Quantum encoding whale optimization algorithm for global optimization and adaptive infinite impulse response system identification

Abstract

The whale optimization algorithm (WOA) is motivated by the predatory nature of bubble nets and mimics dwindling and encircling, bubble net persecuting, and randomized wandering and foraging actions to locate the expansive adequate value. However, the WOA has several deficiencies: inadequate resolution accuracy, sluggish convergence speed, susceptibility to search stagnation, and insufficient localized detection efficiency. A quantum encoding WOA (QWOA) is introduced for global optimization and adaptive infinite impulse response (IIR) system identification. The quantum encoding mechanism exploits the principle of a quantum bit to encode a search agent, which manipulates the state of an essential quantum bit and amends the location data. The quantum rotation gate modulates the quantum bit’s configuration, the quantum NOT gate accomplishes bit mutation and prohibits precocious convergence. The probability amplitude of the quantum bit represents the multistate superposition state of the search agent, which enriches the population diversity, advances individualized information, broadens the detection scope, inhibits premature convergence, facilitates estimation effectiveness, and promotes solution accuracy. The QWOA not only promptly locates the search scope nearest the most appropriate solution but also computes the spiral-shaped encircling route to promote predation diversification. Twenty-three benchmark functions, eight real-world engineering layouts, and adaptive IIR system identification are utilized to assess the QWOA’s feasibility and effectiveness. The experimental results reveal that QWOA successfully equalizes exploration and exploitation to accelerate convergence speed, ameliorate calculation accuracy, and strengthen stability and robustness.