Jason Healing Optimization (JHO) Algorithm

sphere function is used.

Jason Healing Optimization (JHO) AlgorithmConcept:

The JHO Algorithm emphasizes the idea of healing or improving solutions that are not performing well, similar to how a healer restores health. The approach uses a balance of exploration and a targeted healing process to optimize the objective function.

Key Features:

1. Healing Mechanism:

• Poorly performing solutions (those with high objective function values) are "healed" using a targeted improvement strategy. This healing can involve refining or adjusting these solutions to improve their performance.

1. Exploration and Exploitation:

• The algorithm includes an exploration phase to discover new areas in the search space and an exploitation phase where the healing process refines existing solutions.

1. Golden Guidance:

• The algorithm uses a "Golden Healer" (the best solution found so far) to guide the healing process. This guidance helps ensure that the search converges toward optimal regions efficiently.

Algorithm Flow:

1. Initialization: Generate an initial population of solutions randomly within the search space. Evaluate their fitness values using the objective function.
2. Healing Phase:

• Identify poorly performing solutions.
• Apply the healing mechanism to improve these solutions by moving them closer to better-performing regions or using small perturbations to enhance their fitness.

1. Exploration Phase:

• Introduce new solutions into the search space to maintain diversity and avoid local optima.

1. Golden Guidance: Use the Golden Healer (best solution) to influence both the healing and exploration processes.
2. Update and Repeat: If a healed or newly explored solution performs better than the Golden Healer, update the Golden Healer. Repeat the process until a stopping criterion is met.
3. Termination: The algorithm stops after a set number of iterations or when no significant improvement is observed.