An Application of Slime Mould Algorithm for Optimizing Parameters of Power System Stabilizer

Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer

“…Overcoming these shortcomings as well as getting along with the merits of using the metaheuristic optimization algorithms like escaping from local optima, the PSS can be optimally tuned efficiently. Recently, a significant number of such algorithms published like, Chaotic Particle Swarm Optimization [6], whale optimization algorithm [7], enhanced whale optimization algorithm [8], improved Moth flame optimization [9], An antlion optimization [10], Slime Mould Algorithm [11], Coyote Optimization Algorithm [12], Henry Gas Solubility Algorithm [13], collective decision algorithm [14], Particle Swarm Optimization [15], Cuckoo Search Algorithm [16], Salp Swarm Algorithm [17], hybrid dynamic GA-PSO algorithm [18], atom search algorithm [19], Runge Kutta optimizer [20], Genetic Algorithms [21], kidney-inspired algorithm [22], modified harmonic search algorithm [23], sine cosine algorithm [24], Harmony Search [25], farmland fertility algorithm [26]- [29], Bat Algorithm [30], Honey Bee Mating Optimization [31], Jaya Algorithm [32], [33], Grey Wolf Optimizer [34], Backtracking Search Algorithm [35], Grasshopper Optimization Approach [36], Rat Swarm Optimization [37]. Harris Hawk Optimizer [38].…”

“…Hence, the previous equations can be linearized as follows to constitute the wellknown Heffron-Philips model with constants (K1--K6): All these equations can be arranged in a matrix form to present the state-space model as given in Eqs. (10,11).…”

An Equilibrium Optimizer Slime Mould Algorithm For Inverse Kinematics Of The 7-DOF Robotic Manipulator

“…Liu et al [22], Mostafa et al [23] and Yousri et al [24] used hybrid and improved SMA to estimate parameters of solar photovoltaic cells, respectively; Agarwal and Bharti [25] applied improved SMA to the collision-free shortest time path planning of mobile robots; Rizk-Allah et al [26] proposed a chaos-opposition-enhanced SMA (CO-SMA) to minimize the energy costs of wind turbines at highaltitude sites; Hassan et al [27] applied improved SMA (ISMA) to efficiently solve economic and emission dispatch (EED) problem with single and dual objectives; Abdollahzadeh et al [28] proposed a binary SMA to solve the 0-1 knapsack problem; Zubaidi et al [29] combined SMA and artificial neural network (ANN) for urban water demand prediction; Chen and Liu [30] combined Kmeans clustering and chaotic SMA with support vector regression to obtain higher prediction accuracy; Ekinci et al [31] applied SMA to the power system stabilizer design (PSSD); Wazery et al [32] combined SMA and K-nearest neighbor for disease classification and diagnosis system; Wei et al [33] developed a simpler SMA for the problem of wireless sensor network coverage; Wei et al [34] proposed an enhanced SMA in power systems for optimal reactive power dispatch; Premkumar et al [35] and Houssein et al [36] developed multi-objective SMA (MOSMA) for solving complicated multi-objective engineering design problems in the real world; Yu et al [37] proposed an improved SMA (WQSMA) that enhanced the original SMA's robustness by using a quantum rotation gate (QRG) and a water cycle operator. Houssein et al [38] proposed a hybrid SMA and adaptive guided differential evolution (AGDE) algorithm, which makes a good combination of SMA's exploitation ability and AGDE's exploration ability.…”

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

“…The slime mould algorithm (SMA) (Li et al, 2020) is one of those metaheuristic algorithms that has been proposed as a novel optimization approach to address specific problems. It has already been proved to be capable for predicting the demand for urban water (Zubaidi et al, 2020), estimating photovoltaic cell parameters (Kumar et al, 2020), minimizing the cost of energy for the wind turbines on high-altitude sites (Rizk-Allah et al, in press), coordinating the directional overcurrent relays (Draz et al, 2021), mitigating the effects of magnetic coupling between high voltage transmission line and metallic pipeline (Djekidel et al, 2021), estimating the parameters of proton exchange membrane fuel cells (Gupta et al, 2021), tuning cost-effective fuzzy controllers for servo systems (Precup et al, 2021), optimizing parameters of power system stabilizer (Ekinci et al, 2020d), and synthesizing thinned concentric circular antenna arrays (Durmus, 2020). Those listed applications are only a few to name that employ the SMA for better efficiency.…”