Clustering and heuristics algorithm for the vehicle routing problem with time windows

This article presents a novel algorithm based on the cluster first-route second method, which executes a solution through K-means and Optics clustering techniques and Nearest Neighbor and Local Search 2-opt heuristics, for the solution of a vehicle routing problem with time windows (VRPTW). The objective of the problem focuses on reducing distances, supported by the variables of demand, delivery points, capacities, time windows and type of fleet in synergy with the model's taxonomy, based on data referring to deliveries made by a logistics operator in Colombia. As a result, good solutions are generated in minimum time periods after fulfilling the agreed constraints, providing high performance in route generation and solutions for large customer instances. Similarly, the algorithm demonstrates efficiency and competitiveness compared to other methods detailed in the literature, after being benchmarked with the Solomon instance data set, exporting even better results.

Open Sub-Granting Radio Resources in Overlay D2D-Based V2V Communications

Capacity, reliability, and latency are seen as key requirements of new emerging applications, namely Vehicle-to-Everything (V2X) and Machine Type Communication (MTC) in future cellular networks. Device-to-Device (D2D) communication is envisaged to be the enabler to accomplish the requirements for the applications as mentioned earlier. Due to the scarcity of radio resources, a hierarchical radio resource allocation, namely the sub-granting scheme, has been considered for the overlay D2D communication. In this paper, we investigate the assignment of underutilized radio resources from D2D communication to Device-to-Infrastructure (D2I) communication, which are moving in a dynamic environment. The sub-granting assignment problem is cast as a maximization problem of the uplink cell throughput. Firstly, we evaluate the sub-granting signaling overhead due to mobility in a centralized sub-granting resource algorithm, Dedicated Sub-Granting Radio Resource (DSGRR), and then a distributed heuristics algorithm, Open Sub-Granting Radio Resource (OSGRR) is proposed and compared with the DSGRR algorithm and no sub-granting case. Simulation results show improved cell throughput for the OSGRR compared with other algorithms. Besides, it is observed that the overhead incurred by the OSGRR is less than the DSGRR while the achieved cell throughput is yet close to the maximum achievable uplink cell throughput.

A Method for Transportation Planning and Profit Sharing in Collaborative Multi-Carrier Vehicle Routing

This paper addresses a collaborative multi-carrier vehicle routing problem (CMCVRP) where carriers tackle their orders collaboratively to reduce transportation costs. First, a hierarchical heuristics algorithm is proposed to solve the transportation planning problem. This algorithm makes order assignments based on two distance rules and solves the vehicle routing problem with a hybrid genetic algorithm. Second, the profit arising from the coalition is quantified, and an improved Shapley value method is proposed to distribute the profit fairly to individual players. Extensive experiment results showed the effectiveness of the proposed hierarchical heuristics algorithm and confirmed the stability and fairness of the improved Shapley value method.

Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm

Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively.

Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network

The most vital step in mining data’s in order to have a proper decision making is the classification, it is remains important in multiple of human activities such as the industrial applications, marketing campaigns, research process and the scientific endeavors. The process of classifying involves the objects categorization into classes that are already defined. These categorizations are developed according to the identical attributes of the items or the objects. Multitudes of methods were devised to improve the accuracy in the classification to devour an enhanced performance in terms of faster convergence speed. The algorithm based on water cycle that includes the evaporation, condensation and precipitation (WC-ECP), which is a population based metaheuristic is used in the paper to improve the accuracy in the feed forward neural network (PNN-probabilistic neural network) to standardizes its random constraint choice and in turn improvise the accuracy of the categorization and the speed of the convergence. The proposed method was tested with the five dataset of UCI machine learning repository and was evinced that the WCECP-PNN performed better compared to the other evolutionary algorithms such as the GA which is also a population based Meta-heuristics

Timeseries Forecasting using Long Short-Term Memory Optimized by Multi Heuristics Algorithm

Forecasting future price of financial instruments (such as equity, bonds and mutual funds) has become an ongoing effort of financial and capital market industry members. The most current technology is usually applied by high economic scale companies to solve the ambitious and complicated problem. This paper presents optimization solution for a deep learning model in forecasting selected Indonesian mutual funds' Net Asset Value (NAV). There is a well-known issue in determining a deep learning parameters in LSTM network like window timestep and number of neurons to be used in getting the optimal learning from the historical data. This research tries to provide solution by utilizing multi-heuristics optimization approach consists of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to determine the best LSTM's network parameters, namely window timesteps and number of neurons. The result shows that from the nine selected mutual funds, PSO outperforms GA in optimizing the LSTM model by giving a lower Root Square Mean Error (RMSE) by 460.84% compared to GA's. However, PSO took a longer execution time by 1.78 times of GA's. This paper also confirms that based on RMSE for both training and evaluation dataset, equity mutual fund's forecasted NAV has the highest RMSE followed by fixed income mutual fund's forecasted NAV and money market mutual fund forecasted NAV.

Deadline Aware Prioritized Task Scheduling Algorithm in Cloud Computing

Cloud Computing is a computing Paradigm in which services are provided by service providers on pay-per-use. Task Scheduling is the challenging issue in cloud computing. Task scheduling refers to allocating tasks to available resources to achieve better performance of the system. Here we have proposed a Heuristics algorithm to schedule tasks in given resources which satisfies the QoS of system taking Priority and Deadline of tasks as parameters. Our algorithm is compared with existing algorithms like EDF and TLD algorithms. Our algorithm provides better makespan, increases throughput and utilize resources well compared to existing algorithms

Fuzzy Classification with Comprehensive Learning Gravitational Search Algorithm in Breast Tumor Detection

The research paper herewith presents an effectual diagnosis classification system using fuzzy classifier and a very efficient heuristics algorithm comprehensive learning gravitational search algorithm (CLGSA) which has a good ability to search and finding optimal solutions. The effectiveness of the proposed model is estimating on Wisconsin breast cancer data set available in the UCI Machine learning source in the University of California, Irvine. We testify the data over the parameters of classification of accurateness, sensitivity as well as specificity with a much better and more responsive 10-fold cross validation method; which is considered as a reliable diagnostics model in the medical field. Experiment results have clearly shown that the proposed approach will turn out to be a calculative and decisive medium for cancer detection in the field of medicine