Hi Wendong,
Self-adaptive multi-objective teaching-learning-based optimization (MO-TLBO) is an evolutionary algorithm inspired by the teaching-learning process in a classroom. It aims to solve multi-objective optimization problems by finding a set of Pareto-optimal solutions. Implementing such an algorithm in MATLAB involves several steps. Below is a basic outline and some guidance on how you might set this up.
Key Concepts
- A set of candidate solutions, each representing a possible solution to the optimization problem.
- In a multi-objective context, there are multiple objectives to be optimized simultaneously, often leading to a set of trade-off solutions known as the Pareto front.
- Teaching Phase:
- Teacher: The best solution in the current population acts as the teacher.
- Teaching Factor (TF): Determines the influence of the teacher on the learners. Typically set to 1 or 2.
- Update Rule: Learners (other solutions) are updated by moving towards the teacher, aiming to improve their quality.
4. Learning Phase:
- Peer Interaction: Learners interact with each other to further improve their solutions.
- Update Rule: A learner is updated by comparing it with another randomly selected learner. The interaction is based on whether the other learner is better or worse.
5. Self-Adaptation: Parameters such as the teaching factor or learning rates can be adapted based on the algorithm's progress. This helps the algorithm balance exploration and exploitation dynamically.
6. Dominance: A solution is said to dominate another if it is no worse in all objectives and better in at least one objective.
7. Pareto Front: The set of non-dominated solutions that represent the best trade-offs among the objectives.
Algorithm Steps
- Generate an initial population of solutions randomly within the problem's constraints.
- Evaluate the fitness of each solution based on the multiple objectives.
- Teaching Phase:
- Identify the best solution as the teacher.
- Update each learner by moving it towards the teacher, using the teaching factor to control the influence.
4. Learning Phase:
- For each learner, select another learner randomly.
- Update the learner based on its interaction with the selected peer, either moving towards or away from it depending on their relative performance.
5. Self-Adaptation: Adjust parameters like the teaching factor based on the diversity or convergence of the population.
6. Iteration: Repeat the teaching and learning phases for a set number of iterations or until convergence criteria are met.
7. Pareto Front Extraction: At the end of the iterations, extract the Pareto-optimal solutions from the population.
