LAHS: A novel harmony search algorithm based on learning automata

• A novel learning-based harmony search (HS) algorithm is proposed in this paper.
• Learning automata is employed for fine-tuning of HS parameters.
• No parameter-setting is required in the proposed learning automata based harmony search algorithm.
• The numerical results indicate that the proposed algorithm outperforms the other HS variants.

This study presents a learning automata-based harmony search (LAHS) for unconstrained optimization of continuous problems. The harmony search (HS) algorithm performance strongly depends on the fine tuning of its parameters, including the harmony consideration rate (HMCR), pitch adjustment rate (PAR) and bandwidth (bw). Inspired by the spur-in-time responses in the musical improvisation process, learning capabilities are employed in the HS to select these parameters based on spontaneous reactions. An extensive numerical investigation is conducted on several well-known test functions, and the results are compared with the HS algorithm and its prominent variants, including the improved harmony search (IHS), global-best harmony search (GHS) and self-adaptive global-best harmony search (SGHS). The numerical results indicate that the LAHS is more efficient in finding optimum solutions and outperforms the existing HS algorithm variants.