NSGA-II Procedure Research Articles

Multi-objective optimization of thermophysical properties GO powders-DW/EG Nf by RSM, NSGA-II, ANN, MLP and ML

In this study, prediction, modeling, and optimization have been performed for four TPH properties of graphene oxide nano powder-deionized water/ethylene glycol nf, which is unique compared to other studies. Response surface methodology, artificial neural networks based on multiple layers of perceptron, and algorithms based on machine learning have been developed for prediction and modeling. RSM modeling resulted in coefficients of determination of 0.9984, 0.9986, 0.9995, and 0.9996 for TC (k), density (ρ), SHC (cp), and viscosity (μ), respectively. The highest prediction errors for RSM models were 0.3644%, 0.0374%, 2.049%, and 0.2296% for k, ρ, μ, and cp. A higher temperature and a higher WF of NPs increased the TC of the nf. The maximum MLP model error was 0.43%, 6.59%, 12.64%, and 1.04% for ρ, cp, μ, and k, respectively. TC and SHC were optimized using the NSGA-II algorithm. The NSGA-II procedure indicated the maximum k and cp occurred at the highest temperatures. The temperature must be kept at its maximum to reach the optimal stage. Also, it was proven that temperature is a much more significant parameter than the nanoparticle WF.

Multi-objective evolutionary algorithm for solving energy-aware fuzzy job shop problems

A growing concern about the environmental impact of manufacturing processes and in particular the associated energy consumption has recently driven some researchers within the scheduling community to consider energy costs in addition to more traditional performance-related measures, such as satisfaction of due-date commitments. Recent research is also devoted to narrowing the gap between real-world applications and academic problems by handling uncertainty in some input data. In this paper, we address the job shop scheduling problem, a well-known hard problem with many applications, using fuzzy sets to model uncertainty in processing times and with the target of finding solutions that perform well with respect to both due-date fulfilment and energy efficiency. The resulting multi-objective problem is solved using an evolutionary algorithm based on the NSGA-II procedure, where the decoding operator incorporates a new heuristic procedure in order to improve the solutions’ energy consumption. This heuristic is based on a theoretical analysis of the changes in energy consumption when a solution is subject to slight changes, referred to as local right shifts. The experimental results support the theoretical study and show the potential of the proposal.

Multi-objective optimization of an aluminum torch brazing process by means of genetic programming and R-NSGA-II

This paper presents a hybrid of genetic programming (GP) and reference-point-based non-dominated sorting genetic algorithm (R-NSGA-II) for the multi-objective optimization of an aluminum torch brazing process for the fabrication of condensers for the automotive industry. The objectives to be optimized are a vacuum leakage test (quality of product), cycle time, and energy consumption (production cost). GP is used to find a mathematical model that describes the relationship between input and output process parameters. Thereafter, reference-point-based NSGA-II procedure is employed to provide the decision maker with a set of solutions close to her/his preferences. Results show that this approach may support the decision makers of the process to set the optimal input process parameters in order to achieve competitive advantages in quality and production costs.

Mapping multiple plant species abundance patterns - A multiobjective optimization procedure for combining reflectance spectroscopy and species ordination

Nature conservation and ecological restoration crucially depends on the knowledge about spatial patterns of plant species that control habitat conversion and disturbance regimes. Especially, species abundances are capable of indicating early development tendencies for setting habitat management strategies. This study demonstrates the transfer of field spectroscopy to hyperspectral imagery to map multiple plant species abundances in an open dryland area using two imaging spectrometers in two different phenological phases. We show that species abundances can partially be described by multiple gradients forming different coordinates in a contour map. For this purpose, species abundances were projected into an ordination space using non-metric multidimensional scaling and subsequent spatial interpolation. It was demonstrated that different gradients can be modeled in a Partial Least Squares regression framework resulting in distinct spectral features for certain gradient directions. We combine both objectives in a multiobjective NSGA-II procedure to maximize the quantitative determination of species abundance in ordination and spectral predictability in related field spectra, simultaneously. NSGA-II was finally used to select optimal spectral models for n=35 single species that were transferred to hyperspectral imagery for mapping purpose. We can show that abundance predictabilities can be evaluated on the basis of individual model performances that hold different spectral features for each species in a designated phenological phase. Finally, we present spatially explicit multi-species maps for the best n=18 and abundance maps for n=8 models that could be linked to patterns of species richness, coexistence, succession stages and habitat type conditions.

A random keys genetic algorithm for a bicriterion project selection and scheduling problem

The project selection and scheduling problem involves the allocation of limited resources to competing projects over time to optimise a given objective function. However, in practical applications, multiple criteria need to be considered, leading us to formulate the problem as a multiple objective combinatorial optimisation (MOCO) model. Activities are subject to precedence constraints, as well as a budget limiting the capital available in each planning period. Interdependencies between projects by which the selection of specific subsets of projects may result in cost savings are also represented. We propose a genetic algorithm incorporating random keys and an efficient decoding procedure into the well-known NSGA-II procedure. The performance of this algorithm is evaluated in extensive computational experiments comparing the approximations of the Pareto-optimal set it obtains to those from NSGA-II.

A novel multi criteria decision making model for optimizing time–cost–quality trade-off problems in construction projects

The planning phase of every construction project is entangled with multiple and occasionally conflicting criteria which need to be optimized simultaneously. Multi-criterion decision-making (MCDM) approaches can aid decision-makers in selecting the most appropriate solution among numerous potential Pareto optimal solutions. An evidential reasoning (ER) approach was applied for the first time in the context of project scheduling to identify the best Pareto solution for discrete time–cost–quality trade-off problems (DTCQTPs). An exhaustive framework to synthesize the MCDM approaches with multi-objective optimization techniques was also proposed. To identify all global Pareto optimal solutions, a multi-objective genetic algorithm (MOGA) incorporating the NSGA-II procedure was developed and tested in a highway construction project case study. The Shannon’s entropy technique served to determine the relative weights of the objectives according to their contributions to the uncertainty of the results obtained. A benchmark case study of DTCQTP was solved using the proposed methodology, and the Pareto optimal solutions obtained were subsequently ranked using the ER approach. By investigating the performance of each scheduling alternative based on multiple criteria (e.g., time, cost, and quality), the proposed approach proved effective in raising the efficiently of construction project scheduling.

Design of particle-reinforced polyurethane mould materials for soft tooling process using evolutionary multi-objective optimization algorithms

Polyurethane is used for making mould in soft tooling (ST) process for producing wax/plastic components. These wax components are later used as pattern in investment casting process. Due to low thermal conductivity of polyurethane, cooling time in ST process is long. To reduce the cooling time, thermal conductive fillers are incorporated into polyurethane to make composite mould material. However, addition of fillers affects various properties of the ST process, such as stiffness of the mould box, rendering flow-ability of melt mould material, etc. In the present work, multi-objective optimization of various conflicting objectives (namely maximization of equivalent thermal conductivity, minimization of effective modulus of elasticity, and minimization of equivalent viscosity) of composite material are conducted using evolutionary algorithms (EAs) in order to design particle-reinforced polyurethane composites by finding the optimal values of design parameters. The design parameters include volume fraction of filler content, size and shape factor of filler particle, etc. The Pareto-optimal front is targeted by solving the corresponding multi-objective problem using the NSGA-II procedure. Then, suitable multi-criterion decision-making techniques are employed to select one or a small set of the optimal solution(s) of design parameter(s) based on the higher level information of the ST process for industrial applications. Finally, the experimental study with a typical real industrial application demonstrates that the obtained optimal design parameters significantly reduce the cooling time in soft tooling process keeping other processing advantages.