An Improved Seagull optimization Algorithm for Solving Engineering optimization Problems

Abstract

To achieve optimal cost as nearly as possible in the engineering design problem and overcome the problems of slow convergence speed, precocity and low precision of the Seagull Optimization Algorithm in solving high-dimensional problems, this paper proposes an Improved Seagull Optimisation Algorithm (ISOA) that incorporates the dimensionally learning-based hunting (DLH) search strategy. It integrates the DLH search strategy into the iterative process of the algorithm, allowing the search agents in the same domain to share information with each other, which improves the balance between local and global search, and also effectively improves the population diversification in the mid-and late-stage, and enhances the optimality search performance and application capability of the ISOA. The experimental results demonstrated that ISOA was effective and practical for engineering optimization problems, achieved the optimal cost and cost-effectiveness for all the engineering optimization problems with optimal cost, and indicated the success of the algorithm improvement in this paper.