Community detection is key to understanding the structure of complex networks, and ultimately extracting useful information from them. Applications are diverse: from healthcare to regional geography, from human interactions and mobility to economics. In this paper we present a novel search strategy for the optimization of various objective functions for community detection purposes [ S. Sobolevsky, R. Campari, A. Belyi, and C. Ratti "General optimization technique for high-quality community detection in complex networks" Phys. Rev. E 90, 012811 2014 ]. Existing search strategies take one of the following steps to evolve starting partitions: merging two communities, splitting a community into two, or moving nodes between two distinct communities. The proposed algorithm compounds all three actions. After selecting an initial partition made of a single community, the following steps are iterated as long as the iteration results in an increased objective function score: (1) for each source community, the best possible redistribution of all source nodes into each destination community (either existing or new) is calculated; this also allows for the possibility that the source community entirely merges with the destination; (2) the best merger/split/recombination is performed. As the proposed technique combines all three possible types of steps, we are referring to it as Combo.