Application Research on the Maintainability Allocation Method of a Certain Shooter Seat

In view of the traditional maintainability allocation method for a certain shooter seat for maintainability allocation did not consider the lifecycle expense problem, the improved NSGA-II algorithm (iNSGA-II, for short) is adopted to establish a multiobjective comprehensive trade-off model for a certain shooter seat product lifecycle maintenance-related expenses and mean time to repair (MTTR, for short) and construct multiobjective optimization problem. The experimental results show that the Pareto optimal solution effectively solves the limitation of the traditional maintainability allocation method and then provides a basis for a certain shooter seat to obtain a reasonable maintainability allocation scheme. The superiority of the iNSGA-II algorithm to optimize the maintainability allocation of a certain shooter seat was verified by comparing it with the traditional maintainability allocation method.

Stability of Parametric Intuitionistic Fuzzy Multi-Objective Fractional Transportation Problem

In this study, a parametric intuitionistic fuzzy multi-objective fractional transportation problem (PIF-MOFTP) is proposed. The current PIF-MOFTP has a single-scalar parameter in the objective functions and an intuitionistic fuzzy supply and demand. Based on the (α,β)-cut concept a parametric (α,β)-MOFTP is established. Then, a fuzzy goal programming (FGP) approach is utilized to obtain (α,β)-Pareto optimal solution. We investigated the stability set of the first kind (SSFK) corresponding to the solution by extending the Kuhn-Tucker optimality conditions of multi-objective programming problems. An algorithm to crystalize the progressing SSFK for PIF-MOFTP as well as an illustrative numerical example is presented.

Hybrid Multi-objective Optimization Approach in Water Flooding

Non-dominated Sorting Genetic Algorithm, second version (NSGA-II) is used as a stochastic optimization technique successfully in different engineering applications. In this study, a data-driven optimization strategy based upon evolutionary neural network algorithm (EvoNN) is developed for providing input into NSGA-II optimization. Evolutionary neural-network data-driven model is built and trained using initial solutions generated by NSGA-II optimization coupled with the reservoir simulation model. Evolutionary optimization incorporated in the EvoNN strategy is applied in the trained data-driven model to generate the Pareto optimal solution, which is then used as a guiding input into NSGA-II optimization. The described method is applied in two case studies (i.e. Brugge field model & water injection pattern model). The Pareto optimal solutions obtained with data-driven model guided NSGA-II in both models show improvement in convergence and diversity of the solution. The convergence to the Pareto optimal solution has improved by 9% for case-1 (i.e. Brugge field) and by 43% for case-2 (i.e. water injection pattern model). In addition, the Pareto optimal solution obtained by the proposed hybridization has shown improvement in the water oil ratio (WOR) up to 6% in the Brugge field and up to 97% in the water injection pattern model. This improvement can lead to wide applications in using evolutionary optimizations in real field simulation models at acceptable computation time.

Pareto Effect of LMI for Ship Propulsion

The aim of this study was to analyze the dynamics of a multidimensional object based on the Pareto curve for the Linear Matrix Inequalities (LMI) controller. The study was carried out based on an available “Blue Lady” training vessel model controller with the use of a MATLAB and Simulink simulation package. Research was focused on optimising both the energy to be used when manoeuvring and the ship’s dynamics. Analysis was combined with the application of H2/H∞ norms finding the Pareto optimal solution for mixed norms used at the γ∞ parameter. Observations for a multidimensional ship model proved that it is possible to optimize the system, using principles of the Pareto curve, to reduce energy consumption in steering-propulsion systems while performing precise manoeuvres in ports correctly. Parameter values, received from observations of operation of individual steering-propulsion systems, proved to be reasonable.

Sea Surface Current Velocity Retrieving from TanDAM-X Satellite Data

This is the first investigation for the use of TanDEM-X data, satellite for the Malaysian coastal waters. This aims at utilizing an optimization of the Hopfield neural network to retrieve variation of sea surface current along Malaysian coastal waters. In doing so, a multi-objective evolutionary algorithm based on the Pareto front is used to minimize the error produced due to non-linearity between TanDEM-X data and sea surface movements. This work aimed at retrieving sea surface current from TanDEM-X data along the coastal waters of Malaysia. Two approaches have been implemented, the Hopfield neural network algorithm and Pareto optimal solution. The study shows that the Pareto optimal solution has a higher performance than the Hopfield neural network algorithm with a lower RMSE of ±0.009. Furthermore, a Pareto optimal solution can determine the sea surface current pattern variation along the coastal water from TanDEM-X data. In conclusion, TanDEM-X data shows an excellent promise for retrieving sea surface currents.

The effect of parallel execution on multi-site computation offloading in mobile cloud computing

The demand for running complex applications on smart mobile devices is rapidly increasing. However, the limitations of resources are restricting the development of intensive applications on these devices. The restrictions can be overcome by offloading the computation of an application in the powerful cloud servers. The objective of the computation offloading is to offload the parts of an application to the cloud server to minimize the response time, energy consumption and monetary cost of the application. Unlike prior work in computation offloading, this work considers the effect of parallel execution—on different devices (external parallelism) and on the different cores of a single device (internal parallelism). This work models each device as a multi-server queueing station. It uses genetic algorithm to determine the near-optimal offloading allocation. The results show that considering the effect of parallel execution yields better pareto-optimal solution for the allocation problem compared to excluding parallelism.

The effect of parallel execution on multi-site computation offloading in mobile cloud computing

The demand for running complex applications on smart mobile devices is rapidly increasing. However, the limitations of resources are restricting the development of intensive applications on these devices. The restrictions can be overcome by offloading the computation of an application in the powerful cloud servers. The objective of the computation offloading is to offload the parts of an application to the cloud server to minimize the response time, energy consumption and monetary cost of the application. Unlike prior work in computation offloading, this work considers the effect of parallel execution—on different devices (external parallelism) and on the different cores of a single device (internal parallelism). This work models each device as a multi-server queueing station. It uses genetic algorithm to determine the near-optimal offloading allocation. The results show that considering the effect of parallel execution yields better pareto-optimal solution for the allocation problem compared to excluding parallelism.

Multi-objective reference point based enriched swarm optimization with an application to blood supply chain under natural disaster

The application and analysis of effective blood supply chain network under natural disaster imposed many critical challenges which addressed through an optimization of multiple objectives functions. In this article, relies on reference point algorithm, a user-preference based enriched swarm optimization algorithm is proposed where, inner reference points were produced depending on the perturbed reference point. For each inner reference point, weakly/ɛ-properly Pareto optimal solution was generated using augmented achievement function. All the generated solutions (points) are presented as potential positions for particles in the particle swarm optimization PSO. The proposed algorithm has been reinforced with a novel chaotic contraction operator to retain the feasibility of the particles. To prove the validity of our algorithm, the obtained results are compared with true Pareto optimal front and three of the most salient evolutionary algorithms using inverted generational distance metric IGD. In addition it was implement to detect the most cost and time efficient blood supply chain to provide the required blood types demand on the blood transfusion center in emergence situation, where, it is required to solve this real life application with predefined supply time and predefined supply cost, which is considered as reference point to get the nearby Pareto optimal solution. By the experimental outcomes, we proved that the proposed algorithm is capable to find the set of Paetro optimal solutions nearby the predefined reference points.