Parallel Multi-Population Technique for Meta-Heuristic Algorithms on Multi Core Processor

Đạt, Nguyễn Tiến; Kien, Cao Van; Ánh, Hồ Phạm Huy; Sơn, Nguyễn Ngọc

doi:10.1109/gtsd50082.2020.9303114

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“…Investigates [7] the whole search area in quest of new, more diversified solutions. The latter is a condensing technique that makes use of the data in the best solution identified throughout the local search region.…”

Section: Introductionmentioning

confidence: 99%

Review Optimized Artificial Neural Network by Meta-Heuristic Algorithm and its Applications

Kadhim

Mosa

2021

J. Al-Qadisiyah Comp. Sci. Math.

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A Meta-Heuristic Algorithms Optimization (MAHO) is inspired by nature. The Artificial neural network (ANN) has been shown to be successful in a variety of applications, including machine learning. ANNs were optimized using meta-optimization methods to enhance classification performance and predictions. The fundamental objective of combining a meta-heuristic algorithm (MHAO) with an artificial neural network (ANN) is to train the network to update the weights. The training would be speedier than with a standard ANN since it will use a meta-heuristic method with global optimal searching capability to avoid local minimum and will also optimize difficult problems. will discuss some of these meta-heuristic algorithms using ANN as they are applied to common data sets, as well as real-time specific classification and prediction experiences. In order to give researchers motivational insights into their own fields of application.

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Section: Introductionmentioning

confidence: 99%