A Modified Water Cycle Algorithm: An Opposition Based Meta-Heuristic Optimization to Solve Real World Engineering Problems

Abstract

This paper proposes the Opposition based learning on a latest recent population based Water Cycle Algorithm on different benchmark constraint optimization techniques. Water cycle is a Hydrological based technique which works on better search location of the stream and river that flows to the sea which works on certain control parameters that will be defined initially and obtain the population matrix. With the help of the application of the opposition learning opposite search will be made to receive the better search location to find the better fitness value and avoid the premature convergence and get best convergence rate. This Proposed Opposition based Water Cycle Algorithm is implemented and tested on fifteen benchmark problems mentioning the fitness value as well as the constraints value. The convergence plot using a comparative study between Water Cycle Algorithm and Opposition based Water Cycle Algorithm, the proposed method had proved to obtain the best result and superior for the problems on to which it had implemented. The ANOVA test result is shown for the statistical analysis of the data obtained.