Multi-Criteria Optimization Manipulator Trajectory Planning

It is common knowledge that the optimal values of the calibrated parameters of a rainfall-runoff model for one model response may not be the optimal values for another model response. Thus, it is highly desirable to derive a Pareto front or trade-off curve on which each point represents a set of optimal values satisfying the desirable accuracy levels of each of the model responses. This paper presents a new genetic algorithm (GA) based calibration scheme, accelerated convergence GA (ACGA), which generates a limited number of points on the Pareto front. A neural network (NN) is then trained to compliment ACGA in the derivation of other desired points on the Pareto front by mimicking the relationship between the ACGA-generated calibration parameters and the model responses. The calibration scheme, ACGA, is linked with HydroWorks and tested on a catchment in Singapore. Results show that ACGA is more efficient and effective in deriving the Pareto front compared to other established GA-based optimization techniques such as vector evaluated GA, multiobjective GA, and nondominated sorting GA. Verification of the trained NN forecaster indicates that the trained network reproduces ACGA generated points on the Pareto front accurately. Thus, ACGA-NN is a useful and reliable tool to generate additional points on the Pareto front.

We present an evolutionary approach to a difficult, multiobjective problem in groundwater quality management: how to pump-and-treat (PAT) contaminated groundwater to remove the most contaminant at the least cost. Although evolutionary multiobjective (EMO) techniques have been applied successfully to monitoring of groundwater quality and to containment of contaminated groundwater, our work is a first attempt to apply EMO to the long-term (ten year) remediation of contaminated water. We apply an improved version of the Niched Pareto GA (NPGA 2) to determine the pumping rates for up to fifteen fixed-location wells. The NPGA2 uses Pareto-rank-based tournament selection and criteria-space niching to find nondominated frontiers. With 15 well locations, the niched Pareto genetic algorithm is demonstrated to outperform both a single objective genetic algorithm (SGA) and enumerated random search (ERS) by generating a better tradeoff curve.

The two primary goals of a multi-objective evolutionary algorithm (MOEA) for solving multi-objective optimization problems are to search as much non-dominated solutions as possible towards the true Pareto front and to maintain diversity of Pareto optimal solutions along tradeoff curves. This study presents a new hybrid MOEA, the niched Pareto tabu search combined with genetic algorithm (NPTSGA), to find Pareto-optimal solutions to multi-objective optimization problems. The NPTSGA is developed on the thoughts of integrating genetic algorithm (GA) with the improved tabu search (TS) based MOEA, niched Pareto tabu search (NPTS). The proposed NPTSGA is then tested through a simple test example and compared with other two techniques, NPTS and niched Pareto genetic algorithm (NPGA). Computational results indicate that the proposed NPTSGA is an efficient and effective method for solving multi-objective problems, while keeping the balance between the intensification of non-domination to the true Pareto-optimal solutions (TPS) and the diversification of the near Pareto-optimal solutions (NPS) along the tradeoff curves.

A genetic algorithm was used to search for optimal future land use and transportation plans for a pair of high-growth cities. Hundreds of thousands of plans were considered. Constraints were imposed to insure adequate housing for future residents. Objectives included the minimization of traffic congestion, the minimization of costs, and the minimization of change from the status quo. The genetic algorithm provided planners and decision-makers with a rich set of optimal plans known as the Pareto set. An interactive computer tool was developed to assist decision-makers in scanning the information in the Pareto set as they “shop” for a plan.

A MEM Electric Field Sensor Optimization by Multi-Objective Niched Pareto Genetic Algorithm

Multi-objective optimization of the shell in autonomous intelligent argo profiling float

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.

This paper covers an investigation on the effects of diversity control in the search performances of single-objective and multi-objective genetic algorithms. The diversity control is achieved by means of eliminating duplicated individuals in the population and dictating the survival of non-elite individuals via either a deterministic or a stochastic selection scheme. In the case of single-objective genetic algorithm, onemax and royal road R 1 functions are used during benchmarking. In contrast, various multi-objective benchmark problems with specific characteristics are utilised in the case of multi-objective genetic algorithm. The results indicate that the use of diversity control with a correct parameter setting helps to prevent premature convergence in single-objective optimisation. Furthermore, the use of diversity control also promotes the emergence of multi-objective solutions that are close to the true Pareto optimal solutions while maintaining a uniform solution distribution along the Pareto front.

This paper proposes a new method of concurrent SOC and SOH estimation using a combination of recursive least square (RLS) algorithm and particle swarm optimization (PSO). The RLS algorithm is equipped with multiple fixed forgetting factors (MFFF) which are optimized by PSO. The performance of the hybrid RLS-PSO is compared with the similar RLS which is optimized by single objective genetic algorithms (SOGA) as well as multi-objectives genetic algorithm (MOGA). Open circuit voltage (OCV) is treated as a parameter to be estimated at the same timewith internal resistance. Urban Dynamometer Driving Schedule (UDDS) is used as the input data. Simulation results show that the hybrid RLS-PSO algorithm provides little better performance than the hybrid RLS-SOGA algorithm in terms of mean square error (MSE) and a number of iteration. On the other hand, MOGA provides Pareto front containing optimum solutions where a specific solution can be selected to have OCV MSE performance as good as PSO.

Many complex electromagnetic designs require a multi-objective approach for the optimization as in the case of array synthesis. In this class of problems, there exists an ideal set of solutions, named the Pareto front, over which no other solution of the population dominates. Multi-objective genetic algorithms (MOAs) performs similarly to conventional genetic algorithms except that, rather than providing only a single optimal solution, the MOAs lead toward the determination of a Pareto front. This set presents a range of solutions for which no one can simultaneously satisfy the required performance but represents a tradeoff of the design requirements. Our aim is to develop a tool for the synthesis of array working at multiple frequencies within a large frequency band, with constraints imposed to the radiation pattern in terms of half-power beam width (HPBW) and side lobe level (SLL). More in detail, we are interested in developing an efficient code for more than two objectives. Therefore a particular version of multi-objective genetic algorithm, called nondominated sorting genetic algorithm (NSGA-II) has been implemented to optimize the array factor on the horizontal plane of a V-shaped array at three different frequencies. Multi-objective

In order to ensure the normal operation of OBD (On Board Diagnostics) system, manufacturers are required to conduct self-examination and supervision of OBD system, which is the performance evaluation of production vehicles. Multi-objective GA (Genetic Algorithm) simulates the process of biological evolution, dealing with a population, and can generate a large number of non-inferior solutions in one optimization process, so it can search the approximate Pareto optimal solution set of multi-objective optimization problems. In this paper, a fault diagnosis model based on multi-objective GA is proposed to diagnose the faults of automobile engines. The main idea is to use advanced multi-objective GA NSGA-II to adjust the parameters of the production car performance. The simulation results show that the fault samples can be completely and accurately identified by the established model, and the identification rate reaches 91.27%. Therefore, the fault diagnosis model based on multi-objective GA proposed in this paper can be used for fault diagnosis of automobile engine.

Background: This article reviews recent patents pertaining to the technological achievements and shortcomings of aircraft Position Indicator and Actuator System (PIAS). Generally, most patents have concentrated more on design, while implementing various developed single or multiobjective algorithms to solve component or system optimal design problems such as those of PIAS. Thus, there is a noticeable lack of patent efforts on either developing new constraint-handling techniques especially for multi-objective optimization, or applying advance single-objective constrainthandling techniques to multi-objective optimization. Method: To address this, the authors of this article in there past work developed a multi-objective optimization algorithm called Genetic Pareto Set Identification Algorithm plus Different Sex (GPSIA+DS), which considers the feasible and infeasible solution in it optimization strategy. However, the application of GPSIA+DS to real world reliability engineering problem has not been validated. Result: Thus, in this work, GPSIA+DS ability to handle an aircraft PIAS optimization problem was presented. PIAS component failure rates were obtained via Fault Tree Analysis (FTA). PIAS cost and reliability were the study objective functions subject to weight constrains. The performance of GPSIA+DS for the test problem was investigated through a comparative study. Conclusion: In the comparative study GPSIA+DS was compared with fast-sort genetic algorithm plus constrained dominance principle (NSGA-II+CD). Although NSGA-II+DS out performed GPSIA+DS for this test problem, it is evident that GPSIA+DS is equally an efficient algorithm. Keywords: Aircraft system, constrain, failure rate, genetic algorithm, multi-objective optimization (MOO), pareto set, reliabilityAircraft system, constrain, failure rate, genetic algorithm, multi-objective optimization (MOO), pareto set, reliability

Based on the concept of multi-objective optimization, the requirement for the design of un-round cross-sectioned airframe and stealthy performance combines the Pareto methodology and Genetic Algorithm, by using the selected operators which are the reproduction, niche and the Pareto set filter. Niched Pareto Genetic Algorithm gives better distribution and also to improve the optimization efficiency of response surface method (RSM). Based on the N-S equations numerical method to calculate the aerodynamic performance and the application of physical theory of optics and diffraction to calculate the cross-sectional area of the scattering radar, it compromises and optimizes the objective of being non-cylindrical cross-sectioned and stealthy. Hence, optimized design results are obtained.

In multiobjective optimization problem, a substantial number of solutions generated along the Pareto front are unacceptable to the decision maker. In this paper, we propose a variation of the concept of Pareto dominance by using a penalty based Multi Objective Genetic Algorithm which attempts to obtain the nondominated set of Pareto solutions within the acceptable region for decision maker. A Relenting Factor is used to incorporate decision maker's opinion corresponding to the aspiration for respective objective function. The proposed method is evaluated on standard benchmark test problems DTLZ1 and DTLZ2 with 2 and 3 objectives each. Results indicate that within certain limitations, the proposed method is able to converge to the true Pareto front as well as limit the solutions to region of interest to the Decision Maker.

It is well understood that binary classifiers have two implicit objective functions (sensitivity and specificity) describing their performance. Traditional methods of classifier training attempt to combine these two objective functions (or two analogous class performance measures) into one so that conventional scalar optimization techniques can be utilized. This involves incorporating a priori information into the aggregation method so that the resulting performance of the classifier is satisfactory for the task at hand. We have investigated the use of a niched Pareto multiobjective genetic algorithm (GA) for classifier optimization. With niched Pareto GA's, an objective vector is optimized instead of a scalar function, eliminating the need to aggregate classification objective functions. The niched Pareto GA returns a set of optimal solutions that are equivalent in the absence of any information regarding the preferences of the objectives. The a priori knowledge that was used for aggregating the objective functions in conventional classifier training can instead be applied post-optimization to select from one of the series of solutions returned from the multiobjective genetic optimization. We have applied this technique to train a linear classifier and an artificial neural network (ANN), using simulated datasets. The performances of the solutions returned from the multiobjective genetic optimization represent a series of optimal (sensitivity, specificity) pairs, which can be thought of as operating points on a receiver operating characteristic (ROC) curve. All possible ROC curves for a given dataset and classifier are less than or equal to the ROC curve generated by the niched Pareto genetic optimization.