ATTRACTIVE AND REPULSIVE PARTICLE SWARM OPTIMIZATION, HYBRID ARTIFICIAL BEE ALGORITHM FOR SOLVING REACTIVE POWER OPTIMIZATION PROBLEM

Reactive Power Optimization is a complex combinatorial optimization problem involving non-linear function having multiple local minima, non-linear and discontinuous constrains. This paper presents Attractive and repulsive Particle Swarm Optimization (ARPSO) and Hybrid Artificial Bee Colony (HABC) applied for reactive optimization problem. It is one of the recent additions to the class of swarm intelligence based algorithms that mimics the foraging behaviour of honey bees. Artificial bee colony (ABC) consists of three groups of bees namely employed, onlooker and scout bees. In ABC, the food locations represent the potential candidate solution. In the present study an attempt is made to generate the population of food sources (Colony Size) adaptively and the variant is named as A-ABC. A-ABC is further enhanced to improve convergence speed and exploitation capability, by employing the concept of elitism, which guides the bees towards the best food source. This enhanced variant is called E-ABC. ARPSO and HABC are applied to Reactive Power Optimization problem and are evaluated on standard IEEE 30Bus System. The results show that ABC prevents premature convergence to high degree but still keeps a rapid convergence. It gives best solution when compared to Attractive and repulsive Particle Swarm Optimization (ARPSO) and Particle Swarm Optimization (PSO).