Particle Swarm Optimization and Tabu Search Hybrid Algorithm for Flexible Job Shop Scheduling Problem – Analysis of Test Results

A Robust Feature Construction for Fish Classification Using Grey Wolf Optimizer

The low quality of the collected fish image data directly from its habitat affects its feature qualities. Previous studies tended to be more concerned with finding the best method rather than the feature quality. This article proposes a new fish classification workflow using a combination of Contrast-Adaptive Color Correction (NCACC) image enhancement and optimization-based feature construction called Grey Wolf Optimizer (GWO). This approach improves the image feature extraction results to obtain new and more meaningful features. This article compares the GWO-based and other optimization method-based fish classification on the newly generated features. The comparison results show that GWO-based classification had 0.22% lower accuracy than GA-based but 1.13 % higher than PSO. Based on ANOVA tests, the accuracy of GA and GWO were statistically indifferent, and GWO and PSO were statistically different. On the other hand, GWO-based performed 0.61 times faster than GA-based classification and 1.36 minutes faster than the other.

Uncertainty Aware T2SS Based Dyna-Q-Learning Framework for Task Scheduling in Grid Computing

Task scheduling is an important activity in parallel and distributed computing environment like grid because the performance depends on it. Task scheduling gets affected by behavioral and primary uncertainties. Behavioral uncertainty arises due to variability in the workload characteristics, size of data and dynamic partitioning of applications. Primary uncertainty arises due to variability in data handling capabilities, processor context switching and interplay between the computation intensive applications. In this paper behavioral uncertainty and primary uncertainty with respect to tasks and resources parameters are managed using Type-2-Soft-Set (T2SS) theory. Dyna-Q-Learning task scheduling technique is designed over the uncertainty free tasks and resource parameters. The results obtained are further validated through simulation using GridSim simulator. The performance is good based on metrics such as learning rate, accuracy, execution time and resource utilization rate.

A Proposal for Honeyword Generation via Meerkat Clan Algorithm

An effective password cracking detection system is the honeyword system. The Honeyword method attempts to increase the security of hashed passwords by making password cracking easier to detect. Each user in the system has many honeywords in the password database. If the attacker logs in using a honeyword, a quiet alert trigger indicates that the password database has been hacked. Many honeyword generation methods have been proposed, they have a weakness in generating process, do not support all honeyword properties, and have many honeyword issues. This article proposes a novel method to generate honeyword using the meerkat clan intelligence algorithm, a metaheuristic swarm intelligence algorithm. The proposed generation methods will improve the honeyword generating process, enhance the honeyword properties, and solve the issues of previous methods. This work will show some previous generation methods, explain the proposed method, discuss the experimental results and compare the new one with the prior ones.

Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation

This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel genetic algorithm. The system being developed uses GPUs for the implementation of a massive agent-based model of Autonomous Vehicle (AV) behaviour in an Artificial Multi-Connected Road Network (AMСRN) consisting of the “Manhattan Grid” and the “Circular Motion Area” that are crossed. A new parallel Real-Coded Genetic Algorithm with a Scalable Nonuniform Mutation (RCGA-SNUM) is developed. The proposed algorithm (RCGA-SNUM) has been examined with the use of known test instances and compared with parallel RCGAs used with other mutation operators (e.g., standard mutation, Power Mutation (PM), mutation with Dynamic Rates (DMR), Scalable Uniform Mutation (SUM), etc.). As a result, RCGA-SNUM demonstrates superiority in solving large-scale optimisation problems when decision variables have wide feasible ranges and multiple local extrema are observed. Following this, RCGA-SNUM is applied to minimising the number of potential traffic accidents in the AMСRN.

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.

Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.

Predictive Analysis of Dengue Outbreak Based on an Improved Salp Swarm Algorithm

The purpose of this study is to enhance the exploration capability of conventional Salp Swarm Algorithm (SSA) with the inducing of Levy Flight. With such modification, it will assist the SSA from trapping in local optimum. The proposed approach, which is later known as an improved SSA (iSSA) is employed in monthly dengue outbreak prediction. For that matter, monthly dataset of rainfall, humidity, temperature and number of dengue cases were employed, which render prediction information. The efficiency of the proposed algorithm is evaluated using Root Mean Square Error (RMSE), and compared against the conventional SSA and Ant Colony Optimization (ACO). The obtained results suggested that the iSSA was not only able to produce lower RMSE, but also capable to converge faster at lower rate as well.