This paper proposes a novel optimization method for solving real-world optimization problems. It is inspired by a cooperative human phenomenon named the mountaineering team-based optimization (MTBO) algorithm. Proposed for the first time, the MTBO algorithm is mathematically modeled to achieve a robust optimization algorithm based on the social behavior and human cooperation needed in considering the natural phenomena to reach a mountaintop, which represents the optimal global solution. To solve optimization problems, the proposed MTBO algorithm captures the phases of the regular and guided movement of climbers based on the leader’s experience, obstacles against reaching the peak and getting stuck in local optimality, and the coordination and social cooperation of the group to save members from natural hazards. The performance of the MTBO algorithm was tested with 30 known CEC 2014 test functions, as well as on classical engineering design problems, and the results were compared with that of well-known methods. It is shown that the MTBO algorithm is very competitive in comparison with state-of-art metaheuristic methods. The superiority of the proposed MTBO algorithm is further confirmed by statistical validation, as well as the Wilcoxon signed-rank test with advanced optimization algorithms. Compared to the other algorithms, the MTBO algorithm is more robust, easier to implement, exhibits effective optimization performance for a wide range of real-world test functions, and attains faster convergence to optimal global solutions.

Given the importance of renewable energy sources in distribution systems, this article addresses the problem of locating and determining the capacity of these sources, namely, wind turbines and solar panels. To solve this optimization problem, a new algorithm based on the behavior of salp is used. The objective functions include reducing losses, improving voltage profiles, and reducing the costs of renewable energy sources. In this method, the allocation of renewable resources is considered for different load models in distribution systems and different load levels using smart meters. Due to the fact that these objective functions are multi-objective, the fuzzy decision-making method is used to select the optimal solution from the set of Pareto solutions. The considered objective functions lead to loss reduction, voltage profile improvement, and RES cost reduction (A allocating RES resources optimally without resource limitations; B: allocating RES resources optimally with resource limitations). In addition, daily wind, solar radiation, and temperature data are taken into account. The proposed method is applied to the IEEE standard 33-bus system. The simulation results show the better performance of the multi-objective salp swarm algorithm (MSSA) at improving voltage profiles and reducing losses in distribution systems. Lastly, the optimal results of the MSSA algorithm are compared with the PSO and GA algorithms.

The rise of the renewable energy sources (RES) in microgrids has increased the impact of damping and low inertia on network stability. This gives rise to several issues in the power systems, including power fluctuations from variable RES, degradation of the frequency regulation, voltage surges, and oversupply from grid‐connected distributed generators. This paper presents a cascaded hybrid load‐frequency controller for an airport microgrid that addresses frequency fluctuations caused by load changes. It is combining a self‐adaptive fuzzy controller (SAF) and a classical two‐degree‐of‐freedom proportional integral derivative (PID) controller (2DOFPID) called CSAF‐2DOFPID. It is applied to the control section of a diesel generator, an electric aircraft energy storage system, and a demand response program. The controller parameters are optimized using a suggested hybrid differential artificial hummingbird algorithm (D‐AHA) to minimize frequency deviation. The proposed control framework is simulated on an airport microgrid comprising various components, and the impact of time delay on the controller's signals, system uncertainty is also examined. The simulation results demonstrate that the proposed self‐adaptive cascaded fuzzy controller outperforms other controllers such as PID, CF‐PID, and CF‐2DOFPID in reducing frequency fluctuations, and the D‐AHA algorithm achieves favourable results compared to other algorithms in the optimal tuning controller parameters.