An enhanced Pied kingfisher optimizer for UAV path planning and engineering design problems

The Pied Kingfisher Optimizer (PKO) is an advanced optimization algorithm. Its slow convergence and propensity to become stuck in local optima are its drawbacks, though. We suggest an Enhanced Pied Kingfisher Optimizer algorithm (EPKO) to overcome these drawbacks. In order to enhance the algorithm's exploratory position modifications and make it easier to identify the global optimum, tent mapping and an adaptive T-distribution control approach are used. Additionally, we present a Cauchy mutation method, which gives individuals a strong ability to avoid local extrema and guide the population in more advantageous directions. In order to improve the optimizer's search performance and greatly boost the algorithm's accuracy, speed, and stability for solving complicated issues, a leader-based boundary control technique is also suggested. We compare EPKO's performance against eight well-known algorithms in a number of dimensions using 29 CEC2017 benchmark functions. The efficacy of EPKO was demonstrated by the fact that our algorithm came out on top in every comparison. We also mathematically modeled the UAV and used a variety of competitor algorithms to address the UAV path planning problem in order to assess the suggested method's practicality. Additionally, we tackled three engineering design challenges using several competitor methods. The results show that EPKO has the best performance. When it comes to solution quality and stability, EPKO generally performs better than its competitors, demonstrating its greater application potential.