Journal Article

Flood algorithm (FLA): an efficient inspired meta-heuristic for engineering optimization

Trojovský, Pavel, Ghasemi, Mojtaba, Abualigah, Laith, Zare, Mohsen, Hemmati, Rasul, Golalipour, Keyvan, Mirjalili, Seyedali

2024

Overview

Introducing a novel meta-heuristic optimization algorithm, the Flood Algorithm (FLA) draws inspiration from the intricate movement and flow patterns of water masses during flooding events in river basins. FLA mathematically models key phenomena such as the movement of water toward slopes, flow rates over time, soil permeability effects, and periodic increases and decreases in water levels from precipitation and losses. Leveraging these models, the algorithm guides the movement and evolution of a population of potential solutions toward enhanced optimality. The algorithm endeavors to establish an appropriate correlation between the fundamental aspects of natural flood events and the optimization process. Its formulation and working mechanism are described in detail. It operates in two main phases—a regular movement phase, where the population moves naturally toward current best solutions, and a flooding phase, which introduces random disturbances to increase diversity. New solutions are periodically introduced while weaker ones are removed, mirroring the natural cycles of water levels. FLA’s effectiveness is demonstrated through its application on well-known benchmark optimization problems and engineering design problems. Extensive comparisons have been carried out on CEC2005 functions using 16 algorithms in both basic and enhanced modes, as well as on CEC2014 functions with dimensions 30, 50, and 100 using a total of 20 other algorithms. These rigorous studies unequivocally confirm the robustness and strength of the proposed algorithm. Furthermore, the algorithm's performance on 12 constrained engineering problems demonstrates its ability to tackle real-world challenges. The FLA’s source code is publicly available at https://www.optim-app.com/projects/fla .

• Publisher: Springer US,Springer Nature B.V
• Subject: Algorithms / Biogeography / Birds / Compilers / Computer Science / Cooperation / Design engineering / Design optimization / Flow distribution / Genetic algorithms / Heuristic / Heuristic methods / Interpreters / Optimization algorithms / Processor Architectures / Programming Languages / River basins / Soil permeability / Source code / Water levels / Water masses