MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems

Many-objective optimization (MaO) deals with a large number of conflicting objectives in optimization problems to acquire a reliable set of appropriate non-dominated solutions near the true Pareto front, and for the same, a unique mechanism is essential. Numerous papers have reported multi-objective evolutionary algorithms to explain the absence of convergence and diversity variety in many-objective optimization problems. One of the most encouraging methodologies utilizes many reference points to segregate the solutions and guide the search procedure. The above-said methodology is integrated into the basic version of the Moth Flame Optimization (MFO) algorithm for the first time in this paper. The proposed Many-Objective Moth Flame Optimization (MaOMFO) utilizes a set of reference points progressively decided by the hunt procedure of the moth flame. It permits the calculation to combine with the Pareto front yet synchronize the decent variety of the Pareto front. MaOMFO is employed to solve a wide range of unconstrained and constrained benchmark functions and compared with other competitive algorithms, such as non-dominated sorting genetic algorithm, multi-objective evolutionary algorithm based on dominance and decomposition, and novel multi-objective particle swarm optimization using different performance metrics. The results demonstrate the superiority of the algorithm as a new many-objective algorithm for complex many-objective optimization problems

Marine dual fuel engines modelling and optimisation employing : a novel combustion characterisation method

Dual fuel (DF) engines have been an attractive alternative of traditional diesel engines for reducing both the environmental impact and operating cost. The major challenge of DF engine design is to deal with the performance-emissions trade-off via operating settings optimisation. Nevertheless, determining the optimal solution requires large amount of case studies, which could be both time-consuming and costly in cases where methods like engine test or Computational Fluid Dynamics (CFD) simulation are directly used to perform the optimisation. This study aims at developing a novel combustion characterisation method for marine DF engines based on the combined use of three-dimensional (3D) simulation and zero-dimensional/one-dimensional (0D/1D) simulation methods. The 3D model is developed with the CONVERGE software and validated by employing the measured pressure and emissions. Subsequently, the validated 3D model is used to perform a parametric study to explore the engine operating settings that allow simultaneous reduction of the brake specific fuel consumption (BSFC) and NOx emissions at three engine operation conditions (1457 r/min, 1629 r/min and 1800 r/min). Furthermore, the derived heat release rate (HRR) is employed to calibrate the 0D Wiebe combustion model by using Response Surface Methodology (RSM). A linear response model for the Wiebe combustion function parameters is proposed by considering each Wiebe parameter as a function of the pilot injection timing, equivalence ratio and natural gas mass. The 0D/1D model is established in the GT-ISE software and used to optimise the performance-emissions trade-off of the reference engine by employing the Nondominated Sorting Genetic Algorithm II (NSGA II). The obtained results provide a comprehensive insight on the impacts of the involved engine operating settings on in-cylinder combustion characteristics, engine performance and emissions of the investigated marine DF engine. By performing the settings optimisation at three engine operating points, settings that lead to reduced BSFC are identified, whilst the NOx emissions comply with the Tier III NOx emissions regulation. The proposed novel method is expected to support the combustion analysis and enhancement of marine DF engines during the design phase, whilst the derived optimal solution is expected to provide guidelines of DF engine management for reducing operating cost and environmental footprint.Dual fuel (DF) engines have been an attractive alternative of traditional diesel engines for reducing both the environmental impact and operating cost. The major challenge of DF engine design is to deal with the performance-emissions trade-off via operating settings optimisation. Nevertheless, determining the optimal solution requires large amount of case studies, which could be both time-consuming and costly in cases where methods like engine test or Computational Fluid Dynamics (CFD) simulation are directly used to perform the optimisation. This study aims at developing a novel combustion characterisation method for marine DF engines based on the combined use of three-dimensional (3D) simulation and zero-dimensional/one-dimensional (0D/1D) simulation methods. The 3D model is developed with the CONVERGE software and validated by employing the measured pressure and emissions. Subsequently, the validated 3D model is used to perform a parametric study to explore the engine operating settings that allow simultaneous reduction of the brake specific fuel consumption (BSFC) and NOx emissions at three engine operation conditions (1457 r/min, 1629 r/min and 1800 r/min). Furthermore, the derived heat release rate (HRR) is employed to calibrate the 0D Wiebe combustion model by using Response Surface Methodology (RSM). A linear response model for the Wiebe combustion function parameters is proposed by considering each Wiebe parameter as a function of the pilot injection timing, equivalence ratio and natural gas mass. The 0D/1D model is established in the GT-ISE software and used to optimise the performance-emissions trade-off of the reference engine by employing the Nondominated Sorting Genetic Algorithm II (NSGA II). The obtained results provide a comprehensive insight on the impacts of the involved engine operating settings on in-cylinder combustion characteristics, engine performance and emissions of the investigated marine DF engine. By performing the settings optimisation at three engine operating points, settings that lead to reduced BSFC are identified, whilst the NOx emissions comply with the Tier III NOx emissions regulation. The proposed novel method is expected to support the combustion analysis and enhancement of marine DF engines during the design phase, whilst the derived optimal solution is expected to provide guidelines of DF engine management for reducing operating cost and environmental footprint

A Step Toward Improving Healthcare Information Integration & Decision Support: Ontology, Sustainability and Resilience

The healthcare industry is a complex system with numerous stakeholders, including patients, providers, insurers, and government agencies. To improve healthcare quality and population well-being, there is a growing need to leverage data and IT (Information Technology) to support better decision-making. Healthcare information systems (HIS) are developed to store, process, and disseminate healthcare data. One of the main challenges with HIS is effectively managing the large amounts of data to support decision-making. This requires integrating data from disparate sources, such as electronic health records, clinical trials, and research databases. Ontology is one approach to address this challenge. However, understanding ontology in the healthcare domain is complex and difficult. Another challenge is to use HIS on scheduling and resource allocation in a sustainable and resilient way that meets multiple conflicting objectives. This is especially important in times of crisis when demand for resources may be high, and supply may be limited. This research thesis aims to explore ontology theory and develop a methodology for constructing HIS that can effectively support better decision-making in terms of scheduling and resource allocation while considering system resiliency and social sustainability. The objectives of the thesis are: (1) studying the theory of ontology in healthcare data and developing a deep model for constructing HIS; (2) advancing our understanding of healthcare system resiliency and social sustainability; (3) developing a methodology for scheduling with multi-objectives; and (4) developing a methodology for resource allocation with multi-objectives. The following conclusions can be drawn from the research results: (1) A data model for rich semantics and easy data integration can be created with a clearer definition of the scope and applicability of ontology; (2) A healthcare system's resilience and sustainability can be significantly increased by the suggested design principles; (3) Through careful consideration of both efficiency and patients' experiences and a novel optimization algorithm, a scheduling problem can be made more patient-accessible; (4) A systematic approach to evaluating efficiency, sustainability, and resilience enables the simultaneous optimization of all three criteria at the system design stage, leading to more efficient distributions of resources and locations for healthcare facilities. The contributions of the thesis can be summarized as follows. Scientifically, this thesis work has expanded our knowledge of ontology and data modelling, as well as our comprehension of the healthcare system's resilience and sustainability. Technologically or methodologically, the work has advanced the state of knowledge for system modelling and decision-making. Overall, this thesis examines the characteristics of healthcare systems from a system viewpoint. Three ideas in this thesis—the ontology-based data modelling approach, multi-objective optimization models, and the algorithms for solving the models—can be adapted and used to affect different aspects of disparate systems