Fatma A. Hashim

TL;DR: A novel metaheuristic algorithm named Henry gas solubility optimization (HGSO), which mimics the behavior governed by Henry’s law to solve challenging optimization problems, provides competitive and superior results compared to other algorithms when solving challenging optimize problems.

TL;DR: The experimental results suggest that AOA is a high-performance optimization tool with respect to convergence speed and exploration-exploitation balance, as it is effectively applicable for solving complex problems.

TL;DR: The experimental results, along with statistical analysis, reveal the effectiveness of HBA for solving optimization problems with complex search-space, as well as, its superiority in terms of convergence speed and exploration–exploitation balance, as compared to other methods used in this study.

TL;DR: The statistical simulation results revealed that the LFD algorithm provides better results with superior performance in most tests compared to several well-known metaheuristic algorithms such as simulated annealing (SA), differential evolution (DE), particle swarm optimization (PSO), elephant herding optimization (EHO), the genetic algorithm (GA), moth-flame optimization algorithm (MFO), whale optimization algorithm

TL;DR: In this paper , a novel nature-inspired metaheuristic algorithm named as snake optimizer (SO) is proposed to tackle a various set of optimization tasks which imitates the special mating behavior of snakes.