FDB-ChOA: An improved FDB-Chimp Optimization Algorithm

FDB-Chimp Optimization Algorithm (FDBChOA) was developed and validated on Global Optimization Problems and Power System Stabilizer

This article has two important contributions to the literature. These are, respectively, the development of the fitness distance balance-based chimp optimization algorithm (FDBChOA) as a powerful meta-heuristic search method that can be used in solving global optimization problems and the optimization of power system stabilizer parameters which is a popular real-world engineering problem. The chimp optimization algorithm (ChOA) is a recently developed population-based heuristic search method that mimics the social behavior of chimps. When the performance of ChOA on benchmark problems is analyzed, it is seen that there is a need for studies on algorithm design and improvement of the algorithm's ability to imitate nature in ChOA, as in many other newly developed meta-heuristic search methods. For this purpose, when the socialization processes of chimps are examined, it is assumed that designing the hunting process of attacker and chaser chimps based on the fitness-distance balance can improve the performance of the ChOA algorithm. In the studies carried out to test this hypothesis, the FDB-based hunting process for attacker and chaser chimps in which different strategies are applied and many FDBChOA variations were designed. The designed algorithms have been tested in the CEC 2020 benchmark suite and in optimizing the parameters of the power system stabilizer in a single-machine infinite bus power system. When the experimental study results were analyzed using statistical test tools, it was seen that FDBChOA variations could find better solutions than ChOA for global optimization problems and optimization of power system stabilizer parameters. It is evaluated that the FDBChOA developed in this paper can also find optimum solutions for many real-world engineering problems and global optimization problems.