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Neural Network Algorithm (NNA)

A new metaheuristic optimization algorithm, inspired by biological nervous systems and artificial neural networks (ANNs) is proposed for solving complex optimization problems. The proposed method, named as neural network algorithm (NNA), is developed based on the unique structure of ANNs. The NNA benefits from complicated structure of the ANNs and its operators in order to generate new candidate solutions. Being an algorithm without any effort for fine tuning initial parameters and statistically superior can distinguish the NNA over other reported optimizers. It can be concluded that, the ANNs and its particular structure can be successfully utilized and modeled as metaheuristic optimization method for handling optimization problems.

Based on the ANNs terminology, the NNA is an adaptive unsupervised method for solving optimization problems. Unsupervised in NNA means there is no clue and information of global optimum and the solutions have been updated by learning from the environment. The NNA is a single-layer perceptron optimization method having self-feedback.

Fig. 1. Schematic view of generating new pattern solutions.

Fig. 2. Processes of the NNA.

Fig. 3. Schematic view for the performance of the NNA.

Interested readers may download open source codes of the NNA using the below link:

Neural Network Algorithm (NNA) (Standard) Source Code  (Written in MATLAB)

Neural Network Algorithm (NNA) (Standard) Source Code for solving Unconstrained Benchmark (F1-F21) Optimization Problems (Written in MATLAB)

Neural Network Algorithm (NNA) (Standard) Source Code for solving Constrained Optimization Problems (Version 1 using feasible approach) (Written in MATLAB)

Neural Network Algorithm (NNA) (Standard) Source Code for solving Constrained Optimization Problems (Version 2 using feasible approach) (Written in MATLAB)

Also, for solving constrained optimization problems, scholars may use the penalty function method applied on the standard unconstrained NNA code.

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Some Related Publications:

“A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm”

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