## 1 Introduction  
Environmental pressure and increasing energy costs escalation create a series of fundamental dilemmas in electric power production. Like many developing countries, the growing consumption of energy fuels bolsters the Chinese economy, but increasing the use also exposes potential disruptions in supply [1]. Thus, renewable energy plays an increasingly important part in the future Chinese power system, supplanting all or part of conventional energy sources. Solar energy deserves attention because of its clean, non-polluting, and sustainable use, and other advantages [2]. It is an ideal energy that fundamentally solves the energy crisis and environmental problems [3]. Solar radiation varies with geographic latitude, season and time of a day due to the various solar positions [4]. To maximize the collection of solar radiation, a PV panel should be installed at the appropriate tilt angle and orientation under various circumstances [5]. Recently, many investigators have searched for the optimum tilt angle (\(\beta_{\text{opt}}\)) and optimum azimuth angle (\(\gamma_{\text{opt}}\)) of solar collectors. [PERSON] [6] used the artificial neural network (ANN) estimator taking the \(H_{\text{g}}\), \(\phi\) and \(E_{\text{L}}\) of the site as inputs, to estimate the optimum tilt angle almost instantaneously while testing. [PERSON] [7] represented the optimum slope and the azimuth angles with the anisotropic model for South Africa, calculating the daily radiation at various slopes and orientations, thus obtaining \(\beta_{\text{opt}}\) and \(\gamma_{\text{opt}}\). The past few decades have seen an increased interest in general-purpose \"black-box\" optimization algorithms that have drawn inspiration from optimization processes that occur in nature in large part [8; 9; 10; 11; 12; 13; 14]. In Ref. [15], an approach combining the orthogonal array experimental technique and an ant direction hybrid differential evolution algorithm (ADHDEOA) was presented for determining the tilt angle for PV modules. In Ref. [16], the tilt angle was changed from \(-20^{\circ}\) to \(90^{\circ}\) in a step size of \(0.1^{\circ}\), and the corresponding value of maximum global solar radiation for a specific period is defined as the optimal tilt angle. In Ref. [17], a particle-swarm optimization method with nonlinear time-varying evolution (PSO-NTVE) was proposed, by which the tilt angle of PV modules were determined for Taiwan. The calculation can be formulated as an optimization problem for maximizing the output electrical energy of the modules. From previous applications, the defect of particle swarm optimization (PSO) prematurity makes it easy to fall into a local optimum; thus, it is necessary to select other algorithms to attempt this research. In 2001, [PERSON] [18] developed a new harmony search (HS) meta-heuristic algorithm that was conceptualized using the musical process of searching for a perfect state of harmony. The word \"heuristic\" refers to \"solution by trial and error method\", and the word \"meta\" refers to \"high level\". The harmony in music is analogous to the optimization solution vector, and the musician's improvisations are analogous to local and global search schemes in optimization techniques [19]. The characteristics of the HS algorithm include the following three aspects: (1) There is no need for initial values of the decision variables; (2) HS uses a stochastic random search, which requires the harmony memory (HM) without any derivative information; and, (3) When compared with meta-heuristic optimization algorithms, it demands fewer mathematical requirements and can be applied to a wider range. [PERSON] [20] and [PERSON] and [PERSON] [21] applied the algorithm to analyze pressure vessel design to minimize the total cost of the material. The HS algorithm was used to solve a weld beam design optimization problem in [19], which was compared with previous solutions. In [22], the HS algorithm was used to determine the near-global optimal initial weights when training the model.