in exploitation. This work validates SFOA’s performance on 65 benchmark functions from classical functions, CEC 2017 and CEC 2022 test suites, and compares with 100 different metaheuristic algorithms, including state-of-the-art optimizers, such as marine predators algorithm, water flow optimizer (WFO), LSHADE, LSHADE-cnEpSin, and LSHADE-SPACMA. Statistical results from one-on-one comparisons demonstrate that the proposed SFOA outperforms 95 compared algorithms in accuracy and 97 algorithms in efficiency, which is only worse than WFO both in accuracy and efficiency. The scalability analysis also demonstrates that SFOA has the capacity to solve high-dimensional benchmark functions. Furthermore, ten real-world engineering optimization problems illustrate the effectiveness of SFOA to achieve global solutions and exhibit stable results. In conclusion, SFOA is promising for solving various optimization problems. The source code of SFOA is publicly available at:  https://ww2.mathworks.cn/matlabcentral/fileexchange/173735-starfish-optimization-algorithm-sfoa .

Temperature regulation in nonlinear and highly dynamic processes such as the continuous stirred-tank heater (CSTH) is a challenging task due to the inherent system nonlinearities and disturbances. This study proposes a novel metaheuristic-driven control strategy, combining the two degrees of freedom-PID acceleration (2DOF-PIDA) controller with the recently developed starfish optimization algorithm (SFOA) for temperature control of the CSTH process. The 2DOF-PIDA controller enhances system performance by decoupling setpoint tracking and disturbance rejection, while the SFOA ensures optimal tuning of controller parameters by leveraging its powerful exploration and exploitation capabilities. Simulation results validate the effectiveness of the proposed approach, demonstrating improved tracking accuracy, disturbance rejection, and robustness compared to conventional methods. The combination of 2DOF-PIDA and SFOA provides a flexible and efficient solution for controlling highly nonlinear systems, with significant implications for industrial temperature regulation applications.

In this study, a novel cascaded exponential proportional–integral–derivative (exp-PID) controller tuned by the starfish optimization algorithm (SFOA) is proposed for enhancing the transient and steady-state performance of nonlinear dynamic systems. The design objective is to achieve improved adaptability, robustness, and precision under varying operating conditions and external disturbances. The exponential PID structure introduces nonlinear modulation in the proportional and derivative components, enabling smoother control action and superior damping characteristics compared to conventional PID and fractional-order PID designs. The proposed SFOA-based exp-PID controller is validated on two benchmark systems: a DC motor speed control system and a three-tank liquid-level process. Across multiple independent trials, the controller achieved outstanding results, with the DC motor system attaining a rise time of 0.0039 s, settling time of 0.0083 s, and zero overshoot, while the three-tank system reached a rise time of 1.72 s, settling time of 2.47 s, overshoot of 1.5%, and steady-state error of 9.22 × 10⁻⁵%. Comparative analyses with recently developed algorithms (including the flood algorithm, greater cane rat algorithm, mantis search algorithm, and dandelion optimizer) as well as previously reported methods demonstrate the superior convergence behavior, stability, and accuracy of the proposed controller. Statistical evaluations further confirm the method’s robustness and consistent performance across repeated runs.

The Internet of Things (IoT) now appears in each domain, from smart cities to home applications. The widespread use of IoT is making its security a real concern. The past few years have revealed an extraordinary increase in computer-intensive applications. Such applications always make huge volumes of data that demand severe latency-aware computational processing abilities. While edge computing is one of the attractive technologies for balancing severe latency-related problems, its deployment produces novel tasks. Edge computing is an innovative model distinguished mainly by its mobility support, geo-distributed process, low latency, and context awareness. However, recent edge computing developments have begun to explore novel IoT potentials that are leveraged from a security perspective. Methods depend upon artificial intelligence (AI) and its subgroups, machine learning (ML) and deep learning (DL), are generally employed to develop a safe Intrusion Detection System (IDS) for IoT. This study proposes a Hybrid Deep Learning-Based Intrusion Detection for Edge Computing Using Starfish Optimization Algorithm (HDLID-ECSOA) technique. The main goal of the HDLID-ECSOA technique is to provide intelligent edge computing in smart cities using advanced optimization models. Initially, the data pre-processing employs the min-max normalization to convert and standardize raw data to improve the efficiency of models. Furthermore, the dingo optimizer algorithm (DOA) technique detects and chooses the most relevant features from input data. Moreover, integrating a convolutional neural network and bidirectional gated recurrent unit with a cross-attention mechanism (CNN-BiGRU-CrAM) technique is implemented for the classification process. To enhance model performance, the starfish optimization algorithm (SFOA) is used for hyperparameter tuning to select the optimal parameters for improved accuracy. A comprehensive experimentation analysis of the HDLID-ECSOA model is performed under the Edge-IIoT and ToN-IoT datasets. The experimental validation of the HDLID-ECSOA model portrayed superior accuracy values of 99.35% and 99.33% over existing techniques under the dual dataset.

Accurate identification of photovoltaic (PV) cell and module parameters is essential for reliable electrical modeling, performance assessment, and long-term energy yield prediction. This task is commonly formulated as an optimization problem, where the root mean square error (RMSE) between measured and estimated current-voltage characteristics is minimized. While numerous metaheuristic algorithms have been proposed to solve this problem, most existing studies focus primarily on algorithmic modifications, with limited attention given to enhancing the problem formulation itself. In this work, a recently introduced metaheuristic, the Starfish Optimization Algorithm (SFOA), is employed for PV parameter extraction and systematically evaluated against four contemporary optimization algorithms. In addition, a novel secant-based reformulation of the objective function is proposed to improve the accuracy of the parameter estimation process beyond the conventional RMSE-based approach. The proposed framework is validated on multiple PV models, including the single-diode (SDM), double-diode (DDM), and three-diode (TDM) models for PV cells, as well as the single-diode model of a PV module (PVM). Two widely used benchmark datasets, RTC France and Photowatt-PWP201, are used for experimental verification. The results demonstrate that integrating the secant-based objective function significantly enhances estimation accuracy and robustness across all considered models. In particular, the SFOA-Secant configuration achieves the lowest RMSE values of for SDM, for DDM, for TDM, and for PVM, outperforming all competing methods. These findings confirm that reformulating the objective function using the secant method constitutes an effective and complementary strategy for improving PV parameter extraction accuracy.

Optimization problems are prevalent in various fields of science and engineering, with several real-world applications characterized by high dimensionality and complex search landscapes. Starfish optimization algorithm (SFOA) is a recently optimizer inspired by swarm intelligence, which is effective for numerical optimization, but it may encounter premature and local convergence for complex optimization problems. To address these challenges, this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm (MCPSFOA). The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA, which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer (CPO). This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces. To further prevent premature convergence, MCPSFOA incorporates Lévy flight, leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima. Subsequently, Gaussian mutation is applied for precise solution tuning, introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation. Notably, the population diversity enhancement mechanism periodically identifies and resets stagnant individuals, thereby consistently revitalizing population variety throughout the optimization process. MCPSFOA is rigorously evaluated on 24 classical benchmark functions (including high-dimensional cases), the CEC2017 suite, and the CEC2022 suite. MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208, 2.310 and 2.417 on these benchmark functions, outperforming 11 state-of-the-art algorithms. Furthermore, the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases, where it also yields excellent results. In conclusion, MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions, but also a practical tool for solving real-world optimization problems.

Population growth and economic development have increased the world’s energy consumption, making it more difficult to manage peak loads and lower the cost of home energy management systems (HEMS). This has led to a need for smart, flexible solutions that incorporate renewable resources to improve sustainability and economic efficiency. To optimize power flow in a hybrid renewable energy system (HRES), this study suggests a fuzzy logic-based energy management system (Fuzzy-EMS) that is improved with reinforcement learning and optimized using the Starfish Optimization Algorithm (SFOA). It incorporates solar photovoltaic (PV), wind turbines (WT), battery storage systems (BSS), and electric vehicles (EVs). Adaptive handling of uncertainties in renewable generation and load demand using a Takagi–Sugeno fuzzy reinforcement learning model with triangular membership functions and 81 rules, real-time energy trading with the upstream grid, and a multi-objective framework that balances cost minimization and renewable utilization maximization are among the main contributions. Cost reductions of 35.2%, 23.8%, and 26.43% under fixed pricing, real-time pricing (RTP), and day-ahead pricing (DAP) models, respectively, are examples of how MATLAB simulations outperform well-known techniques. Furthermore, the system outperforms diesel-based systems by lowering operating costs and carbon emissions by 11.87–18.7% and increasing the use of renewable energy by up to 70% in hybrid scenarios, resulting in a net present cost (NPC) of $269,246 and a levelized cost of electricity (LCOE) of $0.281 over a 20-year period.

The increasing global demand for renewable energy poses significant challenges to grid stability due to the fluctuation and unpredictability of photovoltaic (PV) power generation. To enhance the accuracy of short-term PV power prediction, this study proposes an innovative integrated model that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), optimized using the Starfish Optimization Algorithm (SFOA) and integrated with a multi-method data processing framework. To reduce input feature redundancy and improve prediction accuracy under different conditions, the K-means clustering algorithm is employed to classify past data into three typical weather scenarios. Empirical Mode Decomposition is utilized for multi-scale feature extraction, while Kernel Principal Component Analysis is applied to reduce data redundancy by extracting nonlinear principal components. A hybrid CNN-BiLSTM neural network is then constructed, with its hyperparameters optimized using SFOA to enhance feature extraction and sequence modeling capabilities. The experiments were carried out with historical data from a Chinese PV power station, and the results were compared with other existing prediction models. The results demonstrate that the Root Mean Square Error of PV power generation prediction for three scenarios are 9.8212, 12.4448, and 6.2017, respectively, outperforming all other comparative models.

Accurate and timely analysis of electroencephalogram (EEG) signals is critical for the assessment of neurological disorders such as coma and epileptic seizures. Conventional EEG analysis is often time-consuming, prone to human error, and limited by the availability of skilled specialists, highlighting the need for automated, reliable, and intelligent diagnostic systems. This study presents a unified hybrid framework that leverages meta-heuristic optimized machine learning approaches for the classification of EEG signals in multiple neurological conditions. Features were extracted from EEG signals, including time- and frequency-domain characteristics, statistical properties, and nonlinear metrics. Feature mapping and dimensionality reduction were performed using advanced optimization techniques such as Harris Hawks Optimization (HHO) and the Starfish Optimization Algorithm (SFOA), combined with Fuzzy-PCA and Auto-Encryption (AE) for robust feature representation. Classification was conducted using hybrid models including Fuzzy K-NN, FSVM, and DT-FIS, enabling accurate discrimination between different levels of consciousness and stages of epileptic seizures. Experimental results demonstrated high performance, achieving up to 99.53% accuracy for deep coma classification and 99.28% F1-score for seizure detection, with significant improvements in precision, recall, and robustness against feature variability. The proposed framework highlights the efficacy of combining hybrid learning models, fuzzy logic, and meta-heuristic optimization for EEG-based diagnosis, providing a scalable, automated, and highly accurate system for neurological assessment.