Abstract
Nature-inspired metaheuristic optimization algorithms, e.g., the butterfly optimization algorithm (BOA), have become increasingly popular. The BOA, which adapts the food foraging and social behaviors of butterflies, involves randomly defined, algorithmic-dependent parameters that affect the exploration and exploitation strategies, which negatively influences the overall performance of the algorithm. To address this issue and improve performance, this paper proposes a modified BOA, i.e., the quantum chaos BOA (QCBOA), that relies on chaos theory and quantum computing techniques. Chaos mapping of unpredictable and divergent behavior helps tune critical parameters, and the quantum wave concept helps the representative butterflies in the algorithm explore the search space more effectively. The proposed QCBOA also implements a ranking strategy to maintain balance between the exploration and exploitation phases, which is lacking in conventional BOAs. To evaluate reliability and efficiency, the proposed QCBOA is tested against a well-utilized set of 20 benchmark functions and travelling salesman problem which belongs to the class of combinatorial optimization problems. Besides, the proposed method is also adopted to photovoltaic system parameter extraction to demonstrate its application to real-word problems. An extensive comparative study was also conducted to compare the performance of QCBOA with that of the conventional BOAs, fine-tuned particle swarm optimization (PSO) algorithm, differential evolution (DE), and genetic algorithm (GA). The results demonstrate that, chaos functions with the quantum wave concept yield better performance for most tested cases and comparative results in the rest of the cases. The speed of convergence also increased compared to the conventional BOAs. The proposed QCBOA is expected to provide better results in other real-word optimization problems and benchmark functions.
Highlights
Optimization is a procedure of determining the best solution for a specific problem under a set of given conditions
We have proposed the quantum chaos BOA (QCBOA) to overcome the primary limitations of the original butterfly optimization algorithm (BOA)
The position updation of exploration of butterflies was replaced by the quantum wave technique and the probability switch was replaced by a ranking strategy based on the fitness value of the butterfly’s position
Summary
Optimization is a procedure of determining the best solution for a specific problem under a set of given conditions. Natural surroundings have been the chief motivation for the mainstream utilization of the swarm-based optimization methods and are becoming increasingly dominant and widely acceptable for solving complex real-world optimization problems [2][3][4]. These meta-heuristic methods randomly execute the optimization process which typically begins by generating a population of random solutions. We introduce a chaos function, quantum computing technique, and a ranking strategy to improve the accuracy and performance of the BOA. We expect that the proposed algorithm will provide better results in various real-world problems and other benchmark functions.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
