OPTUS Optimization Algorithm

Rastrigin function is tested

The OPTUS Optimization Algorithm could be a new or hypothetical metaheuristic inspired by concepts tied to the term "OPTUS." Since Optus is a well-known Australian telecommunications company, the algorithm might draw inspiration from communication networks, data optimization, or signal transmission.

Here’s a conceptual idea for the OPTUS Optimization Algorithm:

Key Inspiration:

The algorithm could emulate:

1. Signal Transmission in Networks:

• Mimic how data packets navigate through a network to find the shortest and most efficient path to the destination.

1. Resource Allocation:

• Optimize the distribution of bandwidth or network resources under constraints.

1. Fault Recovery:

• Reflect techniques in communication systems to handle interruptions and self-heal through alternate pathways.

Algorithm Framework:

1. Initialization:

• Define a population of potential solutions (nodes or "packets") randomly distributed in the search space.

1. Transmission Phase:

• Simulate packet routing by evaluating the fitness of solutions (e.g., using a fitness function representing optimization objectives).
• Utilize "nodes" (solutions) that collaborate and reroute based on their connectivity (inspired by routing protocols).

1. Optimization Steps:

• Error Correction (Fault Tolerance):
• Introduce a self-correction mechanism where poorly performing nodes adjust based on stronger neighboring solutions.
• Bandwidth Expansion (Exploration):
• Increase diversity by adding new candidate solutions into sparsely populated areas.
• Signal Amplification (Exploitation):
• Intensify search around high-performing solutions to refine results.

1. Termination:

• Stop when the algorithm converges to an optimal or near-optimal solution or after a set number of iterations.

Potential Applications:

• Network Optimization: For improving signal strength and bandwidth allocation.
• Data Transmission Systems: Minimizing data loss and optimizing transmission paths.
• General Optimization Problems: Adaptable to problems like scheduling, routing, or design optimization.