针对面临的问题寻找优化解是人类的思维习惯和行为方式。借鉴人类丰富的社会经验，模拟人类搜索活动的智能行为，提出了一种新的基于种群的启发式随机搜索算法，即人群搜索算法 SOA（seeker optimization Algorithm）。SOA 研究人类在随机搜索时所采用的交流、协作、记忆、推理、学习知识与经验等的智能行为，结合搜索和进化思想，以搜寻队伍为种群，以搜寻者位置为优化问题的候选解，通过模拟人类搜索的“经验梯度”和不确定性推理，分别确定搜索方向和步长，完成位置更新，实现对所求问题解的优化(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)