Discussing the article: "Deterministic Oscillatory Search (DOS)"
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Check out the new article: Deterministic Oscillatory Search (DOS).
The article introduces Deterministic Oscillatory Search (DOS), a new metaheuristic algorithm that combines the advantages of traditional gradient-based methods with the efficiency of swarm algorithms, but completely avoids the use of random numbers.
Designed to solve complex global optimization problems in 2017 by Archana, DOS is based on the concept of oscillating particle motion in a search space with a deterministic distribution of initial positions. The key feature of the algorithm is its ability to handle multidimensional problems while maintaining full reproducibility: given the same initial conditions, the algorithm always arrives at the same result.
Unlike most metaheuristic algorithms, DOS introduces the concept of "fitness slope" - a mechanism that allows particles to assess whether their current direction is improving the solution and adapt their search strategy. Particles can be in one of three slope states: positive (movement improves the solution), negative (movement worsens the solution), or unknown.
This information is used to control the oscillatory behavior of the particles. When conventional gradient methods reach a point where all directions lead to a deterioration of the objective function, they stop. DOS overcomes this limitation through a swarming mechanism that is activated when oscillating motion fails to provide improvement. In this case, the particle begins to move in the direction of the best known global solution.
Author: Andrey Dik