Ganesan Optimization Algorithm(GOA)

sphere function is used

General Steps of a Hypothetical Ganesan Optimization Algorithm

1. Initialization: Randomly initialize a population of potential solutions (agents) within the search space.
2. Evaluation: Compute the fitness of each agent using the objective function.
3. 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.

1. Update Best Solutions: Track and update the best solutions found so far.
2. 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

• Like other metaheuristic algorithms, GOA could be used in fields such as:
• Engineering design optimization
• Machine learning model tuning
• Resource allocation problems
• Scheduling and logistics

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.