Bahubali-Inspired Optimization Algorithm (BIOA)

This optimization is based on Rajamouli's fictional story Bhagubhali, where all the characters are incorporated.

Pseudocode for Bahubali-Inspired Optimization Algorithm (BIOA)

This pseudocode outlines the implementation of BIOA with inspiration from characters and phases in the Bahubali story.

Initialization

1. Set problem parameters:

• Number of variables, population size, iteration limit.
• Define bounds for the variables [varmin,varmax][var_{min}, var_{max}][varmin​,varmax​].

1. Define influence factors for each character and phase:

• Loyalty, power, justice, strategy, love, sacrifice, support, war.

1. Initialize the population with random values within the bounds.
2. Compute initial fitness values for the population.

Optimization Loop (for each iteration until max_iter)

1. Evaluate Fitness:

• Compute the fitness of each individual.
• Update the best solution and best fitness found so far.

1. Loyalty Phase (Exploration - Kattappa):

• Adjust individuals with small random steps to explore the search space.

1. Power Phase (Exploitation - Bhallaladeva):

• Move individuals towards the current best solution with a probabilistic step.

1. Justice Phase (Diversity - Mahendra Bahubali):

• Introduce diversity by moving individuals towards randomly chosen peers.

1. Strategy Phase (Adaptation - Amarendra Bahubali):

• Replace individuals with randomly generated solutions if they are better.

1. Love Phase (Collaboration - Avantika and Bahubali):

• Combine the traits of two randomly selected individuals to create hybrids.
• Replace a parent with the hybrid if it improves the fitness.

1. Sacrifice Phase (Guidance - Rajamatha):

• Guide individuals closer to the best solution with small calculated steps.

1. Support Phase (Collective Influence - People):

• Adjust individuals towards the average position of the population.

1. War Phase (Coordinated Attack - Kattappa and Mahendra Bahubali):

• Use strategic coordination to move individuals towards a strategic point.

1. Boundary Handling:

• Ensure all individuals remain within [varmin,varmax][var_{min}, var_{max}][varmin​,varmax​].

1. Progress Update:

• Display iteration number and best fitness so far.