Ant Lion Optimizer Based on Spatial Information

Optimization problems widely exist in different real‐world applications. Ant lion optimizer (ALO) is an effective optimization algorithm that simulates the hunting behavior of antlions, but it will easily fall into local optima. To address this issue, this paper proposes a modified version of ALO called spatial ant lion optimizer (SALO). SALO makes full use of spatial informations of solutions to promote its searchability. It utilizes a distance‐based indicator instead of objective fitness to choose ant lions for updating solutions. Besides, it maintains a candidate enhanced archive by a distance‐based updating approach and implements a hybrid‐enhanced strategy in the archive to search for better solutions. Exhaust experiments are carried out through six classic benchmark functions and seven state‐of‐the‐art compared algorithms, and the results validate the superior performance of SALO.