Logistics distribution path optimization research based on adaptive chaotic disturbance flies optimization algorithm

With the development of a sustainable economy, higher requirements are put forward for logistics enterprises, which not only need to meet the requirements of profit growth but also to meet the need of sustainable development. A vehicle routing problem (VRP) optimization model considering carbon emissions and multifuel‐type vehicles (VRP‐CEMF) is proposed to solve the problems of air pollution and high transportation cost in the current logistics distribution. An improved genetic algorithm (IGA) is designed to solve the VRP‐CEMF. The impact of carbon emissions and multifuel‐type vehicles on the logistics distribution path is explored by a real example simulation. The results show that the logistics distribution path optimization considering carbon emissions and multifuel‐type vehicles including hybrid electric vehicles and hydrogen‐fueled vehicles can significantly reduce carbon emissions on the premise of ensuring the lowest total cost. Furthermore, the impact of carbon emissions, hydrogen fuel price, and customer demand on the logistics distribution path is discussed by sensitivity analysis. The research results of this paper provide an effective reference for enterprises to control carbon emissions in the process of logistics distribution and promote the green transformation of logistics.

Optimization of perishable agricultural products logistics distribution path based on IACO-time window constraint

Analysis of distribution path optimization algorithm based on big data technology

The cold chain logistics distribution industry not only demands all goods can be timely distribution but also requires to reduce the entire logistics transportation cost as far as possible, and distribution vehicle route optimization is the key problem of cold chain logistics transportation cost calculation. The traditional optimization method spends a lot of time to search so that it is tough to find the globally optimal path approach, which results in higher distribution costs and lower efficiency. To solve the abovementioned problems, a cold logistics distribution path optimization solution, ground on an improved ant colony optimization algorithm (IACO) is formulated. Specially, other constraints, e.g., the transport time factor, transport cooling factor, and mean road patency factor, can be added to the unified IACO. Meanwhile, the updating mode of traditional pheromone is improved to limit the maximum and minimum pheromone concentration on the road and change the path selection transfer probability. The simulation results and experiment make clear that the IACO algorithm is lower than the chaotic-simulated annealing ant colony algorithm (CSAACO) and the traditional ACO algorithm in terms of convergence speed, logistics transportation distance, and logistics delivery time. At the same time, we have successfully obtained the optimal logistics distribution path, which can provide valuable reference information for improving the economic benefits of cold chain logistics enterprises.

With the proposal of the "double-carbon" development goal, all walks of life are actively implementing the national green and low-carbon development requirements. As an important branch of the logistics industry, cold chain logistics is also a key area to achieve the goal of "double carbon". If the concept of low carbon is introduced into cold chain logistics and the distribution route is rationally planned, it can not only reduce logistics costs, but also reduce carbon emissions. Therefore, the minimization of the total distribution cost is taken as the optimization goal, Under the constraints of vehicle load and time window, the cold chain logistics distribution path optimization model is constructed with the goal of minimizing the total distribution cost. Then designed the genetic algorithm to solve, respectively from considering carbon emissions and carbon emissions two cases planning distribution route, and optimization before single temperature distribution scheme comparative analysis, the results show that the carbon emissions considering the cold chain distribution path optimization, and choose cold storage incubator type in distribution mode, not only can reduce the enterprise distribution cost, improve the distribution efficiency of distribution, but also can reduce the carbon emissions.

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.

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.

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.

This paper considers a stochastic optimization problem over the fixed point sets of quasinonexpansive mappings on Riemannian manifolds. The problem enables us to consider Riemannian hierarchical optimization problems over complicated sets, such as the intersection of many closed convex sets, the set of all minimizers of a nonsmooth convex function, and the intersection of sublevel sets of nonsmooth convex functions. We focus on adaptive learning rate optimization algorithms, which adapt step-sizes (referred to as learning rates in the machine learning field) to find optimal solutions quickly. We then propose a Riemannian stochastic fixed point optimization algorithm, which combines fixed point approximation methods on Riemannian manifolds with the adaptive learning rate optimization algorithms. We also give convergence analyses of the proposed algorithm for nonsmooth convex and smooth nonconvex optimization. The analysis results indicate that, with small constant step-sizes, the proposed algorithm approximates a solution to the problem. Consideration of the case in which step-size sequences are diminishing demonstrates that the proposed algorithm solves the problem with a guaranteed convergence rate. This paper also provides numerical comparisons that demonstrate the effectiveness of the proposed algorithms with formulas based on the adaptive learning rate optimization algorithms, such as Adam and AMSGrad.

With the intensification of global climate change, reducing carbon emissions has become an urgent task for various industries. The logistics industry has high carbon emissions, and traditional path planning ignores environmental impacts. The current hot topic is to study the optimization of logistics paths that consider carbon emissions, in order to achieve a win-win situation for both the economy and the environment. This article aims to explore relevant theories and methods, propose solutions to reduce logistics carbon emissions, and promote sustainable development. Taking the optimization of logistics distribution paths in a certain enterprise as the research object, starting from the global background of energy conservation and emission reduction, this paper proposes to take the total cost (including time window penalty, transportation, carbon emissions, and fixed costs) as the optimization objective, construct a carbon emission logistics distribution path optimization model with time window, solve it using the improved grey wolf optimization algorithm, and compare it with the original plan. The results showed that after optimization, the cost was significantly reduced, verifying the effectiveness of the model, reducing delivery costs, and improving the economic benefits of the enterprise. Not only does it solve the problems of the enterprise, but it also provides reference for related industries.

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.

Parameter self-tuning of servo control systems is crucial for optimizing automation processes, especially in complex systems such as permanent magnet synchronous motors. In this paper, a nonlinear adaptive whale optimization algorithm (NAWOA) is proposed and applied to parameter self-tuning, which improves the traditional whale optimization algorithm (WOA) by nonlinearly adaptively adjusting two parameters during optimization to enhance fast convergence and global search capabilities. A servo control system with three parameters to be tuned is constructed using both simulation and physical methods. Simulation and experimental results show that the NAWOA outperforms the genetic algorithm, particle swarm optimization, and WOA in parameter self-tuning of the servo control system with lower error indicators and fast convergence speed. Although it still faces the challenge of initial condition dependency, the proposed NAWOA provides a powerful solution for real-time industrial applications.

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).

Flexible job shop scheduling problem (FJSP) is a discrete combinatorial optimisation problem. By considering its characteristics, a kind of adaptive discrete cat swarm optimisation algorithm (ADCSO) is proposed with three objectives to be optimised simultaneously, i.e., makespan, maximal machine workload and total workload. Firstly, a two-phase encoding mechanism is proposed to represent each individual, and a heuristic-based strategy is employed to generate the initial population. Secondly, the discrete seeking mode and tracing mode are developed to make the algorithm directly work in a discrete domain. Thirdly, four adaptive adjustment approaches with different decline curves are used to balance the abilities of global search and local search. Furthermore, a local search strategy is embedded into the algorithm to improve the searching capacity. Finally, extensive experimental data are obtained based on the benchmark instances. The comparison results show that ADCSO is effective for the problem under study.

Address wind farm layout problems by an adaptive Moth-flame Optimization Algorithm