Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Multi objective particle swarm optimization: algorithms and applications

Ph.DDOCTOR OF PHILOSOPH

Comparing several heuristics for a packing problem

Packing problems are in general NP-hard, even for simple cases. Since now there are no highly efficient algorithms available for solving packing problems. The two-dimensional bin packing problem is about packing all given rectangular items, into a minimum size rectangular bin, without overlapping. The restriction is that the items cannot be rotated. The current paper is comparing a greedy algorithm with a hybrid genetic algorithm in order to see which technique is better for the given problem. The algorithms are tested on different sizes data.Comment: 5 figures, 2 tables; accepted: International Journal of Advanced Intelligence Paradigm

Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities

Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. While researchers worldwide have proposed a wide variety of EAs, certain limitations remain, such as slow convergence speed and poor generalization capabilities. Consequently, numerous scholars actively explore improvements to algorithmic structures, operators, search patterns, etc., to enhance their optimization performance. Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years. This paper presents a comprehensive survey on integrating reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA). We begin with the conceptual outlines of reinforcement learning and the evolutionary algorithm. We then provide a taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. The RL-assisted procedure is divided according to the implemented functions including solution generation, learnable objective function, algorithm/operator/sub-population selection, parameter adaptation, and other strategies. Finally, we analyze potential directions for future research. This survey serves as a rich resource for researchers interested in RL-EA as it overviews the current state-of-the-art and highlights the associated challenges. By leveraging this survey, readers can swiftly gain insights into RL-EA to develop efficient algorithms, thereby fostering further advancements in this emerging field.Comment: 26 pages, 16 figure

Evolutionary population dynamics and multi-objective optimisation problems

Griffith Sciences, School of Information and Communication TechnologyFull Tex

OPTIMIZATION APPROACH FOR DESIGN OF SPUR GEAR BASED ON GENETIC ALGORITHM

Abstract -  The problem of designing spur gear with minimum mass and smaller size without violating the constraints plays a major role in today's industrial world, since the most commonly encountered mechanical power transmission require low weight. This paper presents an genetic approach to reduce the weight and thickness of the gear, also increases the power transmitting capacity and effectiveness using genetic algorithm (GA). It can be observed that the proposed optimal design with GA has the potential to yield considerably better solutions than the traditional heuristics. At the same time, the GA offer a better understanding of the trade-offs between various constraints.   Key words: Optimal design, genetic algorithm, Spur gea

Fast Non-dominated Sorting in Multi Objective Genetic Algorithm for Bin Packing Problem

The bin packing problem is a problem where goods with different volumes and dimensions are put into a container so that the volume of goods inserted is maximized. The problem of multi-objective bin packing is a problem that is more commonly found in everyday life, because what is considered in packing is usually not only volume.In this research, a multi-objective genetic algorithm is proposed to solve the multi-objective bin packing problem. The proposed genetic algorithm uses non-dominated sorting and crowding distance methods to get the best solution for each objective and to avoid bias. The algorithm is then tested with several test classes that represent different combinations of item and container sizes.From the results of the tests carried out, it was found that the proposed algorithm can find several solutions which are the best candidate solutions for each objective. Also found how the correlation of each objective in the population