Leapfrogging and synoptic Leapfrogging: A new optimization approach

A novel optimization technique is introduced and demonstrated. Leapfrogging starts with a randomly located set of trial solutions (termed players) within the feasible decision variable (DV) space. At each iteration, the player with the worst objective function (OF) value is relocated to a random position within its DV-space reflection on the other side of the player with the best OF value. Test cases reveal that this simple algorithm has benefits over classic direct and gradient-based methods and particle swarm in speed of finding the optimum and in handling surface aberrations, including ridges, multi-optima, and stochastic objective functions. Potential limitations and analysis opportunities are discussed.

► Leapfrogging is a multi-particle direct search optimization algorithm.
► It starts with a randomly located set of trial solutions (termed players) within the feasible decision variable space.
► In the leap-over, the player with the worst objective function value is relocated to a random position on the other side of the player with the best value.
► Over 40 test cases reveal that this simple algorithm has benefits over classic optimization methods in speed of finding the optimum and in handling surface aberrations.