The integration of the Osprey Optimization Algorithm with Black-winged Kite Algorithm Enhances Optimization Capabilities
A novel optimization approach aims to solve complex engineering challenges by merging biological inspiration with algorithmic efficiency.
Recent advancements can often be found at the intersection of biological processes and computational strategies, and the latest research contribution adds another dimension to this growing field. The Black-winged Kite Optimization Algorithm (DKCBKA), enhanced by the Osprey Optimization Algorithm and employing vertical and horizontal crossover techniques, addresses previous limitations and significantly boosts performance metrics.
Developed to combat inadequacies within the traditional Black-winged Kite Algorithm—such as inconsistent accuracy and local optima issues—the DKCBKA introduces dynamic factors and innovative methodologies to refine optimization processes. This improvement is particularly notable across complex, nonlinear problems encountered within engineering, economics, and bioinformatics.
To substantiate these enhancements, fifteen standard benchmark functions were utilized to compare the DKCBKA against five established swarm intelligence algorithms and six improvement strategies. Findings revealed superior accuracy and convergence speed with the DKCBKA, making it commendable for practical applications such as engineering problem-solving. For example, the study indicates improvements of up to 99.885% when applied to engineering optimization scenarios.
"The proposed DKCBKA not only outperforms previous methods but also demonstrates enhanced robustness through innovative crossover strategies, significantly boosting its global and local search capabilities," said the authors of the article, highlighting the potential impacts of their findings on real-world applications.
The underlying mechanisms for the DKCBKA’s success include both the integration of the Osprey method during the attack phase—allowing for improved dynamic adaptation—and the incorporation of stochastic differential mutations during the migration stage to maintain population diversity.
This algorithm also efficiently handles population dynamics through vertical and horizontal crossovers. Such crossovers balance exploration and exploitation, ensuring the algorithm can escape local optima and discover superior solutions across various dimensions.
For clarity, the method operates by first initializing the positions of the Black-winged Kites and iteratively updating their fitness scores based on their location and the performance of their peers. The inclusion of advanced techniques fosters enhanced exploration capabilities and effective search mechanisms.
Simulation results underline these advantages, showcasing the DKCBKA's effectiveness across diverse optimization challenges, reflected by highly favorable performance metrics. To date, implementations of this novel algorithm have illustrated substantial improvements, particularly noted during tests against the CEC2017 and CEC2019 standard function sets.
When applied to engineering optimization problems, such as pressure vessel design and gear train optimization, the DKCBKA has demonstrated significant cost reductions and improved design efficiency. One notable case reported reductions of 18.222% below traditional methods, underscoring the algorithm's practical usability.
Looking forward, the DKCBKA holds promise not only for existing challenges but also offers fertile ground for future research. The authors recommend extending its applications to complex real-world scenarios like offshore wind turbine foundation optimizations among others.
Through its advancements, the DKCBKA stands poised to improve how optimization problems are approached, blending biological insights with cutting-edge computational methodologies.