An Improved Whale Optimization Algorithm Based on Nonlinear Parameters and Feedback Mechanism

Abstract Whale optimization algorithm, as a relatively novel swarm-based intelligence has been extensively utilized in numerous scientific and engineering fields. The intent of this work was to devise modified WOA based on multi-strategy, named MSWOA, address somewhat deficiencies the original WOA, such converging slowly, stagnating at local minima poor stability. First, tent map function is adopted optimize distribution initial population problem domain. Second, new iteration-based update strategies convergence factor inertia weight are constructed regulate balance between global search capabilities improve ability. Additionally, an optimal feedback strategy presented for prey stage enhance Numerical experimental results 24 test benchmark functions reveal that proposed MSWOA significantly improves standard terms solution accuracy speed, outperforms comparison algorithms. Furthermore, show greatest effect performance basic performance, followed by factor, then strategy.