Developing an algorithm that can solve the vehicle routing problem with time windows (VRPTW) and create near-optimal solutions with the least difference in magnitude is a challenging task. This task is evident from the fact that when an algorithm runs multiple times based on a given instance, the generated solutions deviate from each other and may not near-optimal. For this reason, an algorithm that can solve these problems is effective and highly sought after. This paper proposes a novel systematic framework using a two-phase distributed ruin-and-recreate genetic algorithm (RRGA). The two-phase consists of the RRGA phase and ruin-and-recreate (RR) phase, which is designed to run in the distributed computing environment that leveraging these networked resources. This combination of algorithms harnesses the strength of the search diversification and intensification, thereby producing very high-quality solutions. Experiments with Solomon’s benchmark show the RRGA can produce results superior to the recently published hybrid algorithms, best-known solutions, and nine leading hybrid algorithms.

The use of Convolutional Neural Networks (CNN) for the application of wood defects detection has gained significant attention in recent years. In industrial settings, these tasks are typically performed in a strict and consistent environment, making the use of large and complex CNN models unnecessary. Despite this, recent research has continued to focus on the use of such models to achieve increasingly accurate detections. These models require costly machines for inference, making adoption less likely especially for manufacturers in the developing nations. In view of this limitation, this paper proposes a set of strategies to achieve a highly efficient CNN model for fast and accurate wood defects detection based on the YOLOv4-Tiny architecture. The model has been improved with Efficient Channel Attention (ECA) to better select multi-scale features from the backbone network and has been drastically reduced in size through an iterative pruning and recovery process. This results in an 88% reduction in model parameters while retaining accuracy comparable to most state-of-the-art (SOTA) methods. Consequently, the model can perform near real-time inference directly on a general-purpose embedded processor without external hardware accelerators. This research hopes to motivate the development of efficient defect detectors that can run on low-cost embedded devices.

Clustering is one of the most commonly used approaches in data mining and data analysis. One clustering technique in clustering that gains big attention in clustering related research isk-means clustering such that the observation is grouped intokcluster. However, some obstacles such as the adherence of results to the initial cluster centers or the risk of getting trapped into local optimality hinder the overall clustering performance. The purpose of this research is to minimize the dissimilarity of all points of a cluster from gravity center of the cluster with respect to capacity constraints in each cluster, such that each element is allocated to only one cluster. This paper proposes an effective combination algorithm to find optimal cluster center for the analysis of data in data mining and a new combination algorithm is proposed to untangle the clustering problem. This paper presents a new hybrid algorithm, which is, based on cluster center initialization algorithm (CCIA), bees algorithm (BA), and differential evolution (DE), known as CCIA-BADE-K, aiming at finding the best cluster center. The proposed algorithm performance is evaluated with standard data set. The evaluation results of the proposed algorithm and its comparison with other alternative algorithms in the literature confirm its superior performance and higher efficiency.

Data Clustering is one of the most used methods of data mining. The k-means Clustering Approach is one of the main algorithms in the literature of Pattern Recognition and Data Machine Learning which it very popular because of its simple application and high operational speed. But some obstacles such as the adherence of results to initial cluster centers or the risk of getting trapped into local optimality hinders its performance. In this paper, inspired by the Imperialist Competitive Algorithm based on the k-means method, a new approach is developed, in which cluster centers are selected and computed appropriately. The Imperialist Competitive Algorithm (ICA) is a method in the field of evolutionary computations, trying to find the optimum solution for diverse optimization problems. The underlying traits of this algorithm are taken from the evolutionary process of social, economic and political development of countries so that by partly mathematical modeling of this process some operators are obtained in regular algorithmic forms. The investigated results of the suggested approach over using standard data sets and comparing it with alternative methods in the literature reveals out that the proposed algorithm outperforms the k-means algorithm and other candidate algorithms in the pool.

Data clustering is one of widely used methods for data mining. The k-means approach is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. But some hindrances such as the sensitivity to initial values and cluster centers or the risk of trapping in local optimal reduce its best performance. The purpose of kmeans method is minimizing the dissimilarity of observations, from cluster centers. In this paper, a new solution method inspired by harmony search combined with bee algorithm is introduced to improve performance k-means clustering. In this study, harmony and clustering structures are combined to produce harmony clustering. To avoid initial random selection, seed cluster center is considered in primary population as well as bee algorithm has been employed to increase the efficiency of algorithm. The proposed methods have been tested on standard benchmark data sets and also compared to other methods in the literature; it is noted that results show a promising performance leading to better efficiency and capability of the proposed solution.