Ganesan Optimization Algorithm(GOA)

版本 1.0.0 (2.4 KB) 作者: praveen kumar

sphere function is used

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更新时间 2024/11/8

General Steps of a Hypothetical Ganesan Optimization Algorithm

Initialization: Randomly initialize a population of potential solutions (agents) within the search space.
Evaluation: Compute the fitness of each agent using the objective function.
Movement Rules:

Exploration: Use random or semi-random movements to allow agents to explore the search space broadly.
Exploitation: Refine solutions by guiding agents toward promising regions of the search space, possibly using strategies inspired by predator-prey dynamics or cultural heuristics.

Update Best Solutions: Track and update the best solutions found so far.
Termination: The algorithm continues iterating until a termination condition is met (e.g., a maximum number of iterations or a satisfactory fitness value).

Comparison with Established Algorithms

If GOA uses swarm intelligence, it would be similar to PSO or ACO, focusing on collective behavior to solve optimization problems.
If it involves evolutionary concepts, it would resemble Genetic Algorithms, which use selection, crossover, and mutation to evolve better solutions over generations.
The novelty would lie in how these mechanisms are combined or how new rules are introduced to mimic a unique optimization process inspired by Ganesan.

Possible Applications

Example Use Case

If your aim is to optimize a complex function, GOA could employ mechanisms like:
Attracting solutions toward areas with good fitness (similar to predators hunting prey).
Using random dispersal when solutions get stuck in local optima, mimicking prey escaping to avoid being caught.

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版本	已发布	发行说明
1.0.0	2024/11/8		下载