Results 171 to 180 of about 4,109,390 (219)

Air quality prediction via hybrid BiGRU-MLP using binary ant colony optimization and firefly algorithm for hyperparameter tuning. [PDF]

Sci Rep
Mahmoud AA, Alhammad SM, Fouad Y, Osman AM, El-Bakry HM, Elshewey AM.   +5 more
europepmc   +1 more source

Hybrid market design for decentralized energy trading: multi-k double auction with enhanced stable matching. [PDF]

Sci Rep
Reka N, Kuruseelan S.
europepmc   +1 more source

Research on predicting the state of health of lithium-ion batteries via back propagation network based on multi-feature combination of electrochemical impedance spectroscopy. [PDF]

PLoS One
Chen F, Sun S, Jia X, Ren Z, Huang X, Shu X, Li J.   +6 more
europepmc   +1 more source

Automated model selection in population pharmacokinetics using metaheuristic algorithms

Huang Z, Fidler M, Standing JF, Kloprogge F.   +3 more
europepmc   +1 more source

Automated modeling of population pharmacokinetic models using nlmixr2auto: a clinical trial and real-world data study

Huang Z, Fidler M, Nyang’wa B, Standing JF, Kloprogge F.   +4 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.
Marco Dorigo, Thomas Stützle, Tianjun Liao   +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   +3 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

An efficient Ant Colony Optimization algorithm for function optimization

2013 IEEE Congress on Evolutionary Computation, 2013
In this article we have proposed an efficient Ant Colony Optimization method, namely Guided Ant Colony Optimization (GACO) technique for optimizing mathematical functions. The search process of the optimization approach is directed towards a region or a hypercube in a multidimensional space where the amount of pheromone deposited is maximum after a ...
Gautam Garai, Shayantan Debbarman, Tamalika Biswas   +2 more
openaire   +2 more sources

Optimization and simulation research on Ant Colony Algorithm

2010 Sixth International Conference on Natural Computation, 2010
Aims at remedying the default of precocity and stagnation in the standard Ant Colony Algorithm(ACA),the rule of dynamic updating pheromones is presented, so that the area of feasible solutions are expanded, and the capability of global search is enhanced.
Fengtao Lin, Leping Liu
openaire   +1 more source