Discussing the article: "The Blue Monkey (BM) Algorithm"

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Check out the new article: The Blue Monkey (BM) Algorithm.

The article presents an implementation of the Blue Monkey metaheuristic algorithm, which is based on a model of the social behavior of blue monkeys. The article examines the key mechanisms of the algorithm — the group structure of the population, following local leaders, and generational renewal through the replacement of the worst adults with the best offspring — and analyzes the test results.

To find the most suitable optimization algorithm for discrete problems involving trading robots, we will consider another method and its capabilities. Deep in the heart of the African jungle live creatures whose lives — filled with complex interactions and a sophisticated hierarchy — became the inspiration for one optimization method. We are talking about blue monkeys, or Cercopithecus mitis — these small, agile inhabitants of the tree canopy, whose seemingly chaotic behavior is in fact governed by deep biological laws, inspired the development of an algorithm named in their honor: Blue Monkey.

The algorithm was developed by M. Mahmood and B. Al-Khateeb and was published in 2019 in Periodicals of Engineering and Natural Sciences (PEN) 7(3):1054–1066, DOI:10.21533/pen.v7i3.621.

This complex yet harmonious ecosystem is reflected in the mathematical abstraction of the Blue Monkey algorithm:

  • Groups as Teams. The population of agents is divided into independent teams, each of which has its own leader, much like a dominant male.
  • The Leader — The Embodiment of Perfection. The best individual in each group, with the highest “fitness,” embodies the dominant male — a role model.
  • Offspring — The New Generation. Young individuals, the “offspring,” represent a new stage of development: they are learning and ready to take the place of their elders, bringing fresh ideas and solutions.
  • Migration — System Renewal. The migration process works by allowing the best “offspring” to replace less fit “adults,” ensuring continuous renewal and improved efficiency.
  • Social Learning — The Path to the Leader. The movement of the “offspring” toward the group leader, governed by mathematical formulas, mimics the process of social learning, in which the young learn from the experienced.


Author: Andrey Dik

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