Towards Many-objective Optimisation with Hyper-heuristics: Identifying Good Heuristics with Indicators

PPSN 2016: 14th International Conference on Parallel Problem Solving from Nature, 17-21 September 2016, Edinburgh, ScotlandThis is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.The use of hyper-heuristics is increasing in the multi-objective optimisation domain, and the next logical advance in such methods is to use them in the solution of many-objective problems. Such problems comprise four or more objectives and are known to present a significant challenge to standard dominance-based evolutionary algorithms. We in- corporate three comparison operators as alternatives to dominance and investigate their potential to optimise many-objective problems with a hyper-heuristic from the literature. We discover that the best results are obtained using either the favour relation or hypervolume, but conclude that changing the comparison operator alone will not allow for the generation of estimated Pareto fronts that are both close to and fully cover the true Pareto front.This work was funded under EPSRC grant EP/K000519/1

A software interface for supporting the application of data science to optimisation

Many real world problems can be solved effectively by metaheuristics in combination with neighbourhood search. However, implementing neighbourhood search for a particular problem domain can be time consuming and so it is important to get the most value from it. Hyper-heuristics aim to get such value by using a specific API such as `HyFlex' to cleanly separate the search control structure from the details of the domain. Here, we discuss various longer-term additions to the HyFlex interface that will allow much richer information exchange, and so enhance learning via data science techniques, but without losing domain independence of the search control

Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning

This study introduces a resource allocation framework for integrated satellite-terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG) to optimize user association, cache design, and transmission power control, resulting in enhanced energy efficiency. The approach comprises two phases: User Association and Power Control, where users are treated as agents, and Cache Optimization, where the satellite (Bs) is considered the agent. Through extensive simulations, we demonstrate that our approach surpasses conventional single-agent deep reinforcement learning algorithms in addressing cache design and resource allocation challenges in integrated terrestrial-satellite networks. Specifically, our proposed approach achieves significantly higher energy efficiency and reduced time delays compared to existing methods.Comment: 16, 1

Electrochemical generation and utilization of alkoxy radicals

This highlight summarises electrochemical approaches for the generation and utilization of alkoxy radicals, predominantly focusing on recent advances (2012–present). The application of electrochemically generated alkoxy radicals in a diverse range of transformations is described, including discussion on reaction mechanisms, scope and limitations, in addition to highlighting future challenges in this burgeoning area of sustainable synthesis

Deconstructive functionalization of unstrained cycloalkanols via electrochemically generated aromatic radical cations

Herein we report an electrochemical approach for the deconstructive functionalization of cycloalkanols, where various alcohols, carboxylic acids, and N-heterocycles are employed as nucleophiles. The method has been demonstrated across a broad range of cycloalkanol substrates, including various ring sizes and substituents, to access useful remotely functionalized ketone products (36 examples). The method was demonstrated on a gram scale via single-pass continuous flow, which exhibited increased productivity in relation to the batch process

Overview on Blood Transfusion-Transmitted Diseases

As it is important for the Blood transfusion to be extremely safe, some measures have to be taken long safeguarded the blood supply from the major transfusion transmissible diseases (TTIs).  The risk of transfusion-transmitted infection (TTI) rises with the number of donors exposed, and the effects of TTI are frequently more severe in immune compromised people. TTIs (hepatitis B virus [HBV], HIV, and hepatitis C virus [HCV]) are examples of typical transfusion-transmitted infectious agents. As a result of the gradual application of nucleic acid-amplification technology (NAT) screening for HIV, HCV, and HBV, the residual risk of infected window-period donations has been minimized. Nonetheless, infections emerge far more frequently than is commonly acknowledged, needing ongoing surveillance and individual assessment of transfusion-associated risk. Although there is a constant need to monitor present dangers owing to established TTI, the ongoing issues in blood safety are mostly related to surveillance for developing agents, as well as the creation of quick reaction systems when such agents are detected

Synthesis of 5-chloro-thiosulfadiazine Compounds Using Two-Phase Systems of Organic Solvents

We report a direct and simple protocol for the synthesis of 5-chloro-thiosulfadiazine compounds. This reaction has been achieved using two-phase systems of organic solvents (NaOAc/ H2O and Na2CO3/ H2O. The use of NaOAc or Na2CO3 for the preparation of chloro compounds provided all the advantages of cost, safety, and environmental concerns; thus, this method will give broad utility to the organic/medicinal chemist that is pursuing the synthesis of sulfonamides derivatives

A generality analysis of multiobjective hyper-heuristics

Selection hyper-heuristics have emerged as high level general-purpose search methodologies that mix and control a set of low-level (meta) heuristics. Previous empirical studies over a range of single objective optimisation problems have shown that the number and type of low-level (meta) heuristics used are influential to the performance of selection hyper-heuristics. In addition, move acceptance strategies play an important role and can significantly affect the overall performance of a hyper-heuristic. In this paper, we introduce an adapted variant of an existing learning automata based multiobjective hyper-heuristic from the literature. We investigate the performance and generality level of the proposed method, and another learning automata based selection hyper-heuristic, operating over a search space of multiobjective evolutionary algorithms (MOEAs) across two well-known multiobjective optimisation benchmarks. The experimental results demonstrate that, regardless of the number and type of low-level metaheuristics available, the learning automata based hyper-heuristics outperform each constituent MOEA individually, and an online learning and random choice selection hyper-heuristic from the literature. This performance and generality is shown to be consistent across a number of different move acceptance strategies

Minimizing energy consumption for NOMA multi-drone communications in automotive-industry 5.0

The forthcoming era of the automotive industry, known as Automotive-Industry 5.0, will leverage the latest advancements in 6G communications technology to enable reliable, computationally advanced, and energy-efficient exchange of data between diverse onboard sensors, drones and other vehicles. We propose a non-orthogonal multiple access (NOMA) multi-drone communications network in order to address the requirements of enormous connections, various quality of services (QoS), ultra-reliability, and low latency in upcoming sixth-generation (6G) drone communications. Through the use of a power optimization framework, one of our goals is to evaluate the energy efficiency of the system. In particular, we define a non-convex power optimization problem while considering the possibility of imperfect successive interference cancellation (SIC) detection. Therefore, the goal is to reduce the total energy consumption of NOMA drone communications while guaranteeing the lowest possible rate for wireless devices. We use a novel method based on iterative sequential quadratic programming (SQP) to get the best possible solution to the non-convex optimization problem so that we may move on to the next step and solve it. The standard OMA framework, the Karush–Kuhn–Tucker (KKT)-based NOMA framework, and the average power NOMA framework are compared with the newly proposed optimization framework. The results of the Monte Carlo simulation demonstrate the accuracy of our derivations. The results that have been presented also demonstrate that the optimization framework that has been proposed is superior to previous benchmark frameworks in terms of system-achievable energy efficiency

New Surface Aspects towards Photocatalytic Activity of Doped Supported Titanium Dioxide

The present work aims to synthesize nanoscale well dispersed TiO2/SiO2 and TiO2/Al2O3 nanoparticle photocatalysts via an impregnation method for the removal of methyl orange, which was used as a model compound of organic pollutant in wastewater, from an aqueous medium. Also within this frame work, La and Ce metals were loaded onto the surfaces of TiO2/SiO2 and TiO2/Al2O3 by an impregnation method to enhance the photocatalytic activity of the nanoparticles; the activities and physicochemical properties of the photocatalysts were compared before and after loading of metallic La and Ce. The oxide system was characterized by different techniques, including XRD, UV-Vis spectroscopy, FT-IR spectroscopy, SEM, and EDX spectroscopy. Finally, the optimal conditions to complete the photocatalytic oxidation of methyl orange dye were studied. This work holds promise for the efficient photodegradation of pollutants by nanoparticle photocatalysts