Riemannian stochastic fixed point optimization algorithm

With the economic globalization and fast-changing of information technology, the logistics industry and production efficiency have greatly improved, and the logistics distribution have become an important part of the modern logistics industry. The selection of the distribution path in the logistics is the key issue because the choice of reasonable distribution route have great significance on increasing delivery speed, improving service quality, reducing distribution cost and increasing economic benefit. Aiming at this characteristic in the process of logistics distribution, this paper presents an adaptive chaotic disturbance flies optimization algorithm (ACD-FOA), which can optimize fruit flies algorithm in a state of convergence for global optimization. It also can overcome the shortcoming of flies optimization algorithm which is easy to fall into local faults in the solving process to some extent. Based on this advantage, the convergence speed, reliability and optimization on the accuracy of optimization algorithm are much better than the pure flies. Finally the instance simulation test have been carried out, which proved that the adaptive chaotic flies optimization algorithm has better ability of global optimization. In order to prove that the proposed adaptive chaotic disturbance fly optimization algorithm has higher optimization accuracy in logistics distribution, the results were compared with three other models, which indicates that the ACD-FOA established in this paper is more applicable to the current Logistics distribution path optimization. It also can find the optimal logistics distribution path quickly and reduce the logistics distribution cost effectively at the same time.

With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper, which provides a new idea for solving high-dimensional optimization problems. First, in order to balance the algorithm’s search abilities in terms of breadth and depth, the value of parameter G is given by an adaptive dynamic adjustment method. Second, in this paper, a foraging-behavior-improvement strategy is utilized to improve the algorithm’s solution accuracy and depth-optimization ability. Third, the artificial fish swarm algorithm (AFSA) is introduced to construct a dual-population collaborative optimization strategy based on chicken swarms and artificial fish swarms, so as to improve the algorithm’s ability to jump out of local extrema. The simulation experiments on the 17 benchmark functions preliminarily show that the ADPCCSO algorithm is superior to some swarm-intelligence algorithms such as the artificial fish swarm algorithm (AFSA), the artificial bee colony (ABC) algorithm, and the particle swarm optimization (PSO) algorithm in terms of solution accuracy and convergence performance. In addition, the APDCCSO algorithm is also utilized in the parameter estimation problem of the Richards model to further verify its performance.

Soil element monitoring wireless sensor networks (SEMWSNs) are widely used in soil element monitoring agricultural activities. SEMWSNs monitor changes in soil elemental content during agriculture products growing through nodes. Based on the feedback from the nodes, farmers adjust irrigation and fertilization strategies on time, thus promoting the economic growth of crops. The critical issue in SEMWSNs coverage studies is to achieve maximum coverage of the entire monitoring field by adopting a smaller number of sensor nodes. In this study, a unique adaptive chaotic Gaussian variant snake optimization algorithm (ACGSOA) is proposed for solving the above problem, which also has the advantages of solid robustness, low algorithmic complexity, and fast convergence. A new chaotic operator is proposed in this paper to optimize the position parameters of individuals, enhancing the convergence speed of the algorithm. Moreover, an adaptive Gaussian variant operator is also designed in this paper to effectively avoid SEMWSNs from falling into local optima during the deployment process. Simulation experiments are designed to compare ACGSOA with other widely used metaheuristics, namely snake optimizer (SO), whale optimization algorithm (WOA), artificial bee colony algorithm (ABC), and fruit fly optimization algorithm (FOA). The simulation results show that the performance of ACGSOA has been dramatically improved. On the one hand, ACGSOA outperforms other methods in terms of convergence speed, and on the other hand, the coverage rate is improved by 7.20%, 7.32%, 7.96%, and 11.03% compared with SO, WOA, ABC, and FOA, respectively.

Due to improper selection of gradient update direction or learning rate, SGD optimization algorithms for deep learning suffer from oscillation and slow convergence. Although Adam algorithm can adaptively adjust the update direction and learning rate at the same time, it still has the overshoot phenomenon, and hence suffers from wasting computing resources and slow convergence. In this work, the PID controller from the feedback control area is borrowed to re‐express the adaptive optimization algorithm (the Adam optimization algorithm is derived into the integral I component form) of deep learning. In order to alleviate the overshoot phenomenon and hence speed up the convergence of Adam, a complete adaptive PID optimizer (adaptive‐PID) is proposed by incorporating the proportional P and derivative D component. Extensive experiments on standard data sets verify that the proposed adaptive‐PID algorithm significantly outperforms Adam algorithm in terms of convergence rate and accuracy.

The dynamic behavior of the Internet's transmission resources makes it difficult to provide perceptually good quality streaming video. Scalable video encoding techniques have been proposed to deal with this problem. However, an encoded video generally exhibits significant data rate variability to provide consistent visual quality. We are, therefore, faced with the problem of accommodating the mismatch between the available bandwidth variability and the encoded video variability. We investigate quality adaptation algorithms for scalable encoded variable bit-rate video over the Internet. Our goal is to develop a quality adaptation scheme that maximizes perceptual video quality by minimizing quality variation, while at the same time increasing the usage of available bandwidth. We propose an optimal adaptation algorithm and a real-time adaptation algorithm based on whether the network conditions are known a priori. Experimental results show that the real-time adaptation as well as the optimal adaptation algorithm provide consistent video quality when used over both TCP-friendly rate control (TFRC) and transmission control protocol (TCP).

Multi-objective optimization problems can be divided into continuous multi-objective optimization problems and discrete multi-objective optimization problems, and discrete multi-objective optimization is not universal. In practical applications, there are many discrete multi-objective optimization problems. The solution of different problems needs to design different evolutionary multi-objective algorithms according to their specific conditions. The threshold in the traditional multi-objective optimization of wireless network is a preset constant. It has relatively poor performance in the synthesis of images with different noise levels. An adaptive wireless network multi-objective optimization algorithm based on image synthesis is proposed. Based on the maximum inter class variance and maximum peak signal to noise ratio (SNR), an adaptive wireless network multi-objective optimization algorithm is established. The accuracy and noise immunity of image synthesis are also considered. In order to avoid the effect of threshold increase on algorithm efficiency, multi-objective optimization algorithm is introduced into the algorithm. Experiments show that the method proposed in this paper is accurate and robust and has good universality for the synthesis of different noise images.

Emerging as one of the most contemporary machine learning techniques, deep learning has shown success in areas such as image classification, speech recognition, and even playing games through the use of hierarchical architecture which includes many layers of non-linear information. In this paper, a powerful deep learning pipeline, intelligent deep learning (iDeepLe) is proposed for both regression and classification tasks. iDeepLe is written in Python with the help of various API libraries such as Keras, TensorFlow, and Scikit-Learn. The core idea of the pipeline is inspired by the sequential modeling with considering numerous layers of neurons to build the deep architecture. Each layer in the sequential deep model can perform independently as a module with minimum finitudes and does not limit the performance of the other layers. iDeepLe has the ability of employing grid search, random search, and Bayesian optimization to tune the most significant predictor input variables and hyper-parameters in the deep model via adaptive learning rate optimization algorithms for both accuracy and complexity, while simultaneously solving the unknown parameters of the regression or the classification model. The parallel pipeline of iDeepLe has the capacity to handle big data problems using Apache Spark, Apache Arrow, High Performance Computing (HPC) and GPU-enabled machines as well. In this paper, to show the importance of the optimization in deep learning, an exhaustive study of the impact of hyper-parameters in a simple and a deep model using optimization algorithms with adaptive learning rate was carried out.

The Industrial Internet of Things (IIoT) is characterized by the instantaneous communication of diverse critical data across complex, resource-limited networks. To maximize the efficiency and accuracy of task scheduling in such networks, this study introduces a novel task scheduling framework based on the Adaptive Lotus Effect Optimization Algorithm (ALEOA). ALEOA perfectly combines the self-cleaning mechanism and learning function of the Lotus effect with the global search and learning capabilities of the Particle Swarm Optimization (PSO) algorithm to establish equilibrium between local and global search. ALEOA can adaptively adjust routing and task scheduling to support critical data communication and a dynamic network. Results from experiments on typical benchmarking equations and simulations of the IIoT communication environment show that the proposed ALEOA-based model yields higher task acceptance rates, lower communication delays, and improved resource utilization than traditional metaheuristic methods. Statistical analyses using the Wilcoxon Signed Rank test confirmed the effectiveness of this enhancement.

Data classification is an important research direction in machine learning. In order to effectively handle extensive datasets, researchers have introduced diverse classification algorithms. Notably, Kernel Extreme Learning Machine (KELM), as a fast and effective classification method, has received widespread attention. However, traditional KELM algorithms have some problems when dealing with large-scale data, such as the need to adjust hyperparameters, poor interpretability, and low classification accuracy. To address these problems, this paper proposes an Enhanced Adaptive Whale Optimization Algorithm to optimize Kernel Extreme Learning Machine (EAWOA-KELM). Various methods were used to improve WOA. As a first step, a novel adaptive perturbation technique employing T-distribution is proposed to perturb the optimal position and avoid being trapped in a local maximum. Secondly, the WOA’s position update formula was modified by incorporating inertia weight ω and enhancing convergence factor α, thus improving its capability for local search. Furthermore, inspired by the grey wolf optimization algorithm, use 3 excellent particle surround strategies instead of the original random selecting particles. Finally, a novel Levy flight was implemented to promote the diversity of whale distribution. Results from experiments confirm that the enhanced WOA algorithm outperforms the standard WOA algorithm in terms of both fitness value and convergence speed. EAWOA demonstrates superior optimization accuracy compared to WOA across 21 test functions, with a notable edge on certain functions. The application of the upgraded WOA algorithm in KELM significantly improves the accuracy and efficiency of data classification by optimizing hyperparameters. This paper selects 7 datasets for classification experiments. Compared with the KELM optimized by WOA, the EAWOA optimized KELM in this paper has a significant improvement in performance, with a 5%-6% lead on some datasets, indicating the effectiveness of EAWOA-KELM in classification tasks.

An adaptive balance optimization algorithm and its engineering application

A MPR optimization algorithm for FSO communication system with star topology

Hybrid adaptive simplified human learning optimization algorithms for supply chain network design problem with possibility of direct shipment

Nonsmooth convex optimization problems are solved over fixed point sets of nonexpansive mappings by using a distributed optimization technique. This is done for a networked system with an operator, who manages the system, and a finite number of users, by solving the problem of minimizing the sum of the operator’s and users’ nondifferentiable, convex objective functions over the intersection of the operator’s and users’ convex constraint sets in a real Hilbert space. We assume that each of their constraint sets can be expressed as the fixed point set of an implementable nonexpansive mapping. This setting allows us to discuss nonsmooth convex optimization problems in which the metric projection onto the constraint set cannot be calculated explicitly. We propose a parallel subgradient algorithm for solving the problem by using the operator’s attribution such that it can communicate with all users. The proposed algorithm does not use any proximity operators, in contrast to conventional parallel algorithms for nonsmooth convex optimization. We first study its convergence property for a constant step-size rule. The analysis indicates that the proposed algorithm with a small constant step size approximates a solution to the problem. We next consider the case of a diminishing step-size sequence and prove that there exists a subsequence of the sequence generated by the algorithm which weakly converges to a solution to the problem. We also give numerical examples to support the convergence analyses.

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks.

In this paper, we propose a novel optimal quality adaptation algorithm for MPEG-4 fine granular scalability (FGS) stream over wired network. Our algorithm can maximize perceptual video quality by minimizing video quality variation and increasing available bandwidth usage rate. Under the condition that the whole bandwidth evolution is known, we design an optimal algorithm to select layer. When the knowledge of future bandwidth is not available, we also develop an online algorithm based on the optimal algorithm. Simulation showed that both optimal algorithm and online algorithm can offer smoothed video quality evolution.