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In view of the large randomness, redundant path nodes, and low search efficiency of RRT-connect in a complex obstacle environment, this study intends to develop a path-planning method combining RRT-connect and Informed RRT*. First, to solve the problem of large sampling randomness, the Informed RRT* algorithm is combined to adopt a simpler rectangle and limit the sampling range to the rectangle. Second, for the poor quality of the search path, the dynamic step size is used for growth extension, the reverse greedy algorithm is used to delete redundant nodes, the spline curve is used to smooth the path such that the position meets the cubic spline curve and the speed meets the quadratic spline curve, and the final path is optimized. Finally, the proposed algorithm is verified in the simulation and real world using a self-developed quadrupedal robot. Compared with the original RRT-connect algorithm, the first solution time, total number of nodes, and initial path cost were reduced by more than 11%, 8.5%, and 2.5%, respectively.

While tank vehicles are in the process of transporting liquid chemicals, the transportation task may be delayed or terminated due to disruptions in the transportation network. To reduce transportation losses, it may be necessary to evaluate network performance to cope with emergencies in the transportation network. This study investigates the vulnerability of the road network for liquid chemical transportation (RNLCT) under both intentional and random attacks. We selected six network indices, including average degree, average path length, average betweenness of node and edge, average clustering coefficient, and network efficiency to analyze the topological structure of the network. A reverse greedy algorithm is applied to identify 40 key nodes for simulated intentional attacks. The results show that network efficiency drops by 57.3% under targeted attacks, compared to 37.8% under random attacks. Moreover, the proposed method outperforms the distance laplacian centrality, which leads to a 43.3% reduction, demonstrating its effectiveness in identifying key nodes and enhancing network robustness.

Most of the existing influence maximization problem in social networks only focus on single relationship social networks, that is, there is only one relationship in social networks. However, in reality, there are often many relationships among users of social networks, and these relationships jointly affect the propagation of network information and its final scope of influence. Based on the classical linear threshold model and combined with various relationships between network nodes, in this paper MRSN-LT propagation model is proposed to model the influence propagation process between nodes in multiple relationships social networks. Then, MRSN-RRset algorithm based on reverse reachable set is proposed to solve the problem of low computational performance caused by greedy algorithm in the research process of traditional influence maximization. Finally, the experimental results on real data sets show that the proposed method has better influence propagation scope and greater computational performance improvement.

In this paper, Greedy Algorithms such as Kruskal’s, Prim’s, Boruvka’s, Reverse-delete Algorithm were applied on different types of graphs like Mobius-Kantor graph, Durer graph, Golomb graph to find the (MST).

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing.

Software modularization techniques are employed to understand a software system. The purpose of modularization is to decompose a software system from a source code into meaningful and understandable subsystems (modules). Since modularization of a software system is an NP-hard problem, the modularization quality obtained using evolutionary algorithms is more reasonable than greedy algorithms. All evolutionary algorithms presented for software modularization only consider structural features that are dependent on the syntax of programming languages. For most programming languages, does not exist a tool to extract structural features, so it is not possible to modularize them. To overcome this problem, this paper presents a new multi-objective fitness function, named MOF, which exploits the structural (such as calling dependency and inheritance dependency) and non-structural features (such as semantic contained in the code comments and identifier names), aiming to automatically guide optimization algorithms to find a good decomposition of software systems. To evaluate the performance of this objective function, three optimization strategies, namely global-based search, combining global and local search, and Estimation of Distribution (EoD), are adapted to optimize it. The results on Mozilla Firefox indicate that the proposed algorithm which is based on EoD along with the new MOF function outperforms the algorithms that use structural-based objective functions in guiding the optimization process, in finding more understandable modules.

While handling problems of certain complex scene optimization, the Whale Optimization Algorithm (WOA) algorithm may be affected by precocious convergence or local optimal solutions, resulting in the accuracy of low convergence and stagnation of dimensional population. To address these limitations, this research presents a whale optimization algorithm, which is established on pinhole imaging reverse learning and the golden sine strategy (LWOAG). Firstly, LWOAG employs pinhole imaging reverse learning to determine the reverse solution for optimal individual in the population, thereby improving the population's quality and algorithm convergence ability. Secondly, LWOAG utilizes the golden sine operator to perform greedy selection after the whale completes the search update, thus extending the search range and increasing the algorithm's global search capacity. Finally, after conducting comprehensive tests on 12 benchmark functions, LWOAG outperforms other enhanced whale optimization algorithms and intelligent algorithms in terms of accuracy and stability.

Lead acid batteries, as batteries with both cost and performance, are widely used in various fields such as transportation and communication. However, improper recycling can lead to increased environmental pollution. A hybrid lead acid battery reverse logistics center location model based on the genetic algorithm and greedy algorithm is proposed. Firstly, the basic mode of reverse logistics is introduced. A basic model of reverse logistics center location network for lead acid batteries is established based on relevant location principles such as non-zero constraints and cost control conditions. Then, genetic algorithm and greedy algorithm are introduced to solve and analyze the overall model. The performance of each algorithm is applied. Meanwhile, a hybrid algorithm is designed. Finally, the performance of the model is analyzed through experiments, comparing the performance of the individual genetic algorithm, greedy algorithm, and hybrid algorithm. Accuracy, recall, F1 value, and time complexity are selected as evaluation indicators. The total cost and sustainability scores of different models are compared. The experimental results showed that the accuracy of the hybrid algorithm model reached 98.82%, and the recall rate reached 97.39%. The average running time of the hybrid algorithm was 36.14% lower than that of the genetic algorithm. The average running time of the hybrid algorithm was 3.42 s. The average Gap value of the model used in the study was 51.02% lower than that of the comparison models based on dynamic adaptive particle swarm optimization algorithm, an optimized firefly algorithm, and a two-layer programming genetic algorithm-based center location model. The average total cost decreased by 39.96%. The sustainability score was 24.69% higher than the other models on average. The total construction cost of the hybrid algorithm model was lower than the other algorithms by 940000-yuan, 330000 yuan, and 850000 yuan, respectively, with an average cost reduction of 39.96%. Therefore, the proposed location selection model for lead acid battery reverse logistics centers based on the genetic-greedy hybrid algorithm can achieve low-cost and short transportation route center point calculation.

Large scale software systems must be decomposed into modular units to reduce maintenance efforts. Software Architecture Recovery (SAR) approaches have been introduced to analyze dependencies among software modules and automatically cluster them to achieve high modularity. These approaches employ various types of algorithms for clustering software modules. In this paper, we discuss design decisions and variations in existing genetic algorithms devised for SAR. We present a novel hybrid genetic algorithm that introduces three major differences with respect to these algorithms. First, it employs a greedy heuristic algorithm to automatically determine the number of clusters and enrich the initial population that is generated randomly. Second, it uses a different solution representation that facilitates an arithmetic crossover operator. Third, it is hybridized with a heuristic that improves solutions in each iteration. We present an empirical evaluation with seven real systems as experimental objects. We compare the effectiveness of our algorithm with respect to a baseline and state-of-the-art hybrid genetic algorithms. Our algorithm outperforms others in maximizing the modularity of the obtained clusters.

The influence maximization (IM) problem aims to find a set of seed nodes that maximizes the spread of their influence in a social network. The positive influence maximization (PIM) problem is an extension of the IM problem, which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely spread. To solve the PIM problem, this paper proposes the polar and decay related independent cascade (IC-PD) model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social networks. To overcome the low efficiency of the greedy based algorithm, this paper defines the polar reverse reachable (PRR) set and devises a signed reverse influence sampling (SRIS) algorithm. The algorithm utilizes the IC-PD model as well as the PRR set to select seeds. There are two phases in SRIS. One is the sampling phase, which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR sets. The other is the node selection phase, which uses a greedy coverage algorithm to select optimal seeds. Finally, Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in effectiveness. Especially on the Slashdot dataset, SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.