This chapter introduces a novel swarm algorithm called Locust Search (LS), designed for complex optimization tasks. The LS algorithm, inspired by the behavior of locust swarms, models the cooperative interactions seen among these insects. It incorporates two distinct behavioral modes: solitary and social. Depending on the mode, individuals undergo evolutionary operators that mimic various cooperative behaviors observed in nature. The algorithm's effectiveness is demonstrated through a comparative analysis with other established evolutionary methods, using several standard benchmark functions commonly discussed in evolutionary algorithm research. The results show the algorithm's high proficiency in locating global optima across various benchmarks. Swarm Intelligence (SI), a field within artificial intelligence, serves as the foundation for LS, focusing on the development of intelligent, multi-agent algorithms derived from the collective behavior of social insects or animals. Over the years, many SI algorithms have been created to solve complex optimization challenges. However, not all algorithms perform equally well across different problems, leading to ongoing research aimed at discovering new techniques for better optimization performance.