Learn and self-examination algorithm

This work aims to explore and improve the problems of the general optimized algorithm. General optimized algorithm is the mainstream method these days because it is suitable for a wide array of problems. Among all the options, genetic algorithm, particle swarm algorithm, and simulated annealing methods are the ones most commonly used to deal with difficult but regular fitness functions. Yet each method still has its pros and cons. Whether a particular method is employed is dependent on if it complies with fitness standards and the time it takes to fulfill a specific objective. Each fitness functions serve its own purposes. In light of the aforementioned needs, this work will propose an algorithm that is better than the general optimized ones.