NL-SHADE-LBC algorithm with linear parameter adaptation bias change for CEC 2022 Numerical Optimization

Vladimir Stanovov, Shakhnaz Akhmedova, Eugene Semenkin

20222022 IEEE Congress on Evolutionary Computation (CEC)41 citationsDOI

Abstract

In this paper the adaptive differential evolution algorithm is presented, which includes a set of concepts, such as linear bias change in parameter adaptation, repetitive generation of points for bound constraint handling, as well as non-linear population size reduction and selective pressure. The proposed algorithm is used to solve the problems of the CEC 2022 Bound Constrained Single Objective Numerical Optimization bench-mark problems. The computational experiments and analysis of the results demonstrate that the NL-SHADE-LBC algorithm presented in this study is able to demonstrate high efficiency in solving complex optimization problems compared to the winners of the previous years' competitions.

Topics & Concepts

Constraint (computer-aided design)AlgorithmUpper and lower boundsDifferential evolutionMathematical optimizationSet (abstract data type)Adaptation (eye)Reduction (mathematics)Optimization problemComputer sciencePopulationLinear programmingMathematicsOpticsPhysicsGeometryDemographyMathematical analysisProgramming languageSociologyMetaheuristic Optimization Algorithms ResearchAdvanced Multi-Objective Optimization AlgorithmsAdvanced Optimization Algorithms Research