Abstract
AbstractThe backpropagation (BP) algorithm is a gradient-based algorithm used for training a feedforward neural network (FNN). Despite the fact that BP is still used today when FNNs are trained, it has some disadvantages, including the following: (i) it fails when non-differentiable functions are addressed, (ii) it can become trapped in local minima, and (iii) it has slow convergence. In order to solve some of these problems, metaheuristic algorithms have been used to train FNN. Although they have good exploration skills, they are not as good as gradient-based algorithms at exploitation tasks. The main contribution of this article lies in its application of novel memetic approaches based on the Gravitational Search Algorithm (GSA) and Chaotic Gravitational Search Algorithm (CGSA) algorithms, called respectively Memetic Gravitational Search Algorithm (MGSA) and Memetic Chaotic Gravitational Search Algorithm (MCGSA), to train FNNs in three classical benchmark problems: the XOR problem, the approximation of a continuous function, and classification tasks. The results show that both approaches constitute suitable alternatives for training FNNs, even improving on the performance of other state-of-the-art metaheuristic algorithms such as ParticleSwarm Optimization (PSO), the Genetic Algorithm (GA), the Adaptive Differential Evolution algorithm with Repaired crossover rate (Rcr-JADE), and the Covariance matrix learning and Bimodal distribution parameter setting Differential Evolution (COBIDE) algorithm. Swarm optimization, the genetic algorithm, the adaptive differential evolution algorithm with repaired crossover rate, and the covariance matrix learning and bimodal distribution parameter setting differential evolution algorithm.
Funder
Ministerio de Economía, Industria y Competitividad, Gobierno de España
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Reference76 articles.
1. Abd-Elazim SM, Ali ES (2013) A hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers design. Int J Electr Power Energy Syst 46(1):334–341
2. Abd Elazim SM, Ali ES (2016) Optimal SSSC design for damping power systems oscillations via gravitational search algorithm. Int J Electr Power Energy Syst 82:161–168
3. Aldhafferi N, Owolabi TO, Akande KO, Olatunji SO, Alqahtani A (2018) Development of hybrid computational intelligence model for estimating relative cooling power of manganite-based materials for magnetic refrigeration enhancement. J Eng Appl Sci 13(6):1575–1583
4. Arora, S., Cohen, N., Golowich, N., Hu, W.: A convergence analysis of gradient descent for deep linear neural networks. CoRR abs/1810.0 (2018)
5. Azali S, Sheikhan M (2016) Intelligent control of photovoltaic system using BPSO-GSA-optimized neural network and fuzzy-based PID for maximum power point tracking. Appl Intell 44(1):88–110
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