Geon Hee Lee

Metaheuristic algorithms are widely utilized as effective tools for solving complex optimization problems. Among them, the Harmony Search (HS) algorithm has garnered significant attention for its simple structure and excellent performance. The efficacy of the HS algorithm is heavily dependent on the configuration of its internal parameters, with the Harmony Memory Considering Rate (HMCR) and Pitch Adjusting Rate (PAR) playing pivotal roles. These parameters determine the probabilities of using the Random Generation (RG), Harmony Memory Consideration (HMC), and Pitch Adjustment (PA) operators, thereby controlling the balance between exploration and exploitation. However, a systematic empirical analysis of the interaction between these parameters and the characteristics of the problem at hand remains insufficient. Thus, this study conducts a comprehensive empirical analysis of the performance sensitivity of the HS algorithm to variations in HMCR and PAR values. The analysis is performed on a suite of 23 benchmark functions, encompassing diverse characteristics such as unimodality/multimodality and separability/non-separability, along with 5 real-world optimization problems. Through extensive experimentation, the performance for each parameter combination was evaluated on a rank-based system and visualized using heatmaps. The results experimentally demonstrate that the algorithm’s performance is most sensitive to the HMCR value across all function types, establishing that setting a high HMCR value (≥0.9) is a prerequisite for securing stable performance. Conversely, the optimal PAR value showed a direct correlation with the topographical features of the problem landscape. For unimodal problems, a low PAR value between 0.1 and 0.3 was more effective, whereas for complex multimodal problems with numerous local optima, a relatively higher PAR value between 0.3 and 0.5 proved more efficient in searching for the global optimum. This research provides a guideline into the parameter settings of the HS algorithm and contributes to enhancing its practical applicability by proposing a systematic parameter tuning strategy based on problem characteristics.

The nonogram is a logic puzzle where each cell should be colored or left blank according to row and column clues to reveal a hidden picture. This puzzle is known as an NP-complete combinatorial problem characterized by an exponential increase in the number of candidate solutions with increasing puzzle size. So far, some methods have been investigated to address these challenges, including conventional line-solving techniques, integer programming, and neural networks. This study introduces a novel Harmony Search (HS)-based approach for solving nonogram puzzles, incorporating problem-specific operators designed to effectively reduce the solution search space and accelerate convergence. Experimental results obtained from benchmark puzzles demonstrate that the proposed HS model utilizing a clue-constrained random-generation operator significantly reduces the average number of iterations and enhances the solution-finding success rate. Additionally, the HS model integrating an initially confirmed cell-scanning operator exhibited promising performance on specific benchmark problems. The authors think that the nonogram puzzle can be a good benchmark problem for quantum computing-based optimization in the future, and the proposed HS algorithm can also be combined with quantum computing mechanisms.

Multiple power peaks are introduced under nonuniform irradiation conditions in photovoltaic (PV) systems. However, conventional maximum power point tracking (MPPT) algorithms, such as the perturb-and-observe (P&O) algorithm, are only effective under homogeneous irradiation conditions. Therefore, there is a need for an efficient method for global maximum power point (GMPP) tracking under partial shading conditions (PSCs). This paper proposes a novel MPPT algorithm based on harmony search (HS). The proposed algorithm not only overcomes the power loss induced by a wide search but also avoids falling into local maximum power points under PSCs. A parameter-setting-free (PSF) HS algorithm is introduced, which is further improved for the MPPT problem. Furthermore, the algorithm integrates a reduced-range (RR) operator to reduce the convergence time. To validate the performance of the proposed algorithm, experiments are performed using a 250-W PV system with a PV emulator under different PSCs. The results show that the proposed algorithm outperforms conventional HS and particle swarm optimization algorithms in terms of the number of iterations and convergence time, while maintaining a similar level of accuracy.