Backstabbing Optimization Algorithm (BOA)

sphere function is implemented

Inspiration

The algorithm mimics the behavior of individuals or entities competing within a group, where a "backstabbing" action involves one member outsmarting others to gain an advantage. This could symbolize adaptive strategies in optimization, where solutions deliberately disrupt or replace others to achieve better outcomes.

Steps in BOA

1. Initialization

• Generate a population of candidate solutions.
• Assign an initial "trustworthiness" or "rank" value to each solution.

1. Evaluation

• Evaluate the fitness of each solution using the objective function.

1. Backstabbing Mechanism

• Select a subset of solutions (e.g., the most trustworthy or highest-ranked ones).
• Within this subset, allow the lower-performing solutions to "backstab" (replace) higher-performing solutions by combining or mutating their attributes.
• This "backstab" could involve:
• Exploiting weaknesses in stronger solutions (e.g., focusing on poorly optimized parameters).
• Borrowing advantageous traits from other solutions.

1. Update Trustworthiness

• Adjust the trustworthiness of solutions based on their success in backstabbing:
• Increase for successful disruptors.
• Decrease for those that fail to outperform their targets.

1. Exploration and Exploitation

• Introduce random mutations or perturbations to encourage exploration.
• Refine the best solutions to enhance exploitation.

1. Convergence Check

• Repeat the process until a stopping criterion is met (e.g., maximum iterations, convergence, or satisfactory fitness).