A Whale Optimization Algorithm with Convergence and Exploitability Enhancement and Its Application

The whale optimization algorithm (WOA) is inspired by humpback social behavior and WOA a popular swarm intelligence algorithm. Yet, the does have certain flaws, such as restricted global search capability an inconsistent convergence speed, when dealing with complex problems, easy to fall into local optimum. To address WOA’s shortcomings, modified exploitability enhancement called MWOA-CEE proposed. Three operators, opposition-based learning, exponentially decreasing function, elite-guided Cauchy mutation, are integrated enhance exploration performance avoid First, learning used concept generate opposite position of candidate solution, which can ability quality solution. Then, function balances exploitation phases boosts capability. In end, mutation generated wide disturbance near current improves solutions diversity increases algorithm’s validated on 19 benchmark functions 14 CEC2014 identify IIR systems. It compared seven commonly algorithms. results prove that outperforms other algorithms in terms final solution rate.