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.
