This paper presents an optimization algorithm called Plant Growth Optimization (PGO) and it is applied for different test functions to evaluate its performance. The PGO is based on the plant growth characteristics in which an artificial plant growth model is built including leaf growth, branching, phototropism, and spatial occupancy. The plant growth process is that a plant grows a trunk from its root; some branches will grow from the nodes on the trunk; and then some new branches will grow from the nodes on the branches. Such process is repeated, until a plant is formed. This process is simulated in this algorithm by producing new points (branch points) from initial points (roots). After producing the new points (branch points), the algorithm searches the optimum solution around these points through the operation called leaf growth (for local search). This is used to ensure the accuracy of the solution. It is one of the evolutionary algorithms like the Genetic algorithm. A MATLAB code for the plant growth optimization algorithm is developed and it is tested for three classical test functions. The results are tabulated and plotted.

Author Name: Nalluri Mohan Rao, K. V. Nageswara Rao

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Keywords: Global Optimization, Plant Growth Optimization Algorithm, Nelder-Mead Search Method, Standard Test Functions: Himmelblau’s Function, Beale’s Function and the Powell’s Quartic Function.

ISSN: 0973-2632

EISSN: 2230-7184

EOI/DOI:

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