Liying Wang

TL;DR: The comparison results on the benchmark functions suggest that MRFO is far superior to its competitors, and the real-world engineering applications show the merits of this algorithm in tackling challenging problems in terms of computational cost and solution precision.

TL;DR: A novel physics-inspired metaheuristic optimization algorithm, atom search optimization (ASO), inspired by basic molecular dynamics, is developed to address a diverse set of optimization problems.

TL;DR: The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts, especially for real-world engineering problems, and is more competitive than other reported methods in terms of both convergence rate and computational efforts.

TL;DR: The experimental results demonstrate that ASO can outperform other well-known approaches such as Particle Swarm Optimization, Genetic Algorithm and Bacterial Foraging Optimization and thatASO is competitive to its competitors for parameter estimation problems.

TL;DR: The proposed supply-demand-based optimization algorithm is compared with other state-of-the-art counterparts on 29 benchmark test functions and six engineering optimization problems and proves that SDO is able to provide very promising results in terms of exploration, exploitation, local optima avoidance, and convergence rate.