This study considers the partial monitoring problem with $k$-actions and $d$-outcomes and provides the first best-of-both-worlds algorithms, whose regrets are favorably bounded both in the stochastic and adversarial regimes. In particular, we show that for non-degenerate locally observable games, the regret is $O(m^2 k^4 \log(T) \log(k_Π T) / Δ_{\min})$ in the stochastic regime and $O(m k^{2/3} \sqrt{T \log(T) \log k_Π})$ in the adversarial regime, where $T$ is the number of rounds, $m$ is the maximum number of distinct observations per action, $Δ_{\min}$ is the minimum suboptimality gap, and $k_Π$ is the number of Pareto optimal actions. Moreover, we show that for globally observable games, the regret is $O(c_{\mathcal{G}}^2 \log(T) \log(k_Π T) / Δ_{\min}^2)$ in the stochastic regime and $O((c_{\mathcal{G}}^2 \log(T) \log(k_Π T))^{1/3} T^{2/3})$ in the adversarial regime, where $c_{\mathcal{G}}$ is a game-dependent constant. We also provide regret bounds for a stochastic regime with adversarial corruptions. Our algorithms are based on the follow-the-regularized-leader framework and are inspired by the approach of exploration by optimization and the adaptive learning rate in the field of online learning with feedback graphs.