<p>Recently, modular robotic system (MRS) emerged as a new type of Mobile Ad Hoc Networks (MANETs), referred to as Mobile Ad Hoc Robots, that can take different morphologies to satisfy various needs. Typically, MRS consists of a set of modules that are equipped with micro-sensors to capture surrounding information and adapt themselves to the monitored environment. Unfortunately, MRS faces many challenges such as saving sensor energy consumption, optimizing reconfiguration speed, and building solid and resilient structures effectively when transforming from one morphology to another. In this paper, we propose a novel self-reconfiguration technique called Morphology to Morphology Optimization (M2O) that ensures lower sensor communications and faster reconfiguration process in MRS. Mainly, M2O introduces two optimized versions of Ant Colony Optimization (ACO) and Bipartite Search Graph (BSG) algorithms that provide efficient transitions between robotic morphologies. More specifically, the first algorithm uses a probabilistic model to find the best path between two morphologies based on a weighted graph. The second one aims to separate the robotic modules into two disjoint and independent sets where each module in the first set will be connected to another one in the second set. Consequently, both algorithms offer lower energy consumption during sensors communications and reduce the number of movements during the transition. In order to validate the efficiency of M2O, we conducted simulation using a real MRS, e.g. Roombots, while considering several morphologies related to some real-life scenarios. The obtained results show the relevance of our technique through both algorithms in terms of reducing the number of module actions and the self-reconfiguration time during the MRS transition.</p>