10 papers with code • 0 benchmarks • 1 datasets
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem. For some examples, you can visit https://aliasgharheidari.com/Publications.html
These leaderboards are used to track progress in Metaheuristic Optimization
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design
Inverse molecular design, i. e., designing molecules with specific target properties, can be posed as an optimization problem.
Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design
This paper introduces Gnowee, a modular, Python-based, open-source hybrid metaheuristic optimization algorithm (Available from https://github. com/SlaybaughLab/Gnowee).
In this algorithm, global optima is modeled as sea edge (coast) to which Gammarus creatures are willing to move in order to rest from sea waves and forage in sand.
The experimental results on several well-known benchmarks show the outperforming performance of TBO algorithm in finding the global solution.
We consider shot-based video summarization where the summary consists of a subset of the video shots which can be of various lengths.
The proposed method is inspired by a genre of digital games knowns as “battle royale.” BRO is a population-based algorithm in which each individual is represented by a soldier/player that would like to move toward the safest (best) place and ultimately survive.
This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs).
A cost function is first formulated to convert the path planning into an optimization problem that incorporates requirements and constraints for the feasible and safe operation of the UAV.