Seven Sisters Optimization Algorithm

This research proposes a new swarm-based meta-heuristic algorithm named Seven Sisters Optimizer (SSO) in-spired by the Jungle Babbler bird flock (commonly known as Seven sisters or Seven Brothers (in Hindi)). Jungle Babbler's calls are structurally and functionally organized to serve as a coherent communication system. They have 15 different types of communication classified as affiliative (friendly), with seven call types, and agonistic (unfriendly), with eight call types. The flock head, the sentinel bird, is tasked with the keep tab on optimal habitat and orients the flock members as affiliative and agonistic. The proposed algorithm is tested on 13 well-known benchmark functions. The results of the proposed SSO are verified by a comparative study of of SSO with nine other existing optimization algorithms, which include the most widely used meta-heuristics, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as well as Gravitational Search Algorithm (GSA), Flower Polli-nation Algorithm (FPA), Moth-Flame Optimization (MFO), Bat Algorithm (BOA), Firefly Algorithm (FA), Cuckoo Search Algorithm (CS) along with high-performance optimizer LSHADE-SPACMA (LSP). To evaluate the constraint handling capacity of the proposed algorithm ie SSO, it is further tested on a gear design problem, and the results are compared with different classical and swarm-based optimization approaches. The results demonstrate that the proposed SSO exploration and exploitation capabilities are superior as compared to the other optimizers.