This study delves into the exploration of a novel Multi-objective Snow Ablation Optimizer (MOSAO) algorithm, tailored for addressing expansive Optimal Power Flow (OPF) challenges inherent in intricate power systems. These systems are often complemented with the integration of renewable energy modalities and the state-of-the-art Flexible AC Transmission Systems (FACTS). Building upon the foundational framework of a previously documented single-objective Snow Ablation Optimizer, we have evolved it into the MOSAO paradigm. This transformation is achieved by harnessing the potency of non-dominated sorting coupled with the crowding distance strategy. The task of OPF magnifies in complexity when integrating renewable energy resources due to their inherent unpredictability and intermittent nature. As the modern power landscape evolves, FACTS devices are witnessing an increasing deployment to mitigate network demand and alleviate congestion issues. Within the ambit of this research, we've incorporated a stochastic wind energy source, working synergistically with an array of FACTS instruments. These encompass the static VAR compensator, thyristor-controlled series compensator and thyristor-driven phase shifter, all operating within the confines of an IEEE-30 bus framework. Strategic placement and calibration of these FACTS devices aim to optimize the system by minimizing the cumulative fuel expenditure. The capricious essence of wind as an energy source is elegantly depicted through the lens of Weibull probability density graphs. To distil the optimal middle-ground solutions, we've employed a fuzzy decision-making matrix. When benchmarking our findings against those derived from other esteemed optimization algorithms, we observe a notable distinction. The results from the modified IEEE-30 bus system accentuate the superior convergence, diversity and distribution attributes of MOSAO, especially when scrutinizing power flows. The MOSAO source code is available at: https://github.com/kanak02/MOSAO.

The hybrid model of the power network infrastructure is an essential part of the sophisticated technology of the electrical network. For the traditional Optimal Power Flow (OPF) problem, thermal generators are typically employed to generate electricity. In this case, the amount of fuel required to produce power is constrained, and emissions from the network are frequently neglected. Renewable Energy Sources (RESs) have received increasing attention due to various potential characteristics such as cleanliness, diversity, and renewability. As an outcome, RESs are being integrated into the electrical grid at an increasing rate. The study in this paper proposes a techno-monetary investigation into the single- and multi-objective OPF, coordinating with RESs like wind, PhotoVoltaic (PV), and small hydropower units with hybrid PV. Moreover, the probability density functions of Weibull, Lognormal, and Gumble have been used independently to predict the required power in this paper. This paper considers an equilibrium optimizer for handling the OPF problem. The superior performance of the equilibrium optimizer is further verified with the results of both single- and multi-objective through comparative analysis with state-of-the-art counterparts, and the indications are that the proposed method can find better optimal solutions, use a lower number of generated solutions, and faster convergence with well distributed optimal Pareto front for multi-objective problems. The results are verified by employing a modified IEEE-30 bus hybrid power network, and performance comparisons are made among several well-established algorithms. Simulation findings show that the suggested strategy can achieve a reasonable compromise solution for different objectives.

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

Today, reliable power delivery and increasing demand are important issues in modern power systems. Flexible Alternating Current Transmission Systems (FACTS) devices are used to control transmission line parameters to increase power transfer and stability. Nevertheless, the problem of determining the optimal placement and sizing of these devices is still challenging, as the placement and sizing of the devices affects generation costs, power losses, voltage stability, and system reliability. This study proposes the Fata Morgana Algorithm (FATA), an optimization algorithm inspired by the natural process of mirage formation to optimize placement and sizing of FACTS devices in an IEEE 30 bus system with wind turbine integration. The FATA algorithm is evaluated against recently developed and improved optimization techniques, such as rime-ice formation phenomenon based Improved RIME (IRIME) Algorithm, Newton–Raphson-Based Optimization (NRBO), Resistance Capacitance Algorithm (RCA), Krill Optimization Algorithm (KOA), and Grey Wolf Optimizer (GWO), across multiple optimization objectives: reduction in generation cost, reduction in power loss and combined generation cost plus power loss, termed as Gross cost function. Results obtained show that FATA consistently outperforms the other algorithms in terms of convergence and solution quality, offering a robust approach to solving single objective optimization problems. FATA theoretically provides a good balance between exploration and exploitation, and produces better global solutions. It practically increases power system efficiency by lowering operational costs and losses and improving stability. Results indicate that the FATA algorithm produced minimum generation cost of 807.0405 $/h, which is 0.088–0.426% less than the competing algorithms. It also reduced power losses to 5.5917 MW, which is 1.095–6.781% less than other methods. For gross cost minimization, the FATA algorithm achieved a minimum gross cost of 1366.3727 $/h, which is 0.4799% better than the next best algorithm and 3.2261% better than the worst. The results also show that FATA is robust in solving complex optimization problems in power systems, and it provides significant improvements in run time and convergence efficiency. The main advantage for readers is that FATA provides a reliable and efficient way to optimize power systems. Future work could also investigate the application of FATA in real time, as well as in larger power networks with more renewable energy sources.

This research introduces a novel multi-objective adaptation of the Geometric Mean Optimizer (GMO), termed the Multi-Objective Geometric Mean Optimizer (MOGMO). MOGMO melds the traditional GMO with an elite non-dominated sorting approach, allowing it to pinpoint Pareto optimal solutions through offspring creation and selection. A Crowding Distance (CD) coupled with an Information Feedback Mechanism (IFM) selection strategy is employed to maintain and amplify the convergence and diversity of potential solutions. MOGMO efficacy and capabilities are assessed using thirty notable case studies. This encompasses nineteen multi-objective benchmark problems without constraints, six with constraints and five multi-objective engineering design challenges. Based on the optimization results, the proposed MOGMO is better 54.83% in terms of GD, 64.51% in terms of IGD, 67.74% in terms of SP, 70.96% in terms of SD, 64.51% in terms of HV and 77.41% in terms of RT. Therefore, MOGMO has a better convergence and diversity for solving un-constraint, constraint and real-world application. Statistical outcomes from MOGMO are compared with those from Multi-Objective Equilibrium Optimizer (MOEO), Decomposition-Based Multi-Objective Symbiotic Organism Search (MOSOS/D), Non-dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Multi-Verse Optimization (MOMVO) and Multi-Objective Plasma Generation Optimizer (MOPGO) algorithms, utilizing identical performance measures. This comparison reveals that MOGMO consistently exhibits robustness and excels in addressing an array of multi-objective challenges. The MOGMO source code is available at https://github.com/kanak02/MOGMO.