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The Chimp Optimization Algorithm (ChOA) is a metaheuristic optimization algorithm inspired by the behavior and intelligence of chimpanzees. It involves processes such as chasing, attacking, and social learning to solve optimization problems. The algorithm is designed to optimize the Maximum Power Point (MPP) in fractional shading for solar PV systems.
The ChOA algorithm is applied to a partially shaded PV system, as shown in Fig. 24. In this system, the MPP from the solar PV module is delivered efficiently to the utility end. The algorithm uses equations (7-10) to model the hunting techniques of chimpanzees, which consist of two phases: exploitation and exploration.
The algorithm is designed to optimize the MPP in real-time, using dynamic coefficients (f, m, and C) to revise the function f. The function f reduces nonlinearly from 2.5 to 0 during exploration and exploitation via iteration, using random vectors r1 and r2, and regulation vectors and C. D represents distance between elements, and the chaotic vector m aligns with chimp sexual motivation in the search process.
The algorithm is tested on three case studies, including Pattern-1, Pattern-2, and Pattern-3, using ChOA algorithm. The simulation results of the three cases are presented in Fig. 30, 31, and 32, respectively. The results show that ChOA algorithm converges rapidly within seconds, contrasting with GWO’s higher average convergence time. Both ChOA and GWO excel in pursuing GWP under partial shading conditions, with ChOA achieving 531.198 W from the PV array.
The pseudocode for ChOA is presented, showing the initialize population of chimpanzees, define the maximum number of iterations, evaluate the fitness of each chimpanzee, and identify the best solution. The algorithm iterates until the maximum number of iterations is reached, updating the coefficients and dynamic factors, and evaluating the fitness of each chimpanzee. The best solution is returned as the final optimized result.