Hybridization of algorithms can enhance the overall search capabilities to get the optimal solution. The aim of this study was to invert Time Domain Electromagnetic (TDEM) data using the Flower Pollination Algorithm (FPA) as a new inversion scheme technique. FPA was originally inspired by the fertilization process of flowers, in which pollen transfer grains from male flowers to female flowers. The modeling of TDEM data was done by combining the FPA and elitism (eFPA) techniques. The applicability was tested on forward modeling data and observed data in MATLAB 2017a. In testing the algorithm, we used a model from homogeneous half space to a multi-layer model using different parameters (resistivity and thickness). In addition, in the inversion process, we used field data with various starting model approaches. Based on the results of the TDEM data modeling, FPA and eFPA can both be applied as algorithmic techniques for inversion modeling of TDEM data. The eFPA technique gave better results than FPA.
Inversion of schlumberger sounding curve is non-linear, and multi-minimum. All linear inversion strategies can produce local optimum, and depend on the initial model. Meanwhile, the non-linear bionic method for inversion problems does not require an initial model, simple, flexible, derivation-free mechanism and can avoid local optimum. One of the new algorithm of the non-linear bionic method for geophysical inversion problem is the Flower Pollination Algorithm (FPA). The FPA is used for the inversion of schlumberger sounding curve. This algorithm was stimulated by the pollination process for blooming plants. The applicability of the present algorithm was tested on synthetic models A-type and KH-type curve. Numerical tests in MATLAB R2013a for the synthetic data and the observed data show that FPA can find the global minimum. For further study, inverted results using the FPA are contrasted with the damped least-square (DLSQR) inversion program, Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). The outcomes of the comparison reveal that FPA performs better than the DLSQR inversion program, PSO, and GWO.
Flower pollination algorithm (FPA) is a nature-inspired algorithm that mimics flowering plant pollination behavior. Yang created the FPA in 2012, and it has since proven to be superior to other metaheuristic algorithms. Many FPA variants have recently been developed through modification and hybridization. This paper provides FPA variants consisting of Modified Flower Pollination Algorithm (MFPA), elitism Flower Pollination Algorithm (eFPA), Dimension by Dimension Improvement Flower Pollination Algorithm (DDIFPA) and Flower Pollination Algorithm with Bee Pollinator (BPFPA). Validation of the code that has been created is done through simple model testing. All algorithms provide inversion results that are consistent with true value. The results of two synthetic models indicate that eFPA is the only algorithm that can reach the global optimum. Besides having the best level of accuracy, eFPA also has the best stability.
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