A novel parallel search technique for optimization

In this paper a novel parallel search technique for solving optimization problems is proposed. The novelty of the proposed algorithm is that it uses a very less number of tunable parameters as compared to most of the similarly established stochastic algorithms. In addition, a simple operator called rotate left and complement (RLC) is used in this method that helps to reach distinct points in the search space instead of reaching repeated points as done by conventional meta-heuristics. It also helps to enhance the chance of reaching towards optimal results and save time by not reaching repeated points. Another operator called flip operator is used to introduce variations within the search space as well. Performance of the proposed algorithm is tested on eight benchmark functions available in literature and the results are compared with some of the well-known optimization techniques which show the completeness of the proposed algorithm.