The simulation results show that TPO outperforms PSO and GA in all problem characteristics (flat surface and steep-drop with a combination of many local minima and plateau). To demonstrate its effectiveness, two different classes of problem are evaluated: (1) a continuous benchmark test function compared to particle swarm optimization (PSO) and genetic algorithm (GA) and (2) an NP-hard problem with the traveling salesman problem (TSP) compared to GA and nearest-neighbor (NN) algorithm.
The shoots-branches extension enhances the search diversity and the root system amplifying the search via evaluated fitness. This idea is transformed into an optimization algorithm: shoots with defined branches find the potential solution with the help of roots variable. The collaboration from both systems ensures plant sustainability. Shoots extend to find better sunlight for the photosynthesis process that converts light and water supplied from the roots into energy for plant growth at the same time, roots elongate in the opposite way in search for water and nutrients for shoot survival. A plant growth consists of two main counterparts: plant shoots and roots. In this paper, a new metaheuristic algorithm inspired from a plant growth system is proposed, which is defined as tree physiology optimization (TPO). To date, there are many metaheuristic algorithms introduced with good promising results and also becoming a powerful method for solving numerous optimization problems. This unique characteristic reflects a pattern of optimization that inspires the computational intelligence toward different scopes of optimization: a nondeterministic optimization approach or a nature-inspired metaheuristic algorithm. Nature has the ability of sustainability and improvisation for better survival.
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