CRBA: A Competitive Rate-Based Algorithm Based on Competitive Spiking Neural Networks

MOEICA: Enhanced multi-objective optimization based on imperialist competitive algorithm

In this paper, a multi-objective enhanced imperialist competitive algorithm (MOEICA) is presented. The main structures of the original ICA are employed while some novel approaches are also developed. Other than the non-dominated sorting and crowding distance methods which are used as the main tools for comparing and ranking solutions, an auxiliary comparison approach called fuzzy possession is ...

An Improved Imperialist Competitive Algorithm based on a new assimilation strategy

Meta-heuristic algorithms inspired by the natural processes are part of the optimization algorithms that they have been considered in recent years, such as genetic algorithm, particle swarm optimization, ant colony optimization, Firefly algorithm. Recently, a new kind of evolutionary algorithm has been proposed that it is inspired by the human sociopolitical evolution process. This new algorith...

Spicules-based competitive neural network

We present a new model of unsupervised competitive neural network, based on spicules. This model is capable of detecting topological information of an input space, determining its orientation and, in most case, its skeleton.

Training Neural Networks Based on Imperialist Competitive Algorithm for Predicting Earthquake Intensity

In this study we determined neural network weights and biases by Imperialist Competitive Algorithm (ICA) in order to train network for predicting earthquake intensity in Richter. For this reason, we used dependent parameters like earthquake occurrence time, epicenter’s latitude and longitude in degree, focal depth in kilometer, and the seismological center distance from epicenter and earthquake...

Competitive Neural Networks