Cellular Harmony Search for Optimization Problems

Abstract

Structured population in evolutionary algorithms (EAs) is an important research track where an individual only interacts with its neighboring individuals in the breeding step. The main rationale behind this is to provide a high level of diversity to overcome the genetic drift. Cellular automata concepts have been embedded to the process of EA in order to provide a decentralized method in order to preserve the population structure. Harmony search (HS) is a recent EA that considers the whole individuals in the breeding step. In this paper, the cellular automata concepts are embedded into the HS 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 to cellular EA theory. The experimental results using benchmark functions show that embedding the cellular automata concepts with HS processes directly affects the performance. Finally, a parameter sensitivity analysis of the cHS variation is analyzed and a comparative evaluation shows the success of cHS.

Island-based harmony search for optimization problems

“…The comparative methods also include cHS and HS algorithm (Al-Betar et al, 2013a). Note that cHS is a structured papulation method that incorporates the cellular automata context in HS algorithm to preserve the population diversity.…”

“…As other EAs, HS algorithm suffers from the problem of fast convergence due to diversity losses. Thus the chronic premature convergence might occur (Al-Betar et al, 2013a).…”

“…Cellular automat concepts have been embedded into the framework of HS to come up with a new version called Cellular HS (cHS) (Al-Betar et al, 2013a). The population has been arranged in the form of twodimensional toroidal grid.…”

“…The main merits of HS over other optimization methods are summarized as follows: a novel stochastic derivative is embedded within the HS (Geem, 2008); it needs less mathematical requirements which iteratively generate a new solution after manipulating all existing solutions (Mahdavi, Fesanghary, & Damangir, One of the shortcomings of simple HS algorithm is its inability to maintain diversity of population due to genetic drift (Al-Betar, Khader, Awadallah, Alawan, & Zaqaibeh, 2013a). The structured population mechanisms has recently captured the EA researcher to improve the performance by means of maintaining the diversity among the population members during the search, thus the conical premature convergence can be avoided.…”

Tournament-based harmony search algorithm for non-convex economic load dispatch problem

“…Similarly, the HS algorithm has been modified and hybridized with other efficient methods to cope with the combinatorial nature of highly constrained optimization problems [4,7,1,10,11,14]. Furthermore, the parameter-free HS [41] and population structured of HS are proposed to improve the theoretical aspects of the algorithm [2,5].…”