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The purpose of this paper is to develop a pertinent design optimization methodology for symmetric designs of a metamaterial (MM) unit.

A cell division mechanism is introduced and used to design a new selecting mechanism in the proposed algorithm, a non-dominated sorting cellular genetic algorithm (NSCGA).

The numerical results on solving standard multi-objective test functions and a prototype MM unit positively demonstrate the advantages of the proposed NSCGA.

A new NSGAII-based optimization algorithm, NSCGA, for multi-objective optimization designs of a MM unit is proposed.

This study aims to present development of genetic algorithm (GA)-based framework aimed at minimizing data center cooling energy consumption by optimizing the cooling set-points while ensuring that thermal management criteria are satisfied.

Three key components of the developed framework include an artificial neural network-based model for rapid temperature prediction (Athavale et al., 2018a, 2019), a thermodynamic model for cooling energy estimation and GA-based optimization process. The static optimization framework informs the IT load distribution and cooling set-points in the data center room to simultaneously minimize cooling power consumption while maximizing IT load. The dynamic framework aims to minimize cooling power consumption in the data center during operation by determining most energy-efficient set-points for the cooling infrastructure while preventing temperature overshoots.

Results from static optimization framework indicate that among the three levels (room, rack and row) of IT load distribution granularity, Rack-level distribution consumes the least cooling power. A test case of 7.5 h implementing dynamic optimization demonstrated a reduction in cooling energy consumption between 21%–50% depending on current operation of data center.

The temperature prediction model used being data-driven, is specific to the lab configuration considered in this study and cannot be directly applied to other scenarios. However, the overall framework can be generalized.

The developed framework can be implemented in data centers to optimize operation of cooling infrastructure and reduce energy consumption.

This paper presents a holistic framework for improving energy efficiency of data centers which is of critical value given the high (and increasing) energy consumption by these facilities.

The genetic algorithm (GA) technique is widely used for the optimization of stiffened composite panels. It is based on sequential execution of a number of specific operators, including the encoding of particular design variables. For instance, in the case of a stiffened composite panel, the design variables that need to be encoded are: the number of plies and their stacking sequences in the panel skin and stiffeners. This paper aims to present a novel, implicit, heuristic approach for encoding composite laminates and, through its use, demonstrates an improvement in the optimization process.

The stiffened panel optimization has been formulated as a constrained discrete minimum-weight design problem. GAs, which use both new encoding schemes and those previously described in the literature, have been used to find near-optimal solutions to the formulated problem. The influence of the new encoding scheme on the searching capabilities of the GA has been investigated using comparative analysis of the optimization results.

The new encoding scheme allows the definition of stacking sequences in composites using shorter symbolic representations as compared with standard encoding operators and, as a result of this, a reduction in the problem design space. According to numerical experiments performed in this work, this feature enables GA to obtain near-optimal designs using smaller population sizes than those required if standard encoding schemes are used.

The approach to encoding laminates presented in this paper is based on the original heuristics. In the context of GA-based optimization of stiffened composite panels, the use of the new approach rather than the standard encoding technique can lead to a significant reduction in computational time employed.

For classification problems of customer relationship management (CRM), the purpose of this paper is to propose a method with interpretability of the classification results that combines multiple decision trees based on a genetic algorithm.

In the proposed method, multiple decision trees are combined in parallel. Subsequently, a genetic algorithm is used to optimize the weight matrix in the combination algorithm.

The method is applied to customer credit rating assessment and customer response behavior pattern recognition. The results demonstrate that compared to a single decision tree, the proposed combination method improves the predictive accuracy and optimizes the classification rules, while maintaining interpretability of the classification results.

The findings of this study contribute to research methodologies in CRM. It specifically focuses on a new method with interpretability by combining multiple decision trees based on genetic algorithms for customer classification.

In recent years, the airfoil sections with blunt trailing edge (called flatback airfoils) have been proposed for the inboard regions of large wind‐turbine blades because they provide several structural and aerodynamic performance advantages. The purpose of this paper is to optimize the shape of these airfoils for optimal performance using a multi‐objective genetic algorithm.

A multi‐objective genetic algorithm is employed for shape optimization of flatback airfoils to achieve two objectives, namely the generation of maximum lift as well as the maximum lift to drag ratio. The commercially available software FLUENT is employed for calculation of the flow field using the Reynolds‐Averaged Navier‐Stokes (RANS) equations in conjunction with a two‐equation Shear Stress Transport (SST) turbulence model and a three‐equation k‐kl‐ω turbulence model.

It is shown that the multi‐objective genetic algorithm based optimization can generate superior flatback airfoils compared to those obtained by using a single objective genetic algorithm.

The method of employing genetic algorithms for shape optimization of flatback airfoils could be considered as an excellent example for the optimization of other types of wind turbine blades such as DU FX and S series airfoils.

This paper is the first to employ the multi‐objective genetic algorithm for shape optimization of flatback airfoils for wind‐turbine blades to achieve superior performance.

The results obtained from previous studies are not found to be sufficient for hydrostatic bearing design optimisation since thermodynamic effects are not considered. Therefore, this research is presented with considering parameter variations based on thermodynamic effects for more efficient optimisation of bearing parameters.

Single and multi‐criteria approaches were carried out to determine the hydrostatic journal bearing design parameters so that the total performance of the system is optimal.

It is seen that firstly, the results of single criteria approaches for minimum power, bearing coefficient and minimum temperature rise in circular hydrostatic axial journal bearings are not sufficient, secondly, there is a crucial need to consider multiple criteria optimisation cases and thirdly, thermodynamic effects must be taken into account for more efficient approach to compute the optimum values of bearing design parameters.

Further research is required to develop a genetic algorithm‐based optimisation for bearing design problems considering thermodynamic effects and multiple criteria approaches to compare the results of present study.

Comparison of optimisation results of single and multi‐criteria approaches are given to show temperature variation effects on bearing performance.

Although, there are some works related to design and optimisation of hydrostatic bearings, most of them consider the single criteria optimisation and thermodynamic effects are not usually taken into account. Therefore, this research is different than others since the present approach is implemented with thermodynamic effects and also not limited to single criteria approach.

Using a real-time road network combined with historical traffic data for Al-Salt city, the paper aims to propose a new federated genetic algorithm (GA)-based optimization technique to solve the dynamic vehicle routing problem. Using a GA solver, the estimated routing time for 300 chromosomes (routes) was the shortest and most efficient over 30 generations.

In transportation systems, the objective of route planning techniques has been revised from focusing on road directors to road users. As a result, the new transportation systems use advanced technologies to support drivers and provide them with the road information they need and the services they require to reduce traffic congestion and improve routing problems. In recent decades, numerous studies have been conducted on how to find an efficient and suitable route for vehicles, known as the vehicle routing problem (VRP). To identify the best route, VRP uses real-time information-acquired geographical information systems (GIS) tools.

This study aims to develop a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences.

Furthermore, developing a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences. An adaptive genetic algorithm (GA) is used to determine the optimal time route, taking into account factors that affect vehicle arrival times and cause delays. In addition, ArcGIS' Network Analyst tool is used to determine the best route based on the user's preferences using a real-time map.

This paper aims to focus on the spatial docking task of unmanned vehicles under ground conditions. The docking task of military unmanned vehicle application scenarios has strict requirements. Therefore, how to design a docking robot mechanism to achieve accurate docking between vehicles has become a challenge.

In this paper, first, the docking mechanism system is described, and the inverse kinematics model of the docking robot based on Stewart is established. Second, the genetic algorithm-based optimization method for multiobjective parameters of parallel mechanisms including workspace volume and mechanism flexibility is proposed to solve the problem of multiparameter optimization of parallel mechanism and realize the docking of unmanned vehicle space flexibility. The optimization results verify that the structural parameters meet the design requirements. Besides, the static and dynamic finite element analysis are carried out to verify the structural strength and dynamic performance of the docking robot according to the stiffness, strength, dead load and dynamic performance of the docking robot. Finally, taking the docking robot as the experimental platform, experiments are carried out under different working conditions, and the experimental results verify that the docking robot can achieve accurate docking tasks.

Experiments on the docking robot that the proposed design and optimization method has a good effect on structural strength and control accuracy. The experimental results verify that the docking robot mechanism can achieve accurate docking tasks, which is expected to provide technical guidance and reference for unmanned vehicles docking technology.

This research can provide technical guidance and reference for spatial docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

The purpose of this paper is to study the application of entropy based optimized fuzzy logic control for a real-time non-linear system. Optimization of the fuzzy membership function (MF) is one of the most explored areas for performance improvement of the fuzzy logic controllers (FLC). Conversely, majority of previous works are motivated on choosing an optimized shape for the MF, while on the other hand the support of fuzzy set is not accounted.

The proposed investigation provides the optimal support for predefined MFs by using genetic algorithms-based optimization of fuzzy entropy-based objective function.

The experimental results obtained indicate an improvement in the performance of the controller which includes improvement in error indices, transient and steady-state parameters. The applicability of proposed algorithm has been verified through real-time control of the twin rotor multiple-input, multiple-output system (TRMS).

The proposed algorithm has been used for the optimization of triangular sets, and can also be used for the optimization of other fussy sets, such as Gaussian, s-function, etc.

The proposed optimization can be combined with other algorithms which optimize the mathematical function (shape), and a potent optimization tool for designing of the FLC can be formulated.

This paper presents the application of a new optimized FLC which is tested for control of pitch and yaw angles in a TRMS. The performance of the proposed optimized FLC shows significant improvement when compared with standard references.

Glass is a brittle material produced from silica, which has fine material properties, Owing to its sophisticated material properties, glass has found wide application in various high-technological fields such as aviation, aerospace, communication, optics, biomedical and electronics. However, glass is known as difficult to machine material because of its tendency to brittle fracture during machining. This paper aims to investigate the effects of cutting parameters on surface quality and machining time during micro-milling of brittle glass components.

A comprehensive genetic algorithm-based optimization strategy is used for selection of process parameters such as cutting speed, feed rate and depth of cut. Effectiveness of the proposed strategy is validated by conducting micro-milling cutting experiments on soda-lime glass material.

Results showed that the generated surface quality drastically decrease with increase in the amount of removed material. Lower depth of cut and feed rate result in less amount of cracks formed on machined surface. Also, it is observed that the increase in cutting speed results in better surface quality. Having desired surface quality in shorter machining time directly reduces energy consumed during manufacturing, which is reducing environmental impact of glass parts.

The novelty of this research work lies in simultaneously considering the effects of cutting speed, feed rate, depth of cut on surface quality and machining time for micro-milling operation of brittle glass material. The model is able to find optimum process parameters for high surface quality and minimum machining time.