Computational Intelligence
L. Kanagasabai
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, ...
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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, the position update has been done through the distance of jumping done by Acridoidea. The distance (D) is horizontal (h) with angle (θ), velocity (V) parameter amplifying the rate which is based on the gravity of Ballistic projectile. Normally, the angle will be 45◦ horizontal and it depends on the take-off velocity. ASA algorithm has been tested in standard IEEE 57 bus test system and results show that the proposed ASA algorithm reduced the real power loss effectively.
Computational modelling
L. Kanagasabai
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 ...
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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 between stars and the progression of stars to the black hole is attuned while explore the solution space. Assumption made that heavy objects are stars in a gravitational system, which become black holes and the exploitation of GSA is enhanced. During the progression of the projected algorithm, the radius of the black hole diminishes and more objects are included, which assist to stop early convergence. To improve the exploration and exploitation, stars gravity information has been utilized. During the progression of the projected algorithm, the radius of the black hole diminishes, and more objects are included, which assist to stop early convergence. Some of the most excellent objects turn out to be the black hole, affect other objects by their sturdy gravity. The other objects are alienated into two groups: Heavy agents and light agents. Proposed ABHA has been tested in standard IEEE 14, 30, 57, 118, 300 bus test systems and simulation results show the projected algorithm reduced the real power loss comprehensively.
Computational Intelligence
L. Kanagasabai
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 ...
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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 most excellent location inside the group, when the food source is not rationalized then the group will start probing in different directions for the food sources. Two most important control parameters are Global Leader Limit (GLlimit) and Local Leader Limit (LLlimit) which give appropriate way to global and local leaders correspondingly. Levy flight has been intermingled in the algorithm to enhance the search ability. Proposed AMOA accelerates the exploitation ability that has been tested in standard IEEE 14, 30, 57,118,300 bus test systems. The simulation results show the projected algorithm reduced the real power loss comprehensively.