Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems

• The proposed technique uses the power of global and local searching from HMCR and PAR parameters.
• The proposed technique has been tested on 15 standard benchmark functions and compared with existing techniques.
• The performance of proposed technique is least affected even when the benchmark function is corrupted with noise as compared to other existing techniques.
• The proposed technique provides better results than the other techniques in noisy and scalable environment.
• The effect of harmony memory size has also investigated on proposed technique.

This paper presents a parameter adaptive harmony search algorithm (PAHS) for solving optimization problems. The two important parameters of harmony search algorithm namely Harmony Memory Consideration Rate (HMCR) and Pitch Adjusting Rate (PAR), which were either kept constant or the PAR value was dynamically changed while still keeping HMCR fixed, as observed from literature, are both being allowed to change dynamically in this proposed PAHS. This change in the parameters has been done to get the global optimal solution. Four different cases of linear and exponential changes have been explored. The change has been allowed during the process of improvization. The proposed algorithm is evaluated on 15 standard benchmark functions of various characteristics. Its performance is investigated and compared with three existing harmony search algorithms. Experimental results reveal that proposed algorithm outperforms the existing approaches when applied to 15 benchmark functions. The effects of scalability, noise, and harmony memory size have also been investigated on four approaches of HS. The proposed algorithm is also employed for data clustering. Five real life datasets selected from UCI machine learning repository are used. The results show that, for data clustering, the proposed algorithm achieved results better than other algorithms.