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.
Highlights
The optimization techniques have the utility of navigating the search space using effective operators driven by control parameters
The results reveal that cellular harmony search (cHS) and Harmony search (HS) have identical sensitivity to the different values of harmony memory consideration rate (HMCR) for all functions, and in 0.98 probabilities to use HMCR they get the best results for the majority of optimization functions
A new version of HS algorithm called cellular harmony search algorithm is proposed. cHS is an HS algorithm embedded with cellular automata concepts
Summary
The optimization techniques have the utility of navigating the search space using effective operators driven by control parameters. Due to its advantages over other optimization methods, it stipulates fewer mathematical requirements in the initial search [3] It has a novel stochastic derivative which reduces the number of iterations required to converge towards local minima [4], in addition to being simple, adaptable, general, and scalable [5]. Cellular genetic algorithm (cGA), in particular, is a decentralized method where the population is represented as a toroidal grid of two-dimensions, as shown in Figure 3 [23, 24]. Note that all the neighborhoods have the same size and identical shape This concept embedded in cGA provides useful advantages for the optimization domain [23] and parallel implementations [25, 26] because it assists in providing a high-level of diversity and yields a small diffusion of solutions through the search.
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