An improved sine–cosine algorithm based on orthogonal parallel information for global optimization

Many real-life optimization applications are characterized by the presence of some difficulties such as discontinuity, mixing continuity–discontinuity, prohibited zones, non-smooth and non-convex cost functions. In this sense, traditional optimization algorithms may be stuck in local optima when dealing with these natures. Recently, sine–cosine algorithm (SCA) has been introduced as a global optimization technique for solving optimization problems. However, as a new algorithm, the sucking in local optimal may be occurred due to two reasons. The first is that the diversity of solutions may not be maintained efficiently. The second is that no emphasizing strategy is employed to guide the search toward the promising region. In this paper, a novel SCA based on orthogonal parallel information (SCA-OPI) for solving numerical optimization problems is proposed. In SCA-OPI, a multiple-orthogonal parallel information is introduced to exhibit effectively two advantages: the orthogonal aspect of information enables the algorithm to maintain the diversity and enhances the exploration search, while the parallelized scheme enables the algorithm to achieve the promising solutions and emphases the exploitation search. Further, an experience-based opposition direction strategy is presented to preserve the exploration ability. The proposed SCA-OPI algorithm is evaluated and investigated on different benchmark problems and some engineering applications. The results affirmed that the SCA-OPI algorithm can achieve a highly competitive performance compared with different algorithms, especially in terms of optimality and reliability.