Cellular Harmony Search for Optimization Problems

Structured population in evolutionary algorithms (EAs) is an importantresearch track where an individual only interacts with its neighboring individualsin the breeding step. The main rationale behind this is to providea high level of diversity to overcome the genetic drift. Cellular automataconcepts have been embedded to the process of EA in order to provide a decentralizedmethod in order to preserve the population structure. Harmonysearch (HS) is a recent EA that considers the whole individuals in the breedingstep. In this paper, the cellular automata concepts are embedded into theHS algorithm to come up with a new version called cellular harmony search(cHS). In cHS, the population is arranged as a two-dimensional toroidal grid,where each individual in the grid is a cell and only interacts with its neighbors. The memory consideration and population update are modified according tocellular EA theory. The experimental results using benchmark functionsshow that embedding the cellular automata concepts with HS processes directlyaffects the performance. Finally, a parameter sensitivity analysis of thecHS variation is analyzed and a comparative evaluation shows the success ofcHS