Nature-inspired algorithms can be used as a learning method in the ANN model. In contrast to that, an ANN can also be used as an objective function to a nature-inspired algorithm to improve its capability to ge
A Metaheuristic Optimization Algorithm refers to a family of methods used in computer science that aim to find a global optimum by repeatedly evaluating the objective function without relying on gradient information. These algorithms, such as evolutionary algorithms, particle swarm optimization, and simula...
5. ANN designing by using COA algorithms The LM feed-forward neural network is one of the main algorithms for understanding neural networks, which has some drawbacks in petroleum engineering applications (Huang et al., 2003). To address some of these drawbacks, the Cuckoo Optimization Algorithm ...
Optimization algorithms are developed to update signal timing parameters for intersections that show potential for improvement. In this way, the OSaaS system can dynamically optimize traffic signal periodically every few weeks, compared to the 3~5 years in the current practice. With vehicle trajectory...
researchers are solving those issues by using Nature-Inspired Optimization Algorithms (NIOAs) as alternative methodologies for the multi-thresholding problem. This study presents an up-to-date review on all most important NIOAs employed in multi-thresholding based image segmentation domain. The key iss...
algorithms devised so far. Here, we propose a novel biologically inspired optimizer for artificial and spiking neural networks that incorporates key principles of synaptic plasticity observed in cortical dendrites: GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals). GRAPES ...
The central task in both of these steps is to assign attendees to a single seat at each table. This is done by using a network flow algorithm. Network flow algorithms are readily available in a number of programming languages and run very quickly. ...
(NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuro-Evolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: ...
implemented a hybrid algorithm between the GWO and APF algorithms, namely the GWO-APF algorithm. This algorithm has been applied to on-time path planning of ADS in a static environment. The APF algorithm finds obstacle-free locations. Then, the GWO algorithm minimizes the path and locations ...
Nature-inspired computation has become popular in engineering applications and nature-inspired algorithms tend to be simple and flexible and yet sufficiently efficient to deal with highly nonlinear optimization problems. In this chapter, we first review