Introduction - If you have any usage issues, please Google them yourself
It is human's habit of thinking and behavior to find the optimal solution to the problems. Based on the rich social experience of human beings and simulating the intelligent behavior of human search activities, a new population-based heuristic random search algorithm, Seeker Optimization algorithm, is proposed. SOA This paper studies the intelligent behaviors of communication, cooperation, memory, reasoning, learning knowledge and experience used by human beings in random search, combines the ideas of search and evolution, takes the search team as the population, takes the location of the searcher as the candidate solution of the optimization problem, determines the search direction and step length respectively by simulating the "experience gradient" and uncertainty reasoning of human search, and completes the location update To optimize the solution of the problem