Solving Inverse Kinematics Problems Using Strait of Messina Evolutionary Algorithm

In this paper, we present our novel algorithm called Strait of Messina Evolutionary Algorithm (SOMA).This algorithm takes inspiration from different already well-researched algorithm families such as Bacterial Evolutionary Algorithms, Genetic Algorithms, and Differential Evolution.In SOMA, a three-phase evolutionary process during the iterations is realized.First, the Scylla Operator is executed, followed by the Charybdis Bacterial Mutation, and finally, a Differential Evolution-like phase completes the iteration.We tested our algorithm on four continuous benchmarks (Griewank, Levy, Rosenbrock, and Schweffel) with different dimensional conditions (10, 30, 50, 100), and on a set of inverse kinematics problems consisting of 100 different problems from the same robot model.We used Grey Wolf Optimization, Crayfish Optimization Algorithm, Whale Optimization Algorithm, and Dragonfly Algorithm for comparative analysis.Results show superior results in most of the cases applied, except for Griewank and Rosenbrock functions, where the Crayfish Optimization Algorithm turned out to be a good competitor.The presented algorithm's sourcecode for reproducibility can be accessed from https://github.com/SDFecht/SOMA.