By simultaneously considering variable layers, channels, and connections between different convolution layers, the deep neural architecture can be scalable. Second, a variable-architecture encoding strategy is proposed to encode neural architecture as a fixed-length binary string. Without the complex actor-critic parts, the reinforced IDEA based on simplified Reinforcement Learning can enhance the search efficiency of the original evolutionary algorithm with lower computational cost. First, unlike the typical Reinforcement Learning-based and evolutionary algorithm-based NAS methods, a simplified Reinforcement Learning algorithm is developed and used as reinforced operator controller to adaptively select the efficient operators of IDEA. To address this problem, an adaptive scalable neural architecture search method (AS-NAS) is proposed based on reinforced I-Ching divination evolutionary algorithm (IDEA) and variable-architecture encoding strategy. Neural architecture search (NAS) is a challenging problem in the design of deep learning due to its non-convexity. Index Terms-Image data clustering, knee point-based selection, multiobjective optimization, remote sensing, sine cosine algorithm (SCA), spatial information. The benefits of the proposed method were demonstrated by clustering experiments with ten UCI datasets and four real remote sensing image datasets. Furthermore, the destination solution in the SCA is automatically selected and updated from the current Pareto front through employing the knee-point-based selection approach. In addition, for the first time, the sine cosine algorithm (SCA), which can effectively adjust the local and global search capabilities, is introduced into the framework of multiobjective clustering for continuous optimization. In the proposed method, the clustering task is converted into a multiobjective optimization problem, and the Xie-Beni (XB) index and Jeffries-Matusita (Jm) distance combined with the spatial information term (SI_Jm measure) are utilized as the objective functions. In this article, in order to address these problems, a multiobjective sine cosine algorithm for remote sensing image data spatial-spectral clustering (MOSCA_SSC) is proposed. Although evolutionary multiobjective optimization methods have been presented for the clustering task, the tradeoff between the global and local search abilities is not well adjusted in the evolutionary process. ![]() ![]() Meanwhile, remote sensing images contain complex and diverse spatial-spectral information, which makes them difficult to model with only a single objective function. Therefore, it can be easily affected by the initial values and trapped in locally optimal solutions. Remote sensing image clustering, in essence, belongs to a complex optimization problem, due to the high dimensional-ity and complexity of remote sensing imagery. Remote sensing image data clustering is a tough task, which involves classifying the image without any prior information. Compared with the genetic algorithm, particle swarm optimization, and differential evolution algorithm, our proposed IDEA is much faster in reaching the global optimum. After giving some basic concepts of necessary theorems, definition of admissible functions and I-Ching map, a precise proof of the states converge to the global optimum is presented. Meanwhile, the proposed algorithm is proved to be a homogeneous Markov chain with the positive transition matrix. ![]() ![]() In order to analyze the convergence property of I-Ching divination algorithm, Markov model was adopted to analyze the characters of the operators. Additionally, two new spaces are defined in this paper, which are denoted as hexagram space and state space. These new operators are very flexible in the evolution procedure. There are three operators evolved from I-Ching transformations in this new optimization algorithm, intrication operator, turnover operator, and mutual operator. Inherited from ancient Chinese culture, I-Ching divination has always been used as a divination system in traditional and modern China. An innovative simulated evolutionary algorithm (EA), called I-Ching divination EA (IDEA), and its convergence analysis are proposed and investigated in this paper.
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