Jason Healing Optimization (JHO) Algorithm

版本 1.0.0 (2.2 KB) 作者: praveen kumar
sphere function is used.
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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.
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1.0.0