Optimization of the Calibration for an Internal Combustion Engine Management System Using Multi-Objective Genetic Algorithms

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

This paper investigates UAV conceptual design using optimization methods. The conceptual design of a Medium Altitude Long Endurance (MALE) UAV is formulated as two different optimization problems. The optimization problems use a single objective and dual objective functions. For the single objective case, endurance maximization of the UAV is taken as the objective function with aspect ratio and wing loading as design variables. Wing weight, stall speed, maximum speed and rate of climb are taken as constraints. In the dual objective case, minimizing weight is removed from the constraint set and added as the second objective and four more design variables namely taper ratio, thickness to chord ratio, loiter velocity and loiter altitude are added. In the single objective case, both conventional optimization algorithm and genetic algorithm are used to solve the problem and identical results are obtained. A newly developed multi-objective evolutionary algorithm named NSGA-II (non-dominated sorting genetic algorithm) is used to capture the full Pareto front for the dual objective problem. For the dual objective case, numerical results show that several useful Pareto optimal designs exist for the conceptual design of UAV. These Pareto points are thoroughly analyzed for use in detailed design.

Gene selection from microarray gene expression datasets and clustering of samples into different groups are important data mining tasks for disease identification. Selection of more interpretable genes from the gene expression dataset is an essential data-preprocessing task, which helps to study on cancer diseases. Gene selection during sample clustering is inherently a difficult task as there is no obvious criterion to guide the search. Simultaneous gene selection and sample clustering is a two-way data analysis technique which has recently gained attention in research area. The traditional clustering techniques are unable to handle noisy data properly. So, effective clustering algorithms are more desirable which can deal with the relevant and noise free data. Therefore, target genes selection before sample clustering is essential and of course effective if both the tasks are done simultaneously. In this chapter, optimal gene subset is selected and sample clustering is performed simultaneously using Multi-Objective Genetic Algorithm (MOGA). Different versions of MOGA are employed to choose the optimal gene subset, where natural number of optimal clusters of samples is automatically obtained at the end of the process. Non-dominated sorting genetic algorithm (NSGA), Strength pareto evolutionary algorithm (SPEA) and its modified version SPEA2 are applied for the purpose. The methods use nonlinear hybrid uniform cellular automata for generating initial population, tournament selection strategy, two-point crossover operation, and a suitable jumping gene mutation mechanism to maintain diversity in the population. It uses mutual correlation coefficient; internal and external cluster validation indices as objective functions to find out the non-dominated solutions. To measure the cluster validation indices, clustering algorithm is applied on data subset associated to chromosomes in the population to find out different clusters. After the convergence of genetic algorithm, the best solution from the non-dominated solutions is identified that provides the important genes and categorizes the samples into clusters. The experimental results express the correctness of the proposed simultaneous gene selection and sample categorization method. The goodness of optimality of the clusters obtained using different genetic algorithms is expressed by comparing various cluster validation indices.

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.

Sustainability encompasses the interaction between environmental, economic, and social systems at various levels. In transportation systems, vehicles rely on different fuels to meet energy demands. However, these fuels contribute to environmental pollution by releasing greenhouse gases. Considering this context, the present study investigates a sustainable fixed-charge multi-objective multi-driver multi-route multi-conveyance shortest path problem (FCMOMDMRMCSPP). The primary intent is to identify the most efficient route that minimizes cost, time, risk, carbon emissions, and distance while traveling between sources and destinations. Real-world scenarios often involve uncertainties, making it difficult to precisely define the parameters of FCMOMDMRMCSPP. Triangular fuzzy numbers are introduced in the proposed model to address this ambiguity. The possibilistic programming approach is employed to transform the fuzzy problem into a deterministic one. The resulting deterministic model is then solved using two multi-objective genetic algorithms (MOGAs): aspiration level (AL)-based non-dominated sorting genetic algorithm (NSGA)-II and NSGA-III. An aquatic fish haul system problem has been examined as an application within the framework of the proposed model. For α = 0 and 0.1, the AL-based NSGA-II and III generate 6, 11, and 4 solutions that satisfy the decision-maker’s (DM’s) ALs in cases I, II, and III of the AL and shape parameter combination, respectively. Similarly, for α = 0.5, these methods yield 5, 11, and 4 solutions, while for α = 0.9, they provide 6, 11, and 4 solutions for the same cases. A comparative analysis is conducted between the compromise solutions obtained from the proposed techniques and those derived from the hybrid genetic algorithm, NSGA-II and NSGA-III. A sensitivity analysis of the objective functions concerning the shape parameter and AL is also performed. Finally, the coverage performance measure is computed to assess the effectiveness of the proposed methods.

Non-dominated sorting genetic algorithm has shown excellent advantages in solving complicated optimization problems with discrete variables in a variety of domains. In this paper, we implement a multi-objective genetic algorithm to guide the design of the laminated structure with two objectives: minimizing the mass and maximizing the strength of a specified structure simultaneously, classical lamination theory and failure theory are adopted to compute the strength of a laminate. The simulation results have shown that a non-dominated genetic algorithm has great advantages in the design of laminated composite material. Experiment results also suggest that optimal run times are from 16 to 32 for the design of glass-epoxy laminate with non-dominated sorting genetic algorithm. We also observed that two stages involve the optimization process in which the number of individuals in the first frontier first increases, and then decreases. These simulation results are helpful to decide the proper run times of genetic algorithms for glass-epoxy design and reduce computation costs.

Robust H∞ Controller Design for Aircraft Lateral Dynamics using Multi-objective Optimization and Genetic Algorithms

<div class="section abstract"><div class="htmlview paragraph">Exhaust Noise attenuation is one of the important functions of exhaust muffler. Transmission Loss (TL) is a measure of noise attenuation used in designing exhaust mufflers for NVH. TL is a logarithmic difference between inlet and outlet pressures for unit velocity input at inlet of the muffler and anechoic termination at outlet of the muffler as boundary conditions. TL amplitude and its frequency tuning depends on a combination of various muffler design parameters like volume, length, muffler cross section, pipe cross sections, pipe perforations, number of chambers, baffle perforations, etc. Achieving the desired TL performance with no valleys over a wide frequency range is very challenging. Manual design iterations with large numbers of permutations and combinations of design variables are difficult and time-consuming. It also needs a highly experienced professional to balance TL performance, design variables and design constraints. The current paper discusses an exhaust muffler TL optimization simulation process that couples modeFRONTIER for DOEs, & GT-POWER, for acoustic simulation. All identified design variables are iterated in batch mode within specified design limits. DOEs are set up using Non-dominated Sorting Genetic Algorithm (NSGA) or Multi Objective Genetic Algorithm (MOGA) in modeFRONTIER. Based upon pre-defined iterative simulation cycles, muffler design is optimized to meet design constraints and TL performance. This optimization helps to reduce manual efforts in building simulation models, to carry out more iterations, to reduce solver time, to reduce manual intervention for post processing, and to give optimized TL performance.</div></div>

This paper investigates the problem of dynamic subcarrier and bit allocation in downlink of Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiple Access (OFDMA) Systems. Using Singular Value Decomposition, the MIMO fading channel of each subcarrier is transformed into an equivalent bank of parallel Single Input Single Output sub-channels. To achieve the capacity bound, one must solve a multiuser subcarrier allocation and the optimal bit allocation jointly. To alleviate the computational complexity of joint subcarrier and bit allocation, several suboptimal solutions have been proposed. These suboptimal solutions handle subcarrier and bits individually. We propose the use of Non-dominated Sorting Genetic Algorithm (NSGA)-II, which is a multi-objective Genetic Algorithm, for joint allocation of bits and subcarriers, in the downlink of MIMO-OFDMA system. NSGA-II is intended for optimization problems involving multiple conflicting objectives. Here the two conflicting objectives are Rate Maximization and Transmit Power Minimization. The simulation results indicate remarkable improvement in terms of convergence over previous approaches involving Evolutionary algorithms. At the same time capacity achieved by the proposed algorithm is found to be comparable with that of previous algorithms.

A project scheduling problem investigates a set of activities that have to be scheduled due to precedence priority and resource constraints in order to optimize project-related objective functions. This paper focuses on the multi-mode project scheduling problem concerning resource constraints (MRCPSP). Resource allocation and leveling, renewable and non-renewable resources, and time-cost trade-off are some essential characteristics which are considered in the proposed multi-objective scheduling problem. In this paper, a novel hybrid algorithm is proposed based on non-dominated sorting ant colony optimization and genetic algorithm (NSACO-GA). It uses the genetic algorithm as a local search strategy in order to improve the efficiency of the ant colony algorithm. The test problems are generated based on the project scheduling problem library (PSPLIB) to compare the efficiency of the proposed algorithm with the non-dominated sorting genetic algorithm (NSGA-II). The numerical result verifies the efficiency of the proposed hybrid algorithm in comparison to the NSGA-II algorithm.

We present a multiobjective genetic algorithm that incorporates various genetic algorithm techniques that have been proven to be efficient and robust in their problem domain. More specifically, we integrate rank based selection, adaptive niching through coevolutionary sharing, elitist recombination, and non-dominated sorting into a multiobjective genetic algorithm called ERMOCS. As a proof of concept we test the algorithm on a softkill-scheduling problem.

In the present work, a thermal modeling is conducted for optimal design of compact heat exchangers in order to minimize cost and entropy generation. In this regard, an ε−NTU method is applied for estimation of the heat exchanger pressure drop, as well as effectiveness. Fin pitch, fin height, fin offset length, cold stream flow length, no-flow length, and hot stream flow length are considered as six decision variables. Fast and elitist nondominated sorting genetic algorithm (i.e., nondominated sorting genetic algorithm II) is applied to minimize the entropy generation units and the total annual cost (sum of initial investment and operating and maintenance costs) simultaneously. The results for Pareto-optimal front clearly reveal the conflict between two objective functions, the number of entropy generation units and the total annual cost. It reveals that any geometrical changes, which decrease the number of entropy generation units, lead to an increase in the total annual cost and vice versa. Moreover, for prediction of the optimal design of the plate fin heat exchanger, an equation for the number of entropy generation units versus the total annual cost is derived for the Pareto curve. In addition, optimization of heat exchangers based on considering exergy destruction revealed that irreversibilities, such as pressure drop and high temperature difference between cold and hot streams, play a key issue in exergy destruction. Thus, more efficient heat exchanger leads to have a heat exchanger with higher total cost rate. Finally, the sensitivity analysis of change in the optimum number of entropy generation units and the total annual cost with change in the decision variables of the plate fin heat exchanger is also performed, and the results are reported.

A convenient-accurate optimization method of the multi-effect evaporation system

Magnetorheological brake (MRB) provide a potential alternative to traditional mechanical friction brakes in automobile applications owing to their technical advantages in terms of being compact yet powerful, having superior control performance, and wire-control features. However, the temperature effect has been an important issue that should be considered in the design and precise control of MRB. This paper presents the multi-objective optimal design and stability control of an automotive MRB considering the temperature effect. First, a description of the configuration design, magnetostatic field simulation, and mathematical modeling of the automotive MRB is presented in detail. Subsequently, design optimization of the MRB is carried out. The design of experiment method was adopted to screen out the major design variables, and the optimal solution was obtained using a multi-objective genetic algorithm (GA) non-dominated sorting genetic algorithm (NSGA-II). Thereafter, the torque output and response performance, as well as the temperature characteristics of the optimal MRB prototype are experimentally evaluated. The results indicate that the optimal design of the MRB was reasonable and effective. Finally, a GA-based back propagation neural network proportion integration differentiation controller is proposed for the stability control of the MRB during automobile braking. Its performance was verified to be satisfactory through both simulations and experiments.

This study aims to optimize the impeller geometry of a backward-curved blade centrifugal fan in a helicopter oil cooler to improve the aerodynamic performance of the impeller. The study consists of three main parts: aerodynamic design and parametrization of the impeller, numerical analysis of impeller performance, and multi-objective design optimization to maximize fan static pressure and total to static isentropic efficiency. In the first part, a one-dimensional impeller design was performed using quasi-experimental methods and a direct optimization method with the multi-objective genetic algorithm, and the impeller geometry was parameterized through a commercial tool. The three-dimensional flow through the impeller was solved in the second part with a commercial computational fluid dynamics tool. The Reynolds-averaged Navier-Stokes equations were solved on a multi-block grid, and a second-order accurate finite volume method is employed. The most appropriate turbulence model and grid size were selected considering time, cost, and fidelity. In the third part, a sensitivity analysis was performed with the design-of-experiment method, and the parameters that affect the objective function most significantly were determined. A multi-objective design optimization based on a non-dominated sorting genetic algorithm was performed with the Kriging response surface method, and Pareto-optimal solutions were obtained. Results show that, at the design point, there is a 9.6% and 0.96% increase in the fan static pressure and total to static isentropic efficiency, respectively. Trained kriging response surface model predicted the fan static pressure and total to static isentropic efficiency with an error of 0.12% and 0.33%, respectively.