Results 151 to 160 of about 13,344 (202)

Data-driven flood susceptibility assessment using hybrid machine learning and optimization techniques: case of the Sedrata Watershed, NE Algeria. [PDF]

Sci Rep
Mechentel E, Dairi S, Lefkir A, Eslamian S, Abida H, Djebbar Y.   +5 more
europepmc   +1 more source

Optimization of A comprehensive dispatching system based on ant colony algorithm and dynamic weight power dispatching strategy. [PDF]

Sci Rep
Ming J, Xie Z, Teng H.
europepmc   +1 more source

A Q-learning-based hybrid search algorithm integrating PRM and ACO for 3D UAV path planning. [PDF]

Sci Rep
Liu S, Cui Y, Yu Y, Hu W.
europepmc   +1 more source

Lightweight Detection and Adaptive Path Planning for Selective Hotan Rose Harvesting. [PDF]

Sensors (Basel)
Lin J, Yang Y, Ma B, Wu Z, Chen B.
europepmc   +1 more source

AgriPath: a robust multi-objective path planning framework for agricultural robots in dynamic field environments. [PDF]

Front Plant Sci
Yang C, Zheng D, Chen S, Mansurova M, Belgibaev B, Zhao B.   +5 more
europepmc   +1 more source

Real-world evaluat ion of hybrid Green AI for sustainable and efficient smart supply chain distribution. [PDF]

Sci Rep
Hassouna MA, Aboutabl AE, Diaa NM, Haggag RY.   +3 more
europepmc   +1 more source

A unified ant colony optimization algorithm for continuous optimization [PDF]

European Journal of Operational Research, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tianjun Liao, Thomas Stützle, Marco Dorigo   +2 more
exaly   +2 more sources

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Structural topology optimization using ant colony optimization algorithm

Applied Soft Computing Journal, 2009
The ant colony optimization (ACO) algorithm, a relatively recent bio-inspired approach to solve combinatorial optimization problems mimicking the behavior of real ant colonies, is applied to problems of continuum structural topology design. An overview of the ACO algorithm is first described.
Guan-Chun Luh
exaly   +2 more sources

On the Faster Ant Colony Optimization Algorithm

2009 Fifth International Conference on Natural Computation, 2009
The pheromone trails in ACO are used to reflect the ants’ search experience, so the quality of the pheromone is crucial to the success of ACO. The main factors affecting the quality of the pheromone include the strategy of updating the pheromone and the quality of the constructed solutions.
Yingzhou Bi, Lixin Ding, Jianbo Lu
openaire   +1 more source