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In the present study, a new algorithm is developed for neural network training by combining a gradient-based and a meta-heuristic algorithm. The new algorithm benefits from simultaneous local and global search, eliminating the problem of getting stuck in local optimum. For this purpose, first the global search ability of the grey wolf optimizer (GWO) is improved with the Levy flight, a random walk in which the jump size follows the Levy distribution, which results in a more efficient global search in the search space thanks to the long jumps. Then, this improved algorithm is combined with back propagation (BP) to use the advantages of enhanced global search ability of GWO and local search ability of BP algorithm in training neural network. The performance of the proposed algorithm has been evaluated by comparing it against a number of well-known meta-heuristic algorithms using twelve classification and function-approximation datasets. [ABSTRACT FROM AUTHOR]

Nature-inspired metaheuristic algorithms are considered as the most effective techniques for solving various optimization problems. This paper provides a briefly review of the key features of the cuckoo-inspired metaheuristics: cuckoo search (CS) and cuckoo optimization algorithm (COA). In addition, it discusses some of their important and emerging studies, investigates their applications in several fields, and finally clarifies the differences between both algorithms so as to remove confusion between them. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are being used increasingly to forecast rainfall. In this study, several meta-heuristic algorithms are applied to train an ANN in order to improve the accuracy of rainfall forecasting. Centripetal accelerated particle swarm optimization (CAPSO), a gravitational search algorithm and an imperialist competitive algorithm train a multilayer perceptron (MLP) network as a feed-forward ANN for rainfall forecasting in Johor State, Malaysia. They are employed to forecast the average monthly rainfall in the next 5 and 10 years using the two modes of original (without data preprocessing) and data preprocessing with singular spectrum analysis. Additionally, for each month, the average monthly rainfall during the last 5 years is computed and a month with less rainfall than the average is classified as 0 (light rainfall month), otherwise as 1 (heavy rainfall month). The attributes used in the classification can be applied to forecast the monthly rainfall. The proposed methods integrate the accuracy and structure of ANN simultaneously. The result showed that the hybrid learning of MLP with the CAPSO algorithm provided higher rainfall forecasting accuracy, lower error and higher classification accuracy. One of the main advantages of CAPSO compared with the other algorithms to train MLP includes the following: The algorithm has no need to tune any algorithmic parameter and it shows good performance on unseen data (testing data). [ABSTRACT FROM AUTHOR]