QANA: Quantum-based avian navigation optimizer algorithm

Differential evolution is an effective and practical approach that widely applied for solving global optimization problems. Nevertheless, its effectiveness scalability are decreased when the problems’ dimension increased. Hence, this paper devoted to proposing a novel DE algorithm named quantum-based avian navigation optimizer (QANA) inspired by extraordinary precision of migratory birds during long-distance aerial paths. In QANA, population distributed partitioning into multi flocks explore search space effectively using proposed self-adaptive quantum orientation consisted two mutation strategies, DE/quantum/I DE/quantum/II. Except first iteration, each flock assigned introduced success-based distribution (SPD) policy one strategies. Meanwhile, information flow shared through new communication topology V-echelon. Furthermore, we introduce long-term short-term memories provide meaningful knowledge partial landscape analysis qubit-crossover operator generate next agents. The QANA were extensively evaluated benchmark functions CEC 2018 2013 as LSGO results statistically analyzed Wilcoxon signed-rank sum test, ANOVA, mean absolute error tests. Finally, applicability solve real-world problems was four engineering experimental statistical prove superior competitor swarm intelligence algorithms in test 2013, with overall 80.46% 73.33%, respectively.