Dynamic search zones (DSZ) for harmony search algorithm

Harmony Search algorithm (HSA) is a general-purpose heuristic algorithm that has been widely applied to resolve many combinatorial optimization problems. Since it was developed, many researches focused on improving the algorithm search capabilities through proposing a number of HSA variants. This work aims to focus on improving the investigation process of HSA that is assigned to random consideration part of the algorithm (i.e. random search operator). A new technique is proposed to enhance the exploration behavior of the algorithm, where the search range (zone) of optimization problem is divided randomly into n number of sub-zones. The algorithm will then run through all defined sub-zones to generate n initial harmonies for each sub-zone. To determine the best initial harmonies along with their belonging sub-zones, the average of the fitness values was calculated in each partition. Thereafter, the lowest average calculated will be considered to determine the best sub-zone that will be selected for further search process. To confirm that the best sub-zone was chosen, a new procedure was injected to perform a periodic random search throughout all the sub-zones in order to detect the desired sub-zone. This procedure is named as detection procedure in this paper to reflect its main role. Twelve benchmark functions are used to investigate the positive impact of this technique. The results demonstrate the advantages of the anticipated technique over previous improved algorithms, and suggest a new method for optimization search. However, the results also show that the detection procedure decreases the evaluation iterations, which leads to minimize the convergence rate towards the optimal solution. Thus, this paper opens a new dimension for further exploration.