Modified ESSA: a modified version of the metaheuristic Escape Strategies Algorithm (ESSA), a comparative study in real functions

The global optimization metaheuristic algorithm Escape Strategies Algorithm (ESSA) is a bio-inspired algorithm that uses swarm intelligence for the optimization of real-variable problems, proposed in 2016. It was tested on unconstrained real functions, demonstrating promising characteristics for optimization in continuous variables.

This study proposes a modified version of the Escape Strategies Algorithm (ESSA) metaheuristic, called MODIFIED ESSA. Changes are introduced in the particle initialization functions, as well as new dynamics in their updates; additionally, new operators are introduced in the metaheuristic algorithm. The quality of the solutions provided by the new version of the algorithm is compared to the original version of the metaheuristic, for a standard computational time. This comparison is performed using unconstrained nonlinear real functions as the objective function. The set of test functions includes convex and non-convex functions, as well as unimodal and multimodal functions, with the purpose of testing the algorithm's solutions in diverse environments.