Crow Search Algorithm Based on Neighborhood Search of Non-Inferior Solution Set

An improved opposition-based Crow Search Algorithm for Data Clustering

Data clustering is an ideal way of working with a huge amount of data and looking for a structure in the dataset. In other words, clustering is the classification of the same data; the similarity among the data in a cluster is maximum and the similarity among the data in the different clusters is minimal. The innovation of this paper is a clustering method based on the Crow Search Algorithm (CS...

Optimal Placement and Sizing of TCSC & SVC for Improvement Power System Operation using Crow Search Algorithm

Abstract: The need for more efficient power systems has prompted the use of a new technologies includes Flexible AC transmission system (FACTS) devices. FACTS devices provides new opportunity for controlling the line power flow and minimizing losses while maintaining the bus voltages within a permissible limit. In this thesis a new method is proposed for optimal placement and sizing of Thyristo...

Optimal Operation of CHP Combined Heat Generation Systems Using the Crow Search Optimization Algorithm

Energy efficiency of power plants is less than 60% However, the efficiency of the CHP units can be up to 90 %.CHP units in addition to high efficiency, They reduce environmental pollutants by 13 to 18 percent. The purpose of this thesis is to use the simultaneous power and power generation plants to reach the optimal economic destination for Genco And to maximize economic profit And to minimize...

A Neighborhood Expansion Tabu Search Algorithm Based On Genetic Factors

We provide an improved algorithm called “a neighborhood expansion tabu search algorithm based on genetic factors” (NETS) to solve traveling salesman problem. The algorithm keeps the traditional tabu algorithm’s neighborhood, ensure the algorithm’s strong climbing ability and go to the local optimization. At the same time, introduce the genetic algorithm’s genetic factor (crossover factor and va...

A Hybrid Feature Selection Algorithm Based on Large Neighborhood Search