DATA REPLICATION IN DISTRIBUTED SYSTEMS USING OLYMPIAD OPTIMIZATION ALGORITHM

Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments

Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition

© 2019 Elsevier Ltd This paper proposes a novel bio-inspired optimization method named memetic salp swarm algorithm (MSSA). It is developed by extending the original salp swarm algorithm (SSA) with multiple independent salp chains, thus it can implement a wider exploration and a deeper exploitation under the memetic computing framework. In order to enhance the convergence stability, a virtual population based regroup operation is used for the global coordination between different salp chains. Due to partial shading condition (PSC) and fast time-varying weather conditions, photovoltaic (PV) systems may not be able to generate the global maximum power. Hence, MSSA is applied for an effective and efficient maximum power point tracking (MPPT) of PV systems under PSC. To evaluate the MPPT performance of the proposed algorithm, four case studies are undertaken using Matlab/Simulink, e.g., start-up test, step change of solar irradiation, ramp change of solar irradiation and temperature, and field atmospheric data of Hong Kong. The obtained PV system responses are compared to that of eight existing MPPT algorithms, such as incremental conductance (INC), genetic algorithm (GA), particle swarm optimization (PSO), artificial bees colony (ABC), cuckoo search algorithm (CSA), grey wolf optimizer (GWO), SSA, and teaching-learning-based optimization (TLBO), respectively. Simulation results demonstrate that the output energy generated by MSSA in Spring in HongKong is 118.57%, 100.73%, 100.96%, 100.87%, 101.35%, 100.36%, 100.81%, and 100.22% to that of INC, GA, PSO, ABC, CSA, GWO, SSA, and TLBO, respectively. Lastly, a hardware-in-the-loop (HIL) experiment using dSpace platform is undertaken to further validate the implementation feasibility of MSSA

Artificial Intelligence for autonomous persona generation to shape tailored communications and products and incentivise disaster preparation behaviours

Elizabeth Ditton investigated whether machine learning, specifically clustering algorithms, could be used to mimic expert decision making used for targeted disaster preparation messaging. She found that clustering algorithms could be used to develop personas that achieve the same level of depth and nuance as manually developed personas, without the required resources

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

Time-Cost-Quality Trade-off Model for Optimal Pile Type Selection Using Discrete Particle Swarm Optimization Algorithm

The cost of pile foundations is part of the super structure cost, and it became necessary to reduce this cost by studying the pile types then decision-making in the selection of the optimal pile type in terms of cost and time of production and quality .So The main objective of this study is to solve the time–cost–quality trade-off (TCQT) problem by finding an optimal pile type with the target of "minimizing" cost and time while "maximizing" quality. There are many types In the world of piles but  in this paper, the researcher proposed five pile types, one of them is not a traditional, and   developed a model for the problem and then employed particle swarm optimization (PSO) algorithm, as one of evolutionary algorithms with the help of (Mat lab software), as a tool for decision making problem about choosing the best alternative of the traded piles, and proposes a multi objective optimization model, which aims to optimize the time, cost and quality of the pile types, and assist in selecting the most appropriate pile types. The researcher selected 10 of senior engineers to conduct interviews with them.  And prepared some questions for interviews and open questionnaire. The individuals are selected from private and state sectors each one have 10 years or more experience in pile foundations work. From personal interviews and field survey the research has shown that most of the experts, engineers are not fully aware of new soft wear techniques to helps them in choosing alternatives, despite their belief in the usefulness of using modern technology and software. The Problem is multi objective optimization problem, so after running the PSO algorithm it is usual to have more than one optimal solution, for five proposed pile types, finally the researcher  evaluated and  discussed the output results and  found out that pre-high tension spun (PHC)pile type was the optimal pile type

Smart Health and Safety Equipment Monitoring System for Distributed Workplaces

© 2019 by the authors. This paper presents a design and prototype of an IoT-based health and safety monitoring system using MATLAB GUI. This system, which is called the Smart Health and Safety Monitoring System, is aimed at reducing the time, cost and manpower requirements of distributed workplaces. The proposed system is a real-time control and monitoring system that can access on-line the status of consumable devices in the workplace via the internet and prioritise the critically high location that need replenishing. The system dynamically updates the status of all location, such as first aid boxes, earplug dispensers and fire extinguishers. Simulation results of the proposed system gives shorter path, time and cost in comparison to manual maintenance systems.Brunel University London, Electronic & Computer Engineering Departmen

Overview of Multi-Objective Optimization Approaches in Construction Project Management

The difficulties that are met in construction projects include budget issues, contractual time constraints, complying with sustainability rating systems, meeting local building codes, and achieving the desired quality level, to name but a few. Construction researchers have proposed and construction practitioners have used optimization strategies to meet various objectives over the years. They started out by optimizing one objective at a time (e.g., minimizing construction cost) while disregarding others. Because the objectives of construction projects often conflict with each other, single-objective optimization does not offer practical solutions as optimizing one objective would often adversely affect the other objectives that are not being optimized. They then experimented with multi-objective optimization. The many multi-objective optimization approaches that they used have their own advantages and drawbacks when used in some scenarios with different sets of objectives. In this chapter, a review is presented of 16 multi-objective optimization approaches used in 55 research studies performed in the construction industry and that were published in the period 2012–2016. The discussion highlights the strengths and weaknesses of these approaches when used in different scenarios

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure