A multi-objective evolutionary optimization approach to procedural flight-noise mitigation

Explainable Interactive Evolutionary Multiobjective Optimization

This paper describes the development and application of a new multi-objective evolutionary optimization approach for the design and upgrading of water distribution systems with multiple pumps and service reservoirs. The optimization model employs a pressure-driven analysis simulator that accounts for the minimum node pressure constraints and conservation of mass and energy. Pump scheduling, tank siting and tank design are integrated seamlessly in the optimization without introducing additional heuristic procedures. The computational solution of the optimization problem is entirely penalty-free, thanks to pressure-driven analysis and the inclusion of explicit criteria for tank depletion and replenishment. The model was applied to the Anytown network that is a benchmark optimization problem. Many new solutions were achieved that are cheaper and offer superior performance compared to previous solutions in the literature. Detailed and extensive simulations of the solutions achieved were carried out. Spatial and temporal variations in water quality were investigated by simulating the chlorine residual and disinfection by-products in addition to water age. The hydraulic requirements were satisfied; efficiency of pumps was consistently high; effective operation of the new and existing tanks was achieved; water quality was improved; and overall computational efficiency was high. The formulation is entirely generic.

The huge bandwidth of optical fibres is exploited through wavelength division multiplexing technology, which introduces new complexities in the routing problem. In this context, the wavelength converter allocation problem has become a key factor to minimize blocking. The wavelength converter allocation problem has been treated as a mono-objective problem minimizing the number of wavelength converters or minimizing blocking; however, both criteria are in conflict with each other. Therefore, the wavelength converter allocation problem is studied here in a pure multi-objective optimization context for more appropriate decision making. This work proposes a multi-objective optimization approach based on an evolutionary algorithm which simultaneously minimizes blocking and the number of wavelength converters. Extensive simulations on three real optical networks show promising results in the sense that our algorithm generates the trade-off curve between blocking and the number of converters needed, and outperforms a recently proposed approach.

Optimum dimension of geometric parameters of solar chimney power plants – A multi-objective optimization approach

In this paper, we present a surrogate-based multi-objective evolutionary optimization approach to optimize airfoil aerodynamic designs. Our approach makes use of multiple surrogate models which operate in parallel with the aim of combining their features when solving a costly multi-objective optimization problem. The proposed approach is used to solve five multiobjective airfoil aerodynamic optimization problems. We compare the performance of a multi-objective evolutionary algorithm with surrogates with respect to the same approach without using surrogates. Our preliminary results indicate that our proposal can achieve a substantial reduction in the number of objective function evaluations, which has obvious advantages for dealing with expensive objective functions such as those involved in aeronautical optimization problems.

This paper presents an application of a constrained multiobjective evolutionary algorithm for the design of active suspension controllers for light rail vehicles with the aim of providing superior ride comfort within the suspension's stroke limitation. A multibody dynamic model of a three‐car train is derived and the control parameters are optimized. Force cancellation, skyhook damper, and track‐following are used to synthesize the active controller. Selection of the active suspension parameters is aided by an evolutionary computation algorithm to get the best compromise between ride quality and suspension deflections due to irregular gradient tracks. An evolutionary multiobjective optimization approach accompanied with the Pareto set is proposed to deal with the complicated control design problem.

Controller design is performed to meet some performance criteria. Some of them are the gain and phase margins, control effort, settling time, overshoot, amongst other indexes. Obtaining a controller or compensator that fits all these criteria (or some of them) for a closed loop control is done in both empirical and analytical procedures. The most of these procedures assumes the plant to be controlled could be modelled as first or second-order system. This paper aims to show a way to find the controller parameters based on the multi-objective optimization with a evolutionary approach. The systems to be controlled must be the type SISO (Single Input Single Output) whose transfer functions may have order higher than second order.

Radiation therapy treatment planning is inherently a multiobjective problem, aiming to obtain the best tradeoffs between irradiating the tumor with the prescribed dose and sparing as much as possible the surrounding healthy organs. Many different multiobjective approaches have been proposed for the optimization of radiation intensities for fixed beam irradiation directions. However, multiobjective beam angle optimization is seldom considered. The purpose of this paper is to introduce a new multiobjective optimization framework that explicitly and simultaneously considers the optimization of intensities and also beam directions. Whilst multiobjective optimization of radiation intensities considering a fixed set of beam directions gives rise to a single Pareto front, beam angle optimization gives rise to the appearance of multiple Pareto fronts, each one associated with a given beam angle set. Our framework proposes a beam angle set choice based on the evaluation of non-dominated solutions belonging to different Pareto fronts, using a tree-based approach and a performance indicator to assess the quality of each Pareto front. The proposed approach, illustrated by head-and-neck cancer cases, allows for more flexibility in the calculation of solutions and a better understanding of the existing compromises between different objectives.

Spiral groove is one of the most common types of structures on gas mechanical seals. Numerical research demonstrated that the grooves designed for improving gas film lift or film stiffness often lead to the leakage increase. Hence, a multi-objective optimization approach specially for conflicting objectives is utilized to optimize the spiral grooves for a specific sample in this study. First, the objectives and independent variables in multi-objective optimization are determined by single objective analysis. Then, a set of optimal parameters, i.e., Pareto-optimal set, is obtained. Each solution in this set can get the highest dimensionless gas film lift under a specific requirement of the dimensionless leakage rate. Finally, the collinearity diagnostics is performed to evaluate the importance of different independent variables in the optimization.

Determination of retirement points by using a multi-objective optimization to compromise the first and second life of electric vehicle batteries

A multi-objective optimization approach is often followed by an a posteriori decision-making process, during which the most appropriate solution of the Pareto set is selected by a professional in the field. Conventional visualization methods do not correct for Pareto fronts with irregularly-spaced solutions. However, achieving a uniform spread of solutions can make the decision-making process more intuitive when decision tools such as sliders, which represent the preference for each objective, are used. We propose a method that maps an m-dimensional Pareto front to an (m - 1)-simplex and spreads out points to achieve a more uniform distribution of these points in the simplex while maintaining the local neighborhood structure of the solutions as much as possible. This set of points can then more intuitively be navigated due to the more uniform distribution. We test our approach on a set of non-uniformly spaced 3D Pareto fronts of a real-world problem: deformable image registration of medical images. The results of these experiments are visualized as points in a triangle, showing that we indeed achieve a representation of the Pareto front with a near-uniform distribution of points where these are still positioned as expected, i.e., according to their quality in each of the objectives of interest.

Background: The idea behind the Internet of Things is to bring the virtual world into the physical one by connecting commonplace items. With the help of the Internet of Things (IoT), it is possible to remotely sense or control objects through preexisting network infrastructure. This opens up possibilities for computer-based systems to integrate with the physical world, which in turn improves efficiency, accuracy, and economic benefit while reducing the need for human intervention. Objective: The purpose of this patent study is to investigate how a (NSGA-II) multi-objective genetic algorithm might be utilized to optimize the execution of an Internet of Things (IoT) temperature monitoring Box-Type Solar Cooker (BTSC). To determine the best set of output parameters for an IoT temperature monitoring box-type solar cooker, (NSGA-II) multi-objective genetic algorithms are used to perform optimizations of the figure of merits (F2), cooking power, cooker efficiency, and final water temperature. Methods: The present research work involves the development of a Wi-Fi module system integrated with a smart temperature monitoring system for a BTSC. Keeping track of the temperature data from different locations in the BTSC through the IoT system was the primary objective of this project. A waterproof temperature sensor (DS18B20) was used to keep monitoring. After that, the data was shown on an LCD, stored on a microSD card, and made available through a smartphone. The Blynk Applications' IoT was employed. Using existing data, regression-based computational models are developed to describe the complex correlations between the decision-processing parameters and the input parameters of an IOT-based solar cooker. These models are applied in the objective functions after determining that a genetic algorithm is more appropriate for the problem. To forecast the optimal values about the figure of merits (F2), cooking power, cooker efficiency, and final water temperature, the Pareto fronts have been developed. Results: We compare the values of response variables that were gathered experimentally with the values that were predicted by NSGA-II. The predicted values are found to be quite close to experimental values. This indicates that the multi-objective optimization method, as used in this study, has very good prediction performance. The test results are graphically shown using the error bar. Therefore, it is clear that the optimization process used to adjust the parameters of the solar cooker's performance has been quite effective. According to the findings of the experiment, the temperature at which a cooking pot remained stagnant on average was 158°C. It was determined that the cooker was of class A based on the values of the first figure of merit (F1), the second figure of merit (F2), and the cooking power (P), which were respectively 0.132, 0.359, and 86.108 W. Therefore, the thermal efficiency of the IoT-base temperature monitoring box type solar cooker is 39.99 %. Conclusion: The findings of this inquiry furthermore produced the outcome that the model provided can be applied conveniently with a confidence level of 95% to calculate the figure of merits (F2), cooking power, cooker efficiency, and final water temperature value of an Internet of Things-based temperature monitoring BTSC. The performance of IoT-based BTSC is optimized by providing real- time monitoring and data visualization, ultimately improving their efficiency and reliability. This research provides an educational tool to promote awareness and understanding of renewable energy sources and their potential benefits.

Performance evaluation and multi-objective optimization of an innovative double-stage thermoelectric heat storage system for electricity generation

Municipalities across the United States face the challenge of sanitary sewer overflows (SSOs), events which pose serious public health and environmental problems. SSOs are unintentional discharges of untreated sewage from the sewer system that can occur as a result of rain derived infiltration and inflow (RDII). SSOs can be reduced by decreasing RDII into the collection system, increasing conveyance capacity, and lessening peak flow through detention storage facilities. However, the sheer length of sanitary sewer systems makes the implementation of these controls very costly. Thus, a novel approach is required to a posteriori design cost-effective rehabilitation options. This study describes a multiobjective evolutionary optimization approach to design a rehabilitation strategy for SSOs reduction in the eastern subsewershed of the San Antonio Water System (SAWS) sewer network. The subsewershed consists of 3,304 conduits connected via 3,155 manholes to form a network that is 160.8 miles long and services an area covering 20.4 square miles with an approximate population of 36,000 inhabitants. The hydraulic behavior of the system is simulated using the EPA Storm Water Management Model (EPA-SWMM) hydraulic model, and the number of SSOs is calculated as the number of nodes that oveflow during the 5 year–6 hours design storm. The optimization method utilized was the Nondominated Sorting Genetic Algorithm (NSGA II) to generate near Pareto-optimal solutions that express tradeoffs between the number of SSOs and cost. The NSGA II was implemented in JAVA, where each individual is represented as a set of vectors that is composed of three sets of genes. The decision variables include the number of pipe segments to be replaced, the location of the first link to be replaced, and the amount of commercial diameter increase from existing pipe diameter. The use of the optimization approach is expected to generate more efficient solutions in comparison to typical engineering approaches that look into solving a localized SSOs problems in sanitary sewer networks.

When solving multiobjective optimization problems, there is typically a decision maker (DM) who is responsible for determining the most preferred Pareto optimal solution based on his preferences. To gain confidence that the decisions to be made are the right ones for the DM, it is important to understand the trade-offs related to different Pareto optimal solutions. We first propose a trade-off analysis approach that can be connected to various multiobjective optimization methods utilizing a certain type of scalarization to produce Pareto optimal solutions. With this approach, the DM can conveniently learn about local trade-offs between the conflicting objectives and judge whether they are acceptable. The approach is based on an idea where the DM is able to make small changes in the components of a selected Pareto optimal objective vector. The resulting vector is treated as a reference point which is then projected to the tangent hyperplane of the Pareto optimal set located at the Pareto optimal solution selected. The obtained approximate Pareto optimal solutions can be used to study trade-off information. The approach is especially useful when trade-off analysis must be carried out without increasing computation workload. We demonstrate the usage of the approach through an academic example problem.