Tackling the rich vehicle routing problem with nature-inspired algorithms

In the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.

COMPARATIVE ANALYSIS OF BIO-INSPIRED OPTIMIZATION ALGORITHMS IN NEURAL NETWORK BASED DATA MINING CLASSIFICATION

It always helps to determine optimal solutions for stochastic problems thereby maintaining good balance between its key elements. Nature inspired algorithms are meta-heuristics that mimic the natural activities for solving optimization issues in the era of computation. In the past decades, several research works have been presented for optimization especially in the field of data mining. This paper addresses the implementation of bio-inspired optimization techniques for machine learning based data mining classification by four different optimization algorithms. The stochastic problems are overcome by training the neural network model with techniques such as barnacles mating , black widow optimization, cuckoo algorithm and elephant herd optimization. The experiments are performed on five different datasets, and the outcomes are compared with existing methods with respect to runtime, mean square error and classification rate. From the experimental analysis, the proposed bio-inspired optimization algorithms are found to be effective for classification with neural network training.

COMPARATIVE ANALYSIS OF BIO-INSPIRED OPTIMIZATION ALGORITHMS IN NEURAL NETWORK BASED DATA MINING CLASSIFICATION

It always helps to determine optimal solutions for stochastic problems thereby maintaining good balance between its key elements. Nature inspired algorithms are meta-heuristics that mimic the natural activities for solving optimization issues in the era of computation. In the past decades, several research works have been presented for optimization especially in the field of data mining. This paper addresses the implementation of bio-inspired optimization techniques for machine learning based data mining classification by four different optimization algorithms. The stochastic problems are overcome by training the neural network model with techniques such as barnacles mating , black widow optimization, cuckoo algorithm and elephant herd optimization. The experiments are performed on five different datasets, and the outcomes are compared with existing methods with respect to runtime, mean square error and classification rate. From the experimental analysis, the proposed bio-inspired optimization algorithms are found to be effective for classification with neural network training.

A Modified Cuckoo Search Algorithm for Data Clustering

Clustering of data is one of the necessary data mining techniques, where similar objects are grouped in the same cluster. In recent years, many nature-inspired based clustering techniques have been proposed, which have led to some encouraging results. This paper proposes a Modified Cuckoo Search (MoCS) algorithm. In this proposed work, an attempt has been made to balance the exploration of the Cuckoo Search (CS) algorithm and to increase the potential of the exploration to avoid premature convergence. This algorithm is tested using fifteen benchmark test functions and is proved as an efficient algorithm in comparison to the CS algorithm. Further, this method is compared with well-known nature-inspired algorithms such as Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Particle Swarm Optimization with Age Group topology (PSOAG) and CS algorithm for clustering of data using six real datasets. The experimental results indicate that the MoCS algorithm achieves better results as compared to other algorithms in finding optimal cluster centers.

Optimized Resource Allocation in IoT using Fuzzy Logic and Bio-Inspired Algorithms

IoT smart devices are a confluence of microprocessors, sensors, power source and transceiver modules to effectively sense, communicate and transfer data. Energy efficiency is a key governing value of the network performance of smart devices in distributed IoT networks.Low and discrete power and limited amount of memory and finite amount of resources form some major bottlenecks in the workflow.Dynamic load balancing, reliability and flexibility are heavily relied upon by cloud computing for its accessibility.Resources are dynamically provided to the end client in an as-come on-demand fashion with the global network that is the Internet. Proportionally the need for services is increasing at a rate that is astonishing compared to any other forms of development. Load balancing seems a major challenge faced due to the architecture and the modular nature of our cloud environment. Loads need to be distributed dynamically to all the nodes. In this paper, we have introduced a technique that combines fuzzy logic with various nature inspired algorithms - grey wolf algorithm and firefly algorithm in order to effectively balance the load in a network of IoT devices. The performances of various nature inspired algorithms are compared with a brute force approach on the basis of energy efficiency, network lifetime maximization, node failure rate and packet delivery ratio.

Locust Inspired Algorithm for Cloudlet Scheduling in Cloud Computing Environments

Cloud computing is an emerging paradigm that offers flexible and seamless services for users based on their needs, including user budget savings. However, the involvement of a vast number of cloud users has made the scheduling of users’ tasks (i.e., cloudlets) a challenging issue in selecting suitable data centres, servers (hosts), and virtual machines (VMs). Cloudlet scheduling is an NP-complete problem that can be solved using various meta-heuristic algorithms, which are quite popular due to their effectiveness. Massive user tasks and rapid growth in cloud resources have become increasingly complex challenges; therefore, an efficient algorithm is necessary for allocating cloudlets efficiently to attain better execution times, resource utilisation, and waiting times. This paper proposes a cloudlet scheduling, locust inspired algorithm to reduce the average makespan and waiting time and to boost VM and server utilisation. The CloudSim toolkit was used to evaluate our algorithm’s efficiency, and the obtained results revealed that our algorithm outperforms other state-of-the-art nature-inspired algorithms, improving the average makespan, waiting time, and resource utilisation.