New ACCWOA algorithm based on the acceleration mechanism for solving engineering design problems and global optimization

The Accelerated Whale Algorithm (ACCWOA) is a new version of the Whale Optimization Algorithm (WOA) that incorporates a velocity factor into the individuals. The WOA is a nature-inspired metaheuristic algorithm widely applied to solve engineering problems, which uses shrinking and spiral insertion movements to exploit the solution, and random search to discover new solutions. This algorithm suffers from slow convergence in early iterations, a lack of fine-tuned local search, stagnation in local optima, a lack of mechanisms to maintain diversity, and a lack of historical learning. To address these limitations, this work proposes an acceleration technique that mimics the rapid movement of whales as they pursue their prey. Acceleration technology utilizes the velocity equation to achieve accelerated convergence, enhanced exploitation, improved diversity retention, dynamic behavior, increased scalability and stability, and emergent memory capabilities. The algorithm was evaluated on standard benchmarks, IEEE CEC-2014 and CEC-2017 suites, and five engineering design problems: spring, three-bar truss, pressure vessel, welded beam, and cantilever beam. Results show that ACCWOA achieves rapid convergence, accurate solutions, and competitive efficiency compared to state-of-the-art methods.