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The slime mould algorithm (SMA) is a recent physics-based optimization approach. The main inspiration of the SMA is motivated by the natural oscillating state of the slime mould organisms. In order to boost the performance, several problems must be resolved properly on the original SMA itself. One of these problems is the dilemma of the improper balancing between the exploration and exploitation phases which might deviate the algorithm to be trapped in the local optima. This work introduces a new version of the SMA called mSMA-based on the hybridization of the original SMA with a modified version of the opposition-based learning (mOBL) and the Orthogonal learning (OL) strategies. To assess the performance of the proposed mSMA, it has been evaluated over ten CEC’2020 test suites and three engineering design problems. As the output performance of the thermoelectric generator (TEG) is mainly …