A Modified Monkey Algorithm for Real-Parameter Optimization

Swarm intelligence is an active research field that simulates the collective intelligence in swarms of insects or animals. Recently, several swarm-based search algorithms have been proposed. The monkey algorithm (MA) is one of the most recent swarm intelligence based algorithms which simulates the mountain climbing behavior of monkeys. In this article, an improved version of the monkey algorithm (IMA) is presented. In order to improve the algorithm performance, we modify climb and somersault processes which promote the exploration and exploitation abilities of the algorithm. The proposed IMA is applied on standard benchmark real-valued optimization problems. The results demonstrate that the proposed method is able to produce higher quality solutions with faster convergence than the original MA.