Hybrid harmony search algorithm for global optimization

Ammar, Marwa; Bouaziz, Souhir; Alimi, Adel M.; Abraham, Ajith

doi:10.1109/nabic.2013.6617841

Cited by 19 publications

(10 citation statements)

References 24 publications

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“…Therefore, the optimization of the FBBFNT had two parts; the tree structure evolution and the parameter evolution. A simultaneous evolution of architectures and learning parameters has been adapted using the Evolutionary Computation [20], [26], [31], [33], [39].…”

Section: Evolving Flexible Beta Basis Function Neural Treementioning

confidence: 99%

“…The new representation called Flexible Beta Basis Function Neural Tree (FBBFNT). This system adapted a simultaneous evolution of the structure and the parameters of the NN using different Evolutionary Computation algorithms such as Genetic programming [20], [31], Artificial Immune Systems [33], Particle Swarm Optimization, Differential Evolution, Bacterial Foraging Optimization Algorithm [39], Artificial Bee Colony [31], Harmony Search [26], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Multi-agent evolutionary design of Flexible Beta Basis Function Neural Tree

Ammar

Bouaziz

Alimi

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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Multi-Agent System (MAS) is a very active field that ensures global coherence between agents'interactions in a distributed way and implicit global control. Under the awareness of its power, the application of MAS was no more limited to very specific problems, but to almost application area: optimization, neural network, robotics, fuzzy system, etc. In the other side, a complex system of Artificial Neural Network called Flexible Beta Basis Function Neural Tree (FBBFNT) has reached a great level in the prediction search domain. In the purpose of enlarging the application of the algorithm to complex applications of the real problems, a new architecture of MAS was designed and applied to the FBBFNT process. This new multi-agent system based on communications and negotiations allowed the resolution of more complex prediction problems and the acceleration of the global convergence speed.

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Section: Evolving Flexible Beta Basis Function Neural Treementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-agent evolutionary design of Flexible Beta Basis Function Neural Tree

Ammar

Bouaziz

Alimi

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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show abstract

“…In this context, Evolutionary Computation EC has been considered as a good candidate for the ANN evolution [8]. EC includes Swarm Intelligence like Particle Swarm Optimization (P SO) [9], Evolutionary Algorithms such as Genetic Algorithm (GA) [10] as well as some Mimic Algorithms like Harmony Search (HS) [11] and so on.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent architecture for Multi‐objective optimization of Flexible Neural Tree

et al. 2016

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“…The new representation called Flexible Beta Basis Function Neural Tree (FBBFNT) [9], [10], [11], [12], is more flexible than the classical BBFNN seen that it can find automatically the number of nodes as well as the number of hidden layers. The FBBFNT is evolved by a hybrid algorithm with two levels: structure evolution and parameter evolution using evolutionary computation [13], [14], [15]. The performance of the evolving FBBFNT is tested for approximating some nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%