Artificial Acari Optimization as a new strategy for global optimization of multimodal functions

In the article, the authors present the application of new optimization methods called Artificial Acari Optimization (AAO) and analyze it with other known algorithms for specific groups of benchmarks. The authors were inspired by the social behavior of Acari (mites) creating a strategy for herd searching for solutions. In the article results obtained for AAO were compared with other algorithms belonging to swarm intelligence: particle swarm optimization (PSO), glowworm swarm based optimization algorithm (GSO) and artificial bee colony (ABC). In a benchmark test four functions often used in testing methods of optimization were applied. These functions include: Ackley - with a relatively uniform surface, having tens of local minimums and one global maximum with a much lower value than most of the local minimums; Sphere with a single plane, having just one minimum; Easom characterized by a constant plane over overwhelming majority of domain with only one steep minimum, and Eggholder function with uneven surface having dozens of local minimums, where local minimums are close to the value of the only one global minimum.