A novel population based optimization algorithm known as Sine Cosine Algorithm (SCA), in contrast to meta-heuristics; main feature is randomization having a relevant role in both exploration and exploitation in optimization problem. A novel randomization technique termed adaptive technique is integrated with SCA and exercised on unconstraint test benchmark function andlocalization of partial discharge in transformer like geometry. SCA algorithm has quality feature that it uses simple trigonometric terms like sine and cosine term for every unconstrained and complex constrained optimization problem. Integration of new randomization adaptive technique provides potential that ASCA algorithm to attain global optimal solution and faster convergence with less parameter dependency. Adaptive SCA (ASCA) solutions are evaluated and results shows its competitively better performance over standard SCA optimization algorithms.

Keywords : Meta-heuristic; Sine Cosine Algorithm; Adaptive technique; Global optimal; Benchmark function; Transformer.