Computational Intelligence
1. Real power loss reduction by Acridoidea stirred artificial bee colony algorithm

L. Kanagasabai

Volume 9, Issue 3 , Summer 2020, , Pages 209-215

http://dx.doi.org/10.22105/riej.2020.229820.1133

Abstract
  Acridoidea Stirred Artificial Bee Colony (ASA) Algorithm is applied to solve the power loss reduction problem. In the projected algorithm natural Acridoidea jumping phenomenon has been imitated and the modeled design has been intermingled with artificial bee colony algorithm. In the proposed algorithm, ...  Read More

Computational modelling
2. Amplified black hole algorithm for real power loss reduction

L. Kanagasabai

Volume 9, Issue 2 , Spring 2020, , Pages 130-142

http://dx.doi.org/10.22105/riej.2020.214468.1114

Abstract
  This work presents Amplified Black Hole Algorithm (ABHA) for solving optimal reactive power problem. In the projected approach ABHA, Gravitational Search Algorithm (GSA) is merged with Black Hole Algorithm (BHA). Power loss reduction is the key objective in the proposed work. The gravitational force ...  Read More

Computational Intelligence
3. Factual power loss reduction by augmented monkey optimization algorithm

L. Kanagasabai

Volume 9, Issue 1 , Winter 2020, , Pages 1-12

http://dx.doi.org/10.22105/riej.2020.214459.1112

Abstract
  This paper presents Augmented Monkey Optimization Algorithm (AMOA) applied to solve optimal reactive power problem. Communal behaviour of monkeys has been utilized to model the algorithm. Normally, group monkeys assess the distance from the source to food for foraging behaviour. Local leader renews its ...  Read More