Monkey Algorithm: Local Climbing and Global Jumps in Population Search

Summary

This article describes the Monkey Algorithm, a population-based metaheuristic that searches a fitness landscape through repeated local improvement and broader exploration. Each candidate begins at a random position, climbs through local moves, and makes local jumps in an attempt to find a better point. When local search stalls, a global jump relocates it, with the article’s implementation directing the jump beyond the geometric center of the population. The cycle continues until a stopping condition is met.

The author explains the algorithm’s parameters and implementation, and discusses experiments comparing it with other optimization methods. The reported assessment is mixed: the classical method struggled with local optima and discrete functions, while changes to global jumps improved convergence indicators and result stability. The article also notes that climbing can require many iterations, making the method less suitable for computationally demanding problems. These are optimization experiments rather than trading tests, and the conclusions reflect the author’s implementation and benchmark setup.

Key ideas

• The algorithm alternates local climbing and local jumps to improve each candidate solution.
• When local search stalls, a global jump is used to explore a different region of the search space.
• The described implementation biases global jumps beyond the population’s geometric center.
• The author reports that modifications to global jumps improved convergence and result stability in experiments.
• The method can require many iterations and performed poorly on discrete functions in the reported tests.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.