Improvement of the whale optimization algorithm and its application to engineering design problems

Abstract

Abstract Aiming at the problems of insufficient global exploration ability, low convergence accuracy and slow speed of the standard whale optimization algorithm, the paper proposes a dimension-based neighborhood search strategy, which constructs a neighborhood for each search agent during iteration, and the search agents in this neighborhood can share the search information; considering that the motion of the search agent is a kind of jumping movement assuming successive jumps, which may cause the search agent to prematurely fall into local optimum, so adaptive weights are added to regulate the position update. The improved whale optimization algorithm (notated as: DWOA) is mainly used to solve global optimization and engineering design problems. DWOA and other excellent whale optimization algorithm improvement schemes are evaluated by 23 benchmark test functions and 5 engineering design problems, and the experimental results show that DWOA has strong competitiveness in terms of global exploration ability, local exploitation ability, convergence speed and convergence accuracy. Meanwhile, the improved algorithm has obvious advantages in solving engineering design problems, which also proves its effectiveness and applicability.