An Improved Farmland Fertility Algorithm for Global Function Optimization

Abstract

This paper proposes an improved farmland fertility (IFF) algorithm to increase the convergence rate and precision of the farmland fertility algorithm. A search mode that combines subspace and full space is proposed. The two modes are automatically converted in accordance with the current learning level of the population. Such hybrid search balances the algorithm exploration and exploitation capabilities. The global memory capacity with a fixed size is processed self-adaptively to make its size adaptively change with the iterative process. A mechanism of soil fusion based on neighbor memory learning is also proposed to expand the search range of the current population and ensure population diversity. On the basis of the cosine similarity between the population and the current optimal individual, the area to which the individual belongs is periodically redivided, and the computing resources are reasonably allocated. The results of experiments on the CEC2013 test suite indicate that IFF has evident advantages over seven excellent algorithms in terms of convergence rate and precision.

An improved farmland fertility algorithm for many-objective optimization problems

“…When the subspace search mode is adopted, if the updated dimension is selected in accordance with the order of dimensions, it can better guarantee that each dimension can search more finely during the limited iterations compared with the updated dimension selected randomly. Therefore, in our proposed subspace search mode, when converting to the subspace search mode, the updated dimension of each individual is selected in accordance with the order of dimensions at each iteration, which is similar to the method proposed in 25 , detailed as follows. At the last iteration, dimension d is selected to update, and at this iteration, dimension d + 1 is selected to update.…”

“…At the last iteration, dimension d is selected to update, and at this iteration, dimension d + 1 is selected to update. When all the dimensions have been updated by adopting the subspace search mode, unlike the method proposed in 25 , even if they fail to meet the conversion condition between the subspace and full-space search modes, all individuals adopt the full-space search mode in the next iteration.…”

“…FF performs better than many well-known meta-heuristic methods (including GA, DE, PSO, and ABC) in terms of convergence accuracy, stability, and speed FF. An improved FF algorithm (IFF) has been proposed in 2020 25 . However, FF and IFF are designed for solving complex single-objective optimization problems.…”

Optimal Deep Learning-Based Vocal Fold Disorder Detection and Classification Model on High-Speed Video Endoscopy

“…e contrast between the surrounding areas and the intensity of the glottis is used to identify the glottal area borders using an image gradient [24]. e positive and negative gradients in the kymogram are computed with an eight-pixel step size beside x-and y-axes.…”

Novel Metaheuristic Based on Iterated Constructive Stochastic Heuristic: Dhouib-Matrix-3 (DM3)

“…e farmland fertility algorithm (FFA) inspired from farmland behavior in nature was presented in [13], and improved FFA was proposed in [14]. e African vultures optimization algorithm (AVOA) was illustrated in [15], which is based on the navigation and foraging behaviors of African vultures.…”