Narrow Results

This paper examines the current state of the Enterprise Marketing Ecosystem (EME), especially in the context of Digital Transformation (DT), highlighting both the challenges and opportunities it presents. A significant focus is placed on the potential of genetic algorithms to address the complex optimization problems inherent in this transformation. The core issues faced by enterprises in their digital marketing evolution are elaborated, setting the stage for the exploration of genetic algorithms in this domain. The research delves into the specific steps of the proposed method, utilizing the multi-objective genetic algorithm NSGA-II to optimize various aspects of market strategies. The methodology includes the generation of an initial population, evaluation of fitness according to the tailored needs of the marketing ecosystem, selection, crossover, and mutation operations, followed by the formation of a new generation of solutions. The termination criteria of the algorithm are also discussed. Experimental results are presented, demonstrating the effectiveness of the genetic algorithm in enhancing the DT of the EME. Comparisons with traditional approaches reveal significant improvements in various key performance metrics, underscoring the algorithm’s capability in navigating the complexities of digital marketing optimization.

The necessity for innovative biomaterials has been growing recently due to the rising cost of materials for intricate biomedical equipment. An important tactic to improve critical attributes like hemocompatibility, osseointegration potential, corrosion resistance, and antibacterial capabilities is surface modification. In this paper, an investigation has been made in the field of laser surface modification and the complex interactions between laser parameters and output performance metrics, such as contact angle and surface roughness. Surface modification by laser has been successful and, in this research, the laser parameters such as laser energy (Watts), standoff distance(mm), and frequency (kHz) along with dimple distance on the surface ($\mu$m) were considered on the output performance namely surface roughness in “$\mu$m” and contact angle in “degree”. The experiment has been carried out using the L16 orthogonal array to interpret the complex correlations between these factors and the resulting surface features. Non-dominated sorting genetic algorithm II (NSGA-II) has successfully navigated the complex parameter space and found the optimal combinations that yield the intended outcomes. The results show how important dimple distance and laser frequency are in creating hydrophobic surfaces, as well as how much of an impact they have on surface properties. It has been discovered that higher frequencies and longer standoff distances specifically reduce surface roughness, which is a crucial component in ensuring enhanced biomaterial performance. The result shows that the dimple distance and frequency of the laser have a significant effect on the development of hydrophobic surfaces. Moreover, high frequency and more standoff distance reduce the surface roughness. The predicted combination of laser parameters as per the NSGA-II is 102.91$\mu$m, 33.35W, 223.12mm, 50.01kHz, and gives a surface roughness of 0.86$\mu$m and contact angle of 158.83^∘. In essence, this study not only sheds light on the intricate dynamics governing laser-based surface modification but also paves the way for the design and development of advanced biomaterials with tailored surface properties, poised to revolutionize biomedical applications.

To further enhance the ride comfort of vehicles, a new type of fluid inerter-based hydro-pneumatic suspension (FI-HPS) is proposed. First, this paper combines the fluid type inerter with the dual-chamber hydro-pneumatic suspension (DHPS) and fully considers the nonlinear factors. The nonlinear dynamic model of the fluid inerter is derived, and three structural models, namely the traditional DHPS S0, the ideal FI-HPS S1, and the nonlinear FI-HPS S2, are established. Then, the non-dominated sorting genetic algorithm II (NSGA-II) is employed to optimize the key parameters of the S1 and S2 suspensions. With the S0 suspension as a comparison subject, the simulation results show that the S2 suspension can significantly improve the vehicle’s ride comfort performance. In terms of the time-domain analysis, the root-mean-square (RMS) value of the vehicle body acceleration is reduced by 20.5%, the RMS value of the suspension working space is reduced by 12.7%, and the RMS value of the dynamic tire load is reduced by 8.0%. The frequency domain results indicate that the S2 suspension can effectively reduce the suspension offset frequency vibration, with a more significant effect at low frequencies. Upon analysis of impulsive road conditions, the peak-to-peak (PTP) value of the vehicle body acceleration is reduced by 14.2%, and the PTP value of the suspension working space is reduced by 6.3%. It is revealed that the inclusion of the nonlinear parasitic damping force in the fluid inerter can effectively enhance the overall performance of the hydro-pneumatic suspension system.

The retina is a very complex neural structure, which contains many different types of neurons interconnected with great precision, enabling sophisticated conditioning and coding of the visual information before it is passed via the optic nerve to higher visual centers. The encoding of visual information is one of the basic questions in visual and computational neuroscience and is also of seminal importance in the field of visual prostheses. In this framework, it is essential to have artificial retina systems to be able to function in a way as similar as possible to the biological retinas. This paper proposes an automatic evolutionary multi-objective strategy based on the NSGA-II algorithm for tuning retina models. Four metrics were adopted for guiding the algorithm in the search of those parameters that best approximate a synthetic retinal model output with real electrophysiological recordings. Results show that this procedure exhibits a high flexibility when different trade-offs has to be considered during the design of customized neuro prostheses.

A new zigzag lattice phononic crystal with holes is designed. Nondominated sorting genetic algorithm-II (NSGA-II) is used for the optimization of the newly designed phononic crystal (PC). Results indicate that geometrical parameters are key factors as well as density and elastic modulus for the determination of the bandgaps (BGs). The width of the BG of the optimized PC with holes can be increased three times higher than the initial design without holes.

In this paper, the multi-objective optimization problem (MOOP) of selecting suitable materials from four materials for phononic crystals layouts is investigated based on an optimization method combined nondominated sorting-based genetic algorithm II (NSGA-II) and finite element method (FEM). Driven by different optimization objectives, the changes in the optimized structure compared to the seed structure are the change of the bandgap mechanism from a local resonance mechanism to a Bragg scattering mechanism, the change of the included materials from four to three, and the significant change in the location and extent of the pores and other materials, respectively. The obtained nondominated Pareto solution of MOOP can balance the bandgap and the structural mass, which provides the decision-maker trade-off to select the appropriate optimized solution. Compared with the single-objective optimization problem (SOOP), the MOOP not only obtains a nondominated solution close to the result of SOOP, but also obtains other nondominated solutions of MOOP, which proves the effectiveness of the optimization algorithm in this paper. The design method in this paper can be easily extended to select suitable materials from a wider variety of materials for bandgap optimization design of PnCs, which has very promising applications.

This paper concentrates on energy conservation in flexible manufacturing system. In addition to the energy saving of single machine tool, it is significant to reduce energy consumption in the two sub-systems of process planning and shop scheduling. Compared to traditional methods that consider the two sub-systems separately, integrated optimization of these sub-systems further improves the energy efficiency of the job shop. Furthermore, the transportation of jobs and semi-manufactured jobs in the process have been ignored in previous research, which has a great influence on the process routes selecting, machine dispatching and energy consumption. Therefore, this paper proposes an energy-aware multi-objective integrated optimization model of process planning and shop scheduling considering transportation. Parameters are optimized simultaneously including work piece machining feature selecting, process method selecting, processing sequence and machine dispatching of each job. The non-dominated sorting genetic algorithm is adopted to minimize the total energy consumption and makespan. Finally, a case study using the proposed model is employed to verify that energy consumption of transportation has authentically influence on total energy consumption and scheduling scheme.

Proposed work presents an OR-XNOR-based thermal-aware synthesis approach to reduce peak temperature by eliminating local hotspots within a densely packed integrated circuit. Tremendous increase in package density at sub-nanometer technology leads to high power-density that generates high temperature and creates hotspots. A nonexhaustive meta-heuristic algorithm named nondominated sorting genetic algorithm-II (NSGA-II) has been employed for selecting suitable input polarity of mixed polarity dual Reed–Muller (MPDRM) expansion function to reduce the power-density. A parallel tabular technique is used for input polarity conversion from Product-of-Sum (POS) to MPDRM function. Without performance degradation, the proposed MPDRM approach shows more than 50% improvement in the area and power savings and around 6% peak temperature reduction for the MCNC benchmark circuits than that of earlier literature at the logic level. Algorithmic optimized circuit decompositions are implemented in physical design domain using CADENCE INNOVUS and HotSpot tool and silicon area, power consumption and absolute temperature are reported to validate the proposed technique.

With the development of 5G technology, the traffics which need multiple quality of service (QoS) constraints greatly increase. The existing QoS routing algorithms either support a single QoS constraint or support multiple constraints are mixed with fixed weights. The latter is essentially a single-constrained QoS algorithm. Few studies can provide the quality of service guaranteed under the true sense of multiple constraints, by which only one solution can be provided. In this paper, we propose NSGAII-MCR: a multi-constrained QoS routing algorithm based on non-dominated sorting genetic algorithm II (NSGA-II). In NSGAII-MCR, three representative constraints are selected: delay, bandwidth, and packet loss rate. The validity of the routing is guaranteed by a designed encoding and decoding scheme and a novel individual generation method. Meantime, the original NSGA-II crossover and mutation operators are modified. We further enhance the selection strategy to improve the diversity of solutions and the robustness of NSGAII-MCR. At last, multiple optional routes in the Pareto-optimal front are obtained. The experiment demonstrates that the proposed NSGAII-MCR can achieve high success rate by up to 95%. Compared with the benchmarking methods, NSGAII-MCR can support more constraints and finds more multi-constrained routes.

This paper is focused on the multiobjective design of equivalent accelerated life test (ALT) plans. Equivalent ALT plans are expected to achieve the same statistical performance as a baseline ALT plan yet lead to other desired performance measures such as reduced test time and total cost. Before determining the desired multiobjective equivalent ALT plans, an efficient fast non-dominated sorting genetic algorithm (NSGA-II) is utilized to identify a set of Pareto optimal solutions. To handle a large number of Pareto optimal solutions, a self-organizing map (SOM) and data envelopment analysis (DEA) are sequentially applied to classify the Pareto solutions and reduce the size of the suggested solution set. This integrated approach allows for the tradeoff of information among the Pareto solutions and the reduction in the size of the solution set. It provides a useful tool for practitioners to make meaningful decisions in planning ALT experiments.

Ships have a wide variety of machinery available onboard that is crucial for her sustenance at sea for prolonged durations. The machinery can be grouped into various plants, such as propulsion plant, air conditioning plants, power generation plants, etc., each having its own specific function. The plants in turn are composed of various systems which further comprise various types of machinery. There are redundancies built in at the plant level, as well as at the system and at machinery level, so as to improve the reliability of the ship as a whole. Planning of maintenance schedule, specifically for tasks which can only be undertaken in an ashore repair yard is a daunting task for the maintenance managers. The paper presents a NSGA-II (nondominated sorting genetic algorithm) based multi-objective optimization approach to arrive at an optimum maintenance plan for the vast variety of machinery in order to improve the average reliability of ship's operations at sea at minimum cost. The paper presents the advantages that can accrue from introducing short maintenance periods for a select group of machinery, within the constraints of mandatory operational time, over the method of following a common maintenance interval for all the machinery. The problem function in hand is nonlinear, multi-modal and multi-objective in nature. The search spaces for the problem is noncontinuous and the (multiple) variables, such as time interval for maintenance, serial number of equipment, number of minor maintenance actions, etc., are uncoded real parameters.

This paper aims to develop a predictive model and optimize the performance of the abrasive water jet machining (AWJM) during machining of carbon fiber-reinforced plastic (CFRP) epoxy laminates composite through a unique approach of artificial neural network (ANN) linked with the nondominated sorting genetic algorithm-II (NSGA-II). Initially, 80 AWJM experimental runs were carried out to generate the data set to train and test the ANN model. During the experimentation, the stand-off distance (SOD), water pressure, traverse speed and abrasive mass flow rate (AMFR) were selected as input AWJM variables and the average surface roughness and kerf width were considered as response variables. The established ANN model predicted the response variable with mean square error of 0.0027. Finally, the ANN coupled NSGA-II algorithm was applied to determine the optimum AWJM input parameters combinations based on multiple objectives.

Developing test cases is the most challenging and crucial step in the software testing process. The initial test data must be optimized using a strong optimization technique due to many testing scenarios and poor testing effectiveness. Test prioritization is essential for testing the developed software products in a production line with a restricted budget in terms of time and money. A good understanding of the trade-off between costs (e.g. time and resources needed) and efficiency (e.g. component coverage) is necessary to prioritize test case scenarios for one or more software products. So, this paper proposes an efficient Multi-objective Test Case Generation and Prioritization using an Improved Genetic Algorithm (MTCGP-IGA) in Component-based Software Development (CSD). A random search-based method for creating and prioritizing multi-objective tests has been employed utilizing numerous cost and efficacy criteria. Specifically, the multi-objective optimization comprises maximizing the Prioritized Range of test cases (PR), Pairwise Coverage of Characteristics (PCC), Fault-Finding Capability (FFC), and minimizing Total Implementation Cost (TIC). For this test prioritizing problem, a unique fitness function is constructed with cost-effectiveness metrics. IGA is a robust search technique that exhibits excellent benefits and significant efficacy in resolving challenging issues, including ample space, multiple-peak, stochastic, and universal optimization. Relying on the use of IGA, this paper classifies, computes the objective function, introduces the Nondominated Sorting Genetic Algorithm-II (NSGA-II) method, evaluates each branch’s proximity on the handling route, and arranges the path set to get the best answer. The outcomes demonstrate that the proposed MTCGP-IGA with NSGA-II performed the best than other baseline algorithms in terms of prioritizing the test cases (mean value of 195.2), PCC (mean score of 0.7828), and FFC (mean score of 0.8136).

To reduce the aerodynamic load of super high-speed elevators, in this paper, the coefficient of drag $C_d$ and the coefficient of yawing moment $C_{ym}$ of the elevator are selected as optimization objectives for the optimization of the air rectification cover (ARC) shape. The elliptic curve method was used to build the parametric model of the ARCs, six design variables were selected, and the design space of the ARC was determined. With the optimal Latin hypercube design method, the training points were selected, and the computational fluid dynamics numerical simulation was conducted to calculate the corresponding responses. Then, the relationship between the design variables and the responses was analyzed. The radial basis function (RBF) surrogate model of the relationship between the design variables and responses was constructed. Finally, the non-dominated sorting genetic algorithm-II (NSGA-II) was employed to optimize the shape of the ARC. The results show that the $C_d$ and $C_{ym}$ decrease by 16.51% and 60.92%, respectively, compared with the unoptimized ARC, indicating that the ARC designed in this paper is optimized and can effectively reduce the aerodynamic load. Furthermore, among all the design variables, the bluntness of the ARC in the $X$-direction has the most significant effect on the aerodynamic load, and the height of the ARC ($h_1$ and $h_2$) has the second most significant effect on the aerodynamic load of elevators.

This paper describes the emergency rescue vehicle transportation network within the entire rescue period, and imitates rescue vehicle to select rescue route and to allocate emergency resource. The presented emergency rescue vehicle dispatch model seeks to minimize rescue time as the first objective function, minimize delay cost as the second objective function and maximize lifesaving utility as the last objective function in disaster response operations. To solve the proposed multiple objective model, a hybrid algorithm named nondominated sorting genetic algorithm (NSGA-II) with ant colony algorithm and a NSGA-II with random crossover and mutation, which can find better initial solution, are presented. In order to further prove the validity of the model and algorithm, a more complicated case is cited. Computational results are reported to illustrate the performance of the proposed model and algorithm. Statistical analysis confirms that the proposed random crossover and mutation operator outperforms the original crossover and mutation operator. The sensitivity analysis proves which parameter is more important for objective function values.

Inconel 718 is a nickel-based superalloy having high strength and low thermal conductivity. Due to its properties, wire electrical discharge machining has been selected. The paper reports an experimental investigation of Inconel 718 using zinc-coated brass wire electrode. Based on $L_{27}$, Box-Behnken design of response surface methodology has been adopted to estimate the effect of process parameter on the machining responses. Four controllable process parameters (viz., wire tension, wire speed, discharge current and pulse-on time) vary, each at three discrete levels, between parametric domains. The following machining responses, in terms of material removal rate (MRR), surface roughness ($R_a$) and corner deviation ($C_d$), have been investigated. Finally, an evolutionary computation method has been used based on non-dominated sorting genetic algorithm (NSGA-II) in order to find out the optimal set of solutions for rough-cutting. Experimental data have been used to develop regression models to optimize the process. The adequacy of the developed mathematical model has also been tested by the analysis of variance results. Pareto-optimal settings obtained through NSGA-II have been ranked by gray relation analysis to identify the best optimal set of solutions to avoid lengthiness and impreciseness in the judgment. Confirmation tests have been conducted for optimum machining parameter from the set of Pareto-optimal solutions for proving betterment.

Nowadays, due to environmental issues, government rules and economic interests have increased attention to the collection and recovery of products, which has led to the formation of new concepts such as reverse and closed-loop supply chains. The implementation of the closed-loop supply chain as a solution to sustainable development is expanding from one hand and increasing the profitability of companies on the other. For this purpose, a mathematical model was developed to design an integrated closed-loop supply chain network, which is a combination of two-problem localization problems and flow optimization. The proposed model was designed to minimize network costs and to maximize the level of responsiveness to customers. The cost parameters of establishing centers in this model are uncertain; to overcome the model’s uncertainties, stochastic programming is used. In the mathematical model, supplier, manufacturer, distributor and customer in the direct supply chain and collection/rehabilitation, destruction, recycling centers and, second-type distribution center for sale of second-hand products as well as second-hand products customers in the reverse flow are considered, to be closer to the real today world. This model is multi-periodic mix integer nonlinear programming where the shortage has allowed. To motivate and encourage customers to buy more, in addition to getting closer to the real world and it happens more in practice, is considered all units of discount for transportation cost in the forward flow. To solve this model Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) is using. The parameter tuning was done using the Taguchi method. Then, the important criteria for measurement and comparison of performance algorithms have used, including the Mean Ideal Distance, Diversification Metric, Number of Pareto-optimal Solutions, and the Quality Metric. Results of the Comparative metrics show that NSGA-II outperforms MOPSO in almost all cases in achieving the best trade-off solutions.

Duplex turning (DT) is a novel concept of metal cutting where two tools are employed to cut the objects in lieu of single tool. It shows many benefits over conventional turning in terms of superior dynamic balancing, lower cutting forces and tool wears, better surface finish, reduction in vibration with additional support for workpiece. It is a complex method and the resulting experimental analysis becomes difficult and expensive. In such conditions, modeling techniques show their potential for parametric study, prediction of data for optimization and selection of optimal condition. Generally, soft computing-based Artificial Neural Network (ANN) is applied for modeling and prediction for complicated processes while Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) shows their potential for optimization of complex problems over Genetic Algorithm. Therefore, ANN and NSGA-II techniques are employed for modeling and optimization of DT process to minimize the surface roughness and cutting forces (primary and secondary). Finally, results reflect that ANN efficiently predicts the responses at different input combinations within training data set with absolute percentage errors as 2.55% for roughness, while 3.05% and 3.14% for cutting forces (primary and secondary), respectively. In the same way, optimized results also found within the range of acceptability with percentage errors as 2.57% for roughness, while 3.25% and 3.15% for primary and secondary forces, respectively.

An improved D’Angelo optimization framework based on the surrogate model and optimization algorithm is proposed to design and optimize a new high altitude propeller which is applied to the propulsion system of the stratospheric aircraft. The framework adopts a multilevel optimization strategy to determine a high efficiency and light weight propeller. The aerodynamic characteristics of S1223 airfoil are calculated by Computational Fluid Dynamics (CFD) method. The aerodynamic performance of the D’Angelo propeller which is obtained by the first-level optimization is predicted by Blade element momentum (BEM) theory. Then the D’Angelo propeller chord and twist angle, which are regarded as the initial conditions of the second-level optimization, are optimized to achieve the maximum efficiency by Multi-island genetic algorithm (MIGA). In addition, the non-dominated sorting genetic algorithm II (NSGA-II) is applied to maximize the propeller efficiency and minimize the propeller weight. And the Pareto frontier solutions about the efficiency and the blade area are obtained by NSGA-II. What is more, the aerodynamic characteristics of the D’Angelo propeller, the optimization propeller by MIGA, and the optimization propeller by NSGA-II are calculated by CFD simulation and compared with BEM results. It is shown that the CFD results are in fair agreement with BEM results and the aerodynamic performance of the NSGA-II propeller is prior to the MIGA propeller and is close to the D’Angelo propeller. Besides, the NSGA-II propeller is the lightest among them and can satisfy the cruise requirements of the high altitude propeller.

The optimal design of large-scale process plant is difficult due to the presence of Pareto sets or nondominated solutions. Many conventional and advanced mathematical techniques had been adopted which have their own limitations in solving the complex design problem. In this paper, nondominant-sorted genetic algorithms NSGA and NSGA-II have been adopted for the optimal design of complex Williams-Otto model process plant. The plant consists of a reactor, separation system consisting of heat exchanger, decanter and distillation column. Multiobjective optimization is used to maximize the profit, i.e. the return on investment, to maintain lesser use of costlier raw material and lesser disposal of the waste byproducts. So NSGA-II is employed in this study as an effective replacement for NSGA, classical genetic algorithm, conventional and traditional methods of optimization in solving multiobjective process design problems and to achieve fine-tuning of variables in determining Pareto optimal design parameters. NSGA-II method finding global optimal front has a significant effect on the design of control system for the real time and continuous robust control of complex process plant as each target vector provides proper direction and drives the process to multiobjective optimum conditions.