HBWO-JS: jellyfish search boosted hybrid beluga whale optimization algorithm for engineering applications

Abstract Beluga whale optimization (BWO) algorithm is a recently proposed population intelligence algorithm. Inspired by the swimming, foraging, and falling behaviors of beluga populations, it shows good competitive performance compared to other state-of-the-art algorithms. However, original BWO faces challenges unbalanced exploration exploitation, premature stagnation iterations, low convergence accuracy in high-dimensional complex applications. Aiming at these challenges, hybrid based on jellyfish search optimizer (HBWO-JS), which combines vertical crossover operator Gaussian variation strategy with fusion (JS) optimizer, developed for solving global this paper. First, fused JS improve problem that tends fall into best local solution exploitation stage through multi-stage collaborative exploitation. Then, introduced cross solves processes normalizing upper lower bounds two stochastic dimensions agent, thus further improving overall capability. In addition, forces agent explore minimum neighborhood, extending entire iterative process alleviating Finally, superiority HBWO-JS verified detail comparing basic eight algorithms CEC2019 CEC2020 test suites, respectively. Also, scalability evaluated three (10D, 30D, 50D), results show stable terms dimensional scalability. practical engineering designs Truss topology problems demonstrate practicality HBWO-JS. The has strong ability broad application prospects.