State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Wind Integrated Thermal Unit Commitment Solution Using Grey Wolf Optimizer

The augment of ecological shield and the progressive exhaustion of traditional fossil energy sources have increased the interests in integrating renewable energy sources into existing power system. Wind power is becoming worldwide a significant component of the power generation portfolio. Profuse literature have been reported for the thermal Unit Commitment (UC) solution. In this work, the UC problem has been formulated by integrating wind power generators along with thermal power system. The Wind Generator Integrated UC (WGIUC) problem is more complex in nature, that necessitates a promising optimization tool. Hence, the modern bio-inspired algorithm namely, Grey Wolf Optimization (GWO) algorithm has been chosen as the main optimization tool and real coded scheme has been incorporated to handle the operational constraints. The standard test systems are used to validate the potential of the GWO algorithm. Moreover, the ramp rate limits are also included in the mathematical WGIUC formulation. The simulation results prove that the intended algorithm has the capability of obtaining economical resolutions with good solution quality

Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes

This paper presents a new systematic approach to the optimization of both design and manufacturing variables across a multi-step production process. The approach assumes a generic manufacturing process in which an initial Near Net Shape (NNS) process is followed by a limited number of finishing operations. In this context the optimisation problem becomes a multi-variable problem in which the aim is to optimize by minimizing cost (or time) and improving technological performances (e.g. turning force). To enable such computation a methodology, named Conditional Design Optimization (CoDeO) is proposed which allows the modelling and simultaneous optimization of process parameters and product design (geometric variables), using single or multi-criteria optimization strategies. After investigation of CoDeO’s requirements, evolutionary algorithms, in particular Genetic Algorithms, are identified as the most suitable for overall NNS manufacturing chain optimization The CoDeO methodology is tested using an industrial case study that details a process chain composed of casting and machining processes. For the specific case study presented the optimized process resulted in cost savings of 22% (corresponding to equivalent machining time savings) and a 10% component weight reduction

Solution of the Multi-objective Economic and Emission Load Dispatch Problem Using Adaptive Real Quantum Inspired Evolutionary Algorithm

Economic load dispatch is a complex and significant problem in power generation. The inclusion of emission with economic operation makes it a Multi-objective economic emission load dispatch (MOEELD) problem. So it is a tough task to resolve a constrained MOEELD problem with antagonistic multiple objectives of emission and cost. Evolutionary Algorithms (EA) have been widely used for solving such complex multi-objective problems. However, the performance of EAs on such problems is dependent on the choice of the operators and their parameters, which becomes a complex issue to solve in itself. The present work is carried out to solve a Multi-objective economic emission load dispatch problem using a Multi-objective adaptive real coded quantum-inspired evolutionary algorithm (MO-ARQIEA) with gratifying all the constraints of unit and system. A repair-based constraint handling and adaptive quantum crossover operator (ACO) are used to satisfy the constraints and preserve the diversity of the suggested approach. The suggested approach is evaluated on the IEEE 30-Bus system consisting of six generating units. These results obtained for different test cases are compared with other reputed and well-known techniques

Hybrid bootstrap-based approach with binary artificial bee colony and particle swarm optimization in Taguchi's T-Method

Taguchi's T-Method is one of the Mahalanobis Taguchi System (MTS)-ruled prediction techniques that has been established specifically but not limited to small, multivariate sample data. When evaluating data using a system such as the Taguchi's T-Method, bias issues often appear due to inconsistencies induced by model complexity, variations between parameters that are not thoroughly configured, and generalization aspects. In Taguchi's T-Method, the unit space determination is too reliant on the characteristics of the dependent variables with no appropriate procedures designed. Similarly, the least square-proportional coefficient is well known not to be robust to the effect of the outliers, which indirectly affects the accuracy of the weightage of SNR that relies on the model-fit accuracy. The small effect of the outliers in the data analysis may influence the overall performance of the predictive model unless more development is incorporated into the current framework. In this research, the mechanism of improved unit space determination was explicitly designed by implementing the minimum-based error with the leave-one-out method, which was further enhanced by embedding strategies that aim to minimize the impact of variance within each parameter estimator using the leave-one-out bootstrap (LOOB) and 0.632 estimates approaches. The complexity aspect of the prediction model was further enhanced by removing features that did not provide valuable information on the overall prediction. In order to accomplish this, a matrix called Orthogonal Array (OA) was used within the existing Taguchi's T-Method. However, OA's fixed-scheme matrix, as well as its drawback in coping with the high-dimensionality factor, leads to a sub- optimal solution. On the other hand, the usage of SNR, decibel (dB) as its objective function proved to be a reliable measure. The architecture of a Hybrid Binary Artificial Bee Colony and Particle Swarm Optimization (Hybrid Binary ABC-PSO), including the Binary Bitwise ABC (BitABC) and Probability Binary PSO (PBPSO), has been developed as a novel search engine that helps to cater the limitation of OA. The SNR (dB) and mean absolute error (MAE) were the main part of the performance measure used in this research. The generalization aspect was a fundamental addition incorporated into this research to control the effect of overfitting in the analysis. The proposed enhanced parameter estimators with feature selection optimization in this analysis had been tested on 10 case studies and had improved predictive accuracy by an average of 46.21% depending on the cases. The average standard deviation of MAE, which describes the variability impact of the optimized method in all 10 case studies, displayed an improved trend relative to the Taguchi’s T-Method. The need for standardization and a robust approach to outliers is recommended for future research. This study proved that the developed architecture of Hybrid Binary ABC-PSO with Bootstrap and minimum-based error using leave-one-out as the proposed parameter estimators enhanced techniques in the methodology of Taguchi's T-Method by effectively improving its prediction accuracy

A multi objective volleyball premier league algorithm for green scheduling identical parallel machines with splitting jobs

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms