Mono-objective Function Research Articles

Optimization of system of Oscillating Water Columns to mitigate pitch response of FPSO platforms submitted to head waves

This work presents a novel method for reducing the pitch response of FPSO (Floating, Production, Storage and Offloading) platforms submitted to head waves. The method is based on installing a couple of moonpools, modeled as opened Oscillating Water Columns (OWC), on the vessel. According to detailed literature review, the effects of the vortex shedding can be represented through a nonlinear damping factor, which is proportional to the relative motion between the vessel and the water column within the moonpool.Optimization solvers are adopted to design the moonpools, using potential theory to calculate the vessel response with an iterative procedure to properly account for the nonlinear viscous damping factor related to the vortex shedding.The use of metaheuristic optimization method is discussed, with the choice of Particle Swarm Optimization. Different mono-objective functions are explored by weighing the minimization of the peak response and the area underneath the response curve. The importance of imposing restrictions on the modified vessels to avoid increasing heave motion is presented, using the penalty function also by weighing the peak response and the area. Results show the effectiveness of the procedures proposed in the study and indicate the possibility of expanding to implement it in other applications.

Development of a novel hybrid haptic (nHH) device with a remote center of rotation dedicated to laparoscopic surgery

AbstractThis paper focuses on developing a novel hybrid-haptic (nHH) device with a remote center of rotation with 4 DOFs (degrees of freedom) intendant to be used as a haptic device. The new architecture is composed of two chains handling each one a part of the motions. It has the advantages of a parallel robot as high stiffness and accuracy, and the large workspace of the serial robots. The optimal synthesis of the nHH was performed using real-coded genetic algorithms. The optimization criteria and constraints were established and successively formulated and solved using a mono-objective function. A validation and comparison study were performed between the spherical parallel manipulator and the nHH. The obtained results are promising since the nHH is compared to other similar task devices, such as spherical parallel manipulator, and presents a suitable kinematic performance with a task workspace free singularity inside.

Efficient Multi-objective Optimizers by Meta-heuristics for Power System Control

This paper proposes the Meta-heuristics approaches using genetic algorithms (GA) and particle swarm optimization (PSO) for tuning power system stabilizer PSS parameters. In this work we have proposed a multi-objective function based on two objectives: first maximize the stability margin by increasing the damping factors and second minimize the eigenvalues real parts. For the effectiveness function proposed check, we compared it with mono-objective function. The simulation results, by comparative study between genetic algorithms and particle swarm optimizations techniques via multi objective and mono objective functions proved the efficiency of the PSS adapted by multi-objective function based genetic algorithms in comparison with particle swarm optimization, it’s enhanced stability of power system works under different operating modes and different network configurations. The simulation results obtained under developed graphical user interface (GUI).

A Discrete-Continuous PSO for the Optimal Integration of D-STATCOMs into Electrical Distribution Systems by Considering Annual Power Loss and Investment Costs

Currently, with the quick increase in global population, the energetic crisis, the environmental problematic, and the development of the power electronic devices generated the need to include new technologies for supporting and potentiating electrical distributions systems; Distribution Static Compensators (D-STATCOMs) are highly used for this task due to the advantages that this technology presents: reduction in power loss, operation costs, and chargeability of branches, among others. The possibility to include this kind of technology within the electrical system has shown the need to develop efficient methodologies from the point of view of quality solution, repeatability and processing times by considering operation and investment costs as well as the technical conditions of the electrical grids under a scenario of variable power demand and then representing the real operation of the electrical grid. With the aim to propose a solution for this requirement, this paper presents a new Discrete-Continuous Particle Swarm Optimization (DCPSO) algorithm to solve the problem of the optimal integration of D-STATCOMs into Electrical Distribution Systems (EDSs). In this case, the objective function is the minimization of annual operating costs by using a weighted mono-objective function composed of the annual power loss and the investment cost and by including all constraints associated with the operation of an EDS in a distributed reactive compensation environmentinside the mathematical formulation. In order to evaluate the effectiveness and robustness of the proposed solution method, this study implemented two tests systems (i.e., 33- and 69-bus), as well as four comparison methods, and different considerations related to the inclusion of D-STATCOMs in the EDSs. Furthermore, for evaluating the repeatability of the solution obtained by each solution methods used, each algorithm was executed 100 times in Matlab software. The results obtained demonstrated that the proposed DCPSO/HSA methodology achieved the best trade-off between solution quality and processing time, with low standard deviation values for EDSs of any size.

Multiobjective Optimization Techniques Applied to Three-Phase Transformers Designs

The aim of this research is to present the studies carried out to design three-phase core type distribution transformers, with the aid of optimization techniques. The minimization of losses is presented in two steps, first through the use of a mono-objective function, and second with the use of multi-objective function to minimize losses and the total mass of the active part of the transformer. The algorithms used are: Differential Evolution and Particle Swarm Optimization, their performances are compared through the results obtained. The main project parameters, such as core dimensions, total losses, no load current and the energizing current are estimated analytically through OCTAVE software. The analysis of magnetic flux density in the core is simulated using the Finite Element Method. Multiobjective optimization allows working with two or more conflicting objectives, and at each iteration, it stores the various non-dominant Pareto Front solutions, helping designers to choose the solution that best meets their needs. The results obtained with the mono-objective and multiobjective optimization techniques were interesting to minimize the losses and/or cost of the project

On applicability of MQ-RPIM and MLPG meshless methods with 3D extended-enriched base functions for estimation of mode I stress intensity factor and fatigue crack growth in cyclic tensile and bending load of an un-notched and notched shaft

In this study, a numerical meshless method is used to solve the weak form of the linear elastic equations in solid mechanics. Evaluation and comparison of the numerical meshless methods have been carried out via the radial point interpolation meshless method with multi-quadrics base functions (MQ-RPIM) and meshless local Petrov-Galerkin method (MLPG). Using these two methods, stress intensity factors in an elastic medium containing geometric discontinuities and cracks are estimated based on tensile and bending cyclic loading. The analysis domain has been identified via three-dimensional modeling of the notched and un-notched shafts with an initial surface semi-elliptical crack subjected to tensile or bending cyclic loadings. To enhance the accuracy of calculations, the RPIM meshless method is applied using polynomial and extended-enriched 3D base functions. Shape functions have been developed using standard and optimal parameters and values with Mono-Objective Function in PSO algorithm. In the MLPG meshless method with the extended-enriched functions, discretization is performed via direct and penalty factor methods, to reach more efficient results and meet the boundary conditions. Efficiency comparison of the selected numerical methods with the experimental findings and the numerical analysis of finite elements method indicates that in comparison with the MLPG method, MQ-RPIM enriched meshless method can be utilized with fewer nodes in the analysis domain while reaching the accuracy and convergence with lower stress intensity factors and gentler slope. However, the processing time of the MLPG meshless method is lower than that of the other methods.

On the Optimal Synthesis of a Finger Rehabilitation Slider-Crank-Based Device with a Prescribed Real Trajectory: Motion Specifications and Design Process

This article discusses the mechanical redesign of a finger rehabilitation device based on a slider-crank mechanism. The redesign proposal is to obtain a smaller and more portable device that can recreate the motion trajectories of a finger. The real finger motion trajectories were recorded using a motion capture system. The article focused on the optimal synthesis of the rehabilitation device mechanism formulated as a classic trajectory generation problem. The proposed approach was combined with the recorded finger movements and solved using the genetic algorithm (GA) method. Optimization criteria and constraints were successively formulated and solved using a mono-objective function.

Sizing and implementing off-grid stand-alone photovoltaic/battery systems based on multi-objective optimization and techno-economic (MADE) analysis

Abstract Sun energy is widely utilized to power stand-alone photovoltaic systems (SAPV). However, the lack of long term hourly meteorological data and inaccurate methods result in obstacles in designing the SAPV system. Therefore, an optimal sizing methodology is necessary to guarantee satisfactory performance. In this study, a new mutation adaptive differential evolution (MADE) based on a multi-objective optimization algorithm is presented to optimize the configuration of the off-grid SAPV system. Three conflict objectives are normalized, weighted, and then aggregated by mono-objective function to optimally size the off-grid stand-alone PV system. The performance of the proposed SAPV system is analyzed based on three types of batteries which are lead-acid, AGM, and lithium-ion. The results show that the optimal configuration based on lead-acid has less fitness function ( f ), life cycle cost (LCC), levelized cost of energy (LCE), with a high level of loss of load probability (LLP), and their values are 0.0268, 54,896 USD, 1.58USD, and 0.19%, respectively. Moreover, the optimal numbers of the PV modules (in series and parallel) and lead-acid battery are 9, 28, and 42, respectively. Therefore, the SAPV system based on lead-acid battery can be very suitable for real-world applications.

The pooling of sustainable freight transport

Logistics pooling presents a very important solution for companies to improve the different aspects of sustainability. The objective of this study is to propose a mathematical approach to the pooling of sustainable freight transport. In this work, the pooling performance is evaluated at different levels of sustainability. The proposed MILP (Mixed Integer Linear Programming) consists of designing a shared logistics chain, taking into account economic and environmental objectives, namely the different logistics cost and the different CO2 emissions. The social/societal level, namely the accident rates and the noise level are assessed after model resolving. Also, the transportation fill rate is calculated. Finally, we demonstrated the pooling performance with an illustrative example of freight transport, which is made up of four suppliers who collaborate to deliver six common distribution centers. Firstly, we solve the mathematical model by the mono-objective functions (economic or environmental). Secondly, we solved the model with a multi-objective function, which combines the two objectives (economic and environmental) with the Ɛ-Constraint method.

Improved analog/RF performance of double gate junctionless MOSFET using both gate material engineering and drain/source extensions

In this paper, we propose a new Double Gate Junctionless (DGJ) MOSFET design based on both gate material engineering and drain/source extensions. Analytical models for the long channel device associated to the drain current, analog and radio-frequency (RF) performance parameters are developed incorporating the impact of dual-material gate engineering and two highly doped extension regions on the analog/RF performance of DGJ MOSFET. The transistor performance figures-of-merit (FoM), governing the analog/RF behavior, have also been analyzed. The analog/RF performance is compared between the proposed design and a conventional DGJ MOSFET of similar dimensions, where the proposed device shows excellent ability in improving the analog/RF performance and provides higher drain current and improved figures-of-merit as compared to the conventional DGJ MOSFET. The obtained results have been validated against the data obtained from TCAD software for a wide range of design parameters. Moreover, the developed analytical models are used as mono-objective function to optimize the device analog/RF performance using Genetic Algorithms (GAs). In comparison with the reported numerical data for Inversion-Mode (IM) DG MOSFET, our optimized performance metrics for JL device exhibit enhancement over the reported data for IM device at the same channel length.

Mono-Objective Lyapunov Function Analysis Using a Fixed-Local-Optimal Policy

In this paper we propose an evolutionary technique based in a Lyapunov method (instead of Pareto) for mono-objective optimization, that associate to every Markov-ergodic process a Lyapunov-like mono-objective function. We show that for a class of controllable finite Markov Chains supplied by a given objective-function the system and the trajectory dynamics converge. For representing the trajectory-dynamics properties local-optimal policies are defined to minimize the one-step decrement of the cost-function. We propose a non-converging state-value function that increase and decrease between states of the decision process. Then, we show that a Lyapunov mono-objective function, which can only decrease (or remain the same) over time, can be built for this Markov decision processes. The Lyapunov mono-objective functions analyzed in this paper represent the most frequent type of behavior applied in practice in problems of evolutionary and real coded genetic algorithms considered within the Artificial Intelligence research area. They are naturally related with the, so-called, fixed-local-optimal actions or, in other words, with one-step ahead optimization algorithms widely used in the modern optimization theory. For illustration purposes, we present a simulated experiment that shows the trueness of the suggested method.

Turbine Blade Cooling System Optimization

Designing high performance cooling systems suitable for preserving the service lifetime of nozzle guide vanes of turboshaft engines leads to significant aerodynamic losses. These losses jeopardize the performance of the whole engine. In the same time, a low efficiency cooling system may affect the costs of maintenance repair and overhaul of the engine as component life decreases. Consequently, designing cooling systems of gas turbine vanes is related to a multiobjective design problem. In this paper, it is addressed by investigating the functioning of a blade and optimizing its design by means of an evolutionary algorithm. Systematic 3D CFD simulations are performed to solve the aero-thermal problem. Then, the initial multiobjective problem is solved by aggregating the multiple design objectives into one single relevant and balanced mono-objective function; two different types of mono-objective functions are proposed and compared. This paper also proposes to enhance available knowledge in the literature of cooling systems of gas turbine vanes by simulating the internal cooling system of the vane. From simulations thermal efficiency and aerodynamic losses are compared and their respective influences on the global performances of the whole engine are investigated. Finally, several optimal designs are proposed.

Methods for multi-objective investment and operating optimization of complex energy systems

The design and operations of energy systems are key issues for matching energy supply and consumption. Several optimization methods based on the mixed integer linear programming (MILP) have been developed for this purpose. However, due to uncertainty of some parameters like market conditions and resource availability, analyzing only one optimal solution with mono objective function is not sufficient for sizing the energy system.In this study, a multi-period energy system optimization (ESO) model with a mono objective function is first explained. The model is then developed in a multi-objective optimization perspective to systematically generate a good set of solutions by using integer cut constraints (ICC) algorithm and ε constraint. These two methods are discussed and compared.In the next step, the ESO model is reformulated as a multi-objective optimization model with an evolutionary algorithm (EMOO). In this step the model is decomposed into master and slave optimization.Finally developed models are demonstrated by means of a case study comprising six types of conversion technologies, namely, a heat pump, boiler, photovoltaics, as well as a gas turbine, fuel cell and gas engine. Results show that, EMOO is particularly suited for multi-objective optimizations, working with a population of potential solutions, each presenting a different trade-off between objectives. However, MILP with ICC and ε constraint is more suited for generating a small set of ordered solutions with shorter resolution time.

Heuristics for an industrial car sequencing problem considering paint and assembly shop objectives

The aim of this paper is to study the problem of sequencing a set of vehicles within an industrial environment considering the assembly shop objectives, but also the objectives of the paint shop. The first approach is to solve the problem with a mono-objective function. One heuristic (a progressive, construction-sequence algorithm) and three meta-heuristics (simulated annealing, variable neighbourhood search and an evolutionary algorithm) are described and compared. As the mono-objective approach has limited possibilities, a multi-objective heuristic is finally presented and tested. Because of the industrial context of this research, the computation time is a decisive factor to select the appropriate heuristic.