Abstract: The Whale Optimization Algorithm (WOA) is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic
of humpback whales. In spite of its popularity due to simplicity, ease of implementation, and a limited number of parameters,
WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation
in complex problems. To address this limitation, we propose a new algorithm called Multi-trial Vector-based Whale Optimization
Algorithm (MTV-WOA) that incorporates a Balancing Strategy-based Trial-vector Producer (BS_TVP), a Local
Strategy-based Trial-vector Producer (LS_TVP), and a Global Strategy-based Trial-vector Producer (GS_TVP) to address
real-world optimization problems of varied degrees of difficulty. MTV-WOA has the potential to enhance exploitation and
exploration, reduce the probability of being stranded in local optima, and preserve the equilibrium between exploration and
exploitation. For the purpose of evaluating the proposed algorithm's performance, it is compared to eight metaheuristic
algorithms utilizing CEC 2018 test functions. Moreover, MTV-WOA is compared with well-stablished, recent, and WOA
variant algorithms. The experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the
accuracy of the solutions and convergence rate. Additionally, we conducted the Friedman test to assess the gained results
statistically and observed that MTV-WOA significantly outperforms comparative algorithms. Finally, we solved five engineering
design problems to demonstrate the practicality of MTV-WOA. The results indicate that the proposed MTV-WOA
can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those
of other algorithms.

Key words: Swarm intelligence algorithms · Metaheuristic algorithms · Optimization · Engineering design problems · , Whale optimization algorithm