2020/07/09 by Lorenzo Federici, Federici, Lorenzo, Boris Benedikter +3
Computer Science · Engineering · Mathematics · #Distributed #FOS: Computer and information sciences #FOS: Mathematics #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Optimization and Control (math.OC) #Parallel #Robotic Path Planning Algorithms #Vehicle Routing Optimization Methods #and Cluster Computing (cs.DC) #cs.DC #cs.NE #math.OC
paper · pdf · doi:10.48550/arxiv.2007.04681
2020 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Glasgow, UK
openalex publication_date 2020/07/09 · arxiv created 2020/07/13 · arxiv updated 2020/07/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents the main characteristics of the evolutionary optimization code named EOS, Evolutionary Optimization at Sapienza, and its successful application to challenging, real-world space trajectory optimization problems. EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm, namely, a self-adaptation of the control parameters, an epidemic mechanism, a clustering technique, an ε-constrained method to deal with nonlinear constraints, and a synchronous island-model to handle multiple populations in parallel. The results reported prove that EOSis capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms when applied to high-dimensional or highly-constrained space trajectory optimization problems.