greedy algorithm

Efficient partitioning of the atomic space among parallel FPGAs is crucial for accelerating molecular simulations. Existing research has primarily focused on uniform partitioning, assuming a homogeneous distribution of atoms. However, in scenarios with non-uniform atomic distributions, these approaches may lead to suboptimal performance. This study investigates the impact of non-uniform atom distributions on molecular simulation performance across parallel FPGAs. We propose a novel space partitioning scheme that optimizes the distribution of atomic space among FPGAs, taking into account the spatial heterogeneity of atoms. Our evaluation demonstrates that the proposed scheme consistently outperforms uniform partitioning in terms of simulation speed across various spatial dimensions and atom counts, particularly in scenarios with non-uniform atom distributions.

The Influence Maximization Problem in social networks aims to find a minimal set of individuals to produce the highest influence on other individuals in the network. In the last two decades, a lot of algorithms have been proposed to solve the time efficiency and effectiveness challenges of this NP-Hard problem. Undoubtedly, the CELF algorithm (besides the naive greedy algorithm) has the highest effectiveness among them. Of course, the CELF algorithm is faster than the naive greedy algorithm (about 700 times). This superiority has led many researchers to make extensive use of the CELF algorithm in their innovative approaches. However, the main drawback of the CELF algorithm is the very long running time of its first iteration. Because it has to estimate the influence spread for all nodes by expensive Monte-Carlo simulations, similar to the naive greedy algorithm. In this paper, a heuristic approach is proposed, namely Optimized-CELF algorithm, to improve this drawback of the CELF algorithm by avoiding unnecessary Monte-Carlo simulations. It is found that the proposed algorithm reduces the CELF running time, and subsequently improves the time efficiency of other algorithms that employed the CELF as a base algorithm. Experimental results on the wide spectral of real datasets showed that the Optimized-CELF algorithm provided better running time gain, about 88-99% and 56-98% for k=1 and k=50, respectively, compared to the CELF algorithm without missing effectiveness.

This paper presented greedy algorithm for solving student allocation problem that has arisen in internship program. In internship program, engineering students stay one semester in industries which are located across the country and teachers visit students once/twice for supervision during the program. As the industries scatter across the country, teachers spend long time on travel. And this results in wastage of teachers working time and money spent for transport. Therefore, allocating students to universities near the internship location extensively reduces the transport time and money spent for transport. For the current study, we consider 4th mechanical engineering students who are currently working in the industry. The proposed approach extensively decrease the distance traveled from 23,210 km to 2,488.8 km and the time spent on the road from 397 hrs. 40 min to 51 hrs. 30 min. and finally, the results obtained from the greedy algorithm is compared with other heuristics (i.e., Genetic algorithm and Particle swarm optimization) and the greedy algorithm outperforms the other methods.

Transportation in economic systems such as services, production and distribution enjoys a special and important position and provides a significant portion of the country's gross domestic product. Improvements in transportation system mean improvements in the traveling routes and the elimination of unnecessary distances in any system. The Vehicle Routing Problem (VRP) is one of the practical concepts in the field of investigation and many attempts have been made by researchers in this area. Due to the importance of transportation issues in the real world and the status of these issues in the types of existing systems. In this paper, we investigate the Vehicle Routing Problem with Time Window (VRPTW) and provide a solution for it. The problem of routing vehicles with a time window is an extension of the problem of routing vehicles with limited capacity (CVRP) in which servicing must be done in a specific time window. The purpose of this problem is to optimize the route for each vehicle so as to minimize the total cost of the route and the number of vehicles used, and ultimately maximize customer satisfaction. In the paper, a hybrid method based on cuckoo search and greedy algorithm is proposed to solve the problem of VRPTW. For the cost function, different criteria have been used that are within the framework of the VRPTW problem within hard and soft constraints. In order to evaluate the proposed method, the dataset is used in different sizes. The proposed method is significantly higher compared to similar methods.

A new integer program is presented to model an independent resources assignment problem with resource shortages in the context of municipal fire service. When shortage in resources exists, a critical task for fire department's administrator in a city is to assign the available resources to the fire stations such that the effect of the shortage to cover (in providing service, in extinguishing fire and so on) is minimized. To solve the problem, we propose a polynomial time greedy algorithm.