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.