Quantum Chaotic Butterfly Optimization Algorithm With Ranking Strategy for Constrained Optimization Problems

Nature-inspired metaheuristic optimization algorithms, e.g., the butterfly algorithm (BOA), have become increasingly popular. The BOA, which adapts food foraging and social behaviors of butterflies, involves randomly defined, algorithmic-dependent parameters that affect exploration exploitation strategies, negatively influences overall performance algorithm. To address this issue improve performance, paper proposes a modified i.e., quantum chaos BOA (QCBOA), relies on theory computing techniques. Chaos mapping unpredictable divergent behavior helps tune critical parameters, wave concept representative butterflies in explore search space more effectively. proposed QCBOA also implements ranking strategy to maintain balance between phases, is lacking conventional BOAs. evaluate reliability efficiency, tested against well-utilized set 20 benchmark functions travelling salesman problem belongs class combinatorial problems. Besides, method adopted photovoltaic system parameter extraction demonstrate its application real-word An extensive comparative study was conducted compare with BOAs, fine-tuned particle swarm (PSO) algorithm, differential evolution (DE), genetic (GA). results that, yield better for most cases rest cases. speed convergence increased compared expected provide other problems functions.