An adaptive hybrid atom search optimization with particle swarm optimization and its application to optimal no-load PID design of hydro-turbine governor

Abstract

One metaheuristic algorithm recently introduced is atom search optimization (ASO), inspired by the physical movement of atoms based on the molecular dynamics in nature. ASO displays a unique search ability by employing the interaction force from the potential energy and the constraint force. Despite some successful applications, it still suffers from a local optima stagnation and a low search efficiency. To alleviate these disadvantages, a new adaptive hybridized optimizer named AASOPSO is proposed. In this study, the individual and group cognitive components in particle swarm optimization (PSO) are integrated into ASO to accelerate the exploitation phase, and the acceleration coefficients are introduced to adaptively achieve a good balance between exploration and exploitation. Meanwhile, to improve the search performance of the algorithm, each individual atom possesses its own force constant, which is effectively and adaptively adjusted based on the feedback of the fitness of the atom in some sequential steps. The performance of AASOPSO is evaluated on two sets of benchmark functions compared to the other population-based optimizers to show its effectiveness. Additionally, AASOPSO is applied to the optimal no-load PID design of the hydro-turbine governor. The simulation results reveal that AASOPSO is more successful than its competitors in searching the global optimal PID parameters.

An Improved Mayfly Optimization Algorithm Based on Median Position and Its Application in the Optimization of PID Parameters of Hydro-Turbine Governor

“…In the simulation process, to test the robustness of the optimization algorithm, two typical working conditions are selected. Furthermore, 10% frequency disturbance experiments were carried out under the two working conditions respectively [47], [48]. Besides, Table 8 shows the characteristic parameters of the unit under two specific working conditions.…”

“…Therefore, it is critical to select the three parameters of the governor (K p , K i and K d ) correctly, which makes the turbine regulating system have superior dynamic quality, and which guarantees the safe operation of the units and the electric energy quality. In recent years, many experts and scholars have successively introduced different swarm intelligence optimization algorithms into the PID parameter optimization of the governor and conducted a lot of extensive and in-depth research, such as particle swarm algorithm [47], [48], bacterial foraging algorithm [49], moth-killing algorithm [50], fruit fly algorithm [51], beetle search algorithm [52] and cuckoo search algorithm [53], etc. Furthermore, these methods have their advantages, but there are also common problems such as long calculation time, easy to fall into the local optimum, and premature convergence, which are especially obvious in large-scale and complex problems.…”

A Two-Degree-of-Freedom PID Integral Super-Twisting Controller Based on Atom Search Optimizer Applied to DC-DC Interleaved Converters for Fuel Cell Applications

“…The ASO algorithm formulation begins with the generation of a set of potential samples for optimization. After each iteration, the atoms' locations and velocities are adjusted and the location of the best atom discovered is also updated [49]. Additionally, the atoms' acceleration is determined via two factors: the interaction force from the L-J potential represented by a combination of attraction and repulsion from other atoms and the constraint force caused by the bond length potential, which is the weighted difference in position between each atom and the best atom.…”

Multi‐objectivequantum atom search optimization algorithm for electric vehicle charging station planning

“…Korkmaz and Akgüngör 26 proposed a hybrid ASO algorithm by combining ASO and the grasshopper optimization algorithm (GOA) to identify the optimum cycle length in a traffic control system. Since PSO has a faster convergence rate than ASO, Zhao et al 27 combined ASO and PSO to improve the convergence speed of the basic ASO algorithm and employed it to tune the PID parameters of a hydro‐turbine governor. All the references cited in this paragraph illustrate the potential ability of the ASO algorithm to solve a wide range of problems with modifications or hybridization.…”