Superior Exploration–Exploitation Balance in Shuffled Complex Evolution

Numerous applications within water resources require a robust and efficient optimization algorithm. Given that these applications involve multimodal nonconvex and discontinuous search spaces, evolutionary algorithms (EAs)—which are known to possess global optimization properties—have been widely used for this purpose. For an evolutionary algorithm to be successful, two important facets of the search—exploration and exploitation of the search space—need to be addressed. In this study, we address the issue of achieving a superior exploration–exploitation tradeoff in an extensively used EA, the shuffled complex evolution (SCE-UA). A scheme to improve the exploration capability of the SCE-UA in finding the global optimum is suggested. The scheme proposed a systematically located initial population instead of a randomly generated one used in SCE-UA. On a suite of commonly used test functions, the robustness and efficiency of the modified SCE-UA algorithm was compared with the original SCE-UA. It is observed that when the points in the initial population are strategically placed, it leads to better exploration of the search space, and hence, yields a superior balance between exploration and exploitation. This in turn results in a significant improvement in the robustness of the SCE-UA algorithm.