Human-based algorithms are a type of meta-heuristic algorithm inspired by human behavior, problem-solving strategies, and social interaction. In this paper, human-based meta-heuristic algorithms are presented, as their advantages, limitations, and applications. This paper has an assessment of the rapid evolution of human-based metaheuristic thoughts, their covering towards a unified tissue, and the richness of possible applications in optimization problems. The paper briefly surveys some different human-based meta-heuristic algorithms aiming to solve optimization problems. Human-based algorithms have at least eleven algorithms: Driving Training-Based Optimization (DTBO), Chef-Based Optimization Algorithm (CBOA), Teaching–learning‑based optimization (TLBO), Technical and Vocational Education and Training-Based Optimizer (TVETBO), Sewing Training-Based Optimization (STBO), Volleyball Premier League Algorithm (VPL), Election-Based Optimization Algorithm (EBOA), Interior Search Algorithm (ISA), Social Engineering Optimizer (SEO), Human Behavior-Based Optimization (HBBO) and Seeker Optimization Algorithm (SOA). These algorithms mimic the problem-solving strategies employed by humans to tackle complex optimization tasks. From simulated annealing to genetic algorithms, HBOAs encompass a diverse range of techniques, each offering unique advantages and applications across various domains.