Nowadays, the solution of multiobjective optimization problems in aeronautical and aerospace engineering has become a standard practice. These two fields offer highly complex search spaces with different sources of di...
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Nowadays, the solution of multiobjective optimization problems in aeronautical and aerospace engineering has become a standard practice. These two fields offer highly complex search spaces with different sources of difficulty, which are amenable to the use of alternative search techniques such as metaheuristics, since they require little domain information to operate. From the several metaheuristics available, multiobjective evolutionary algorithms (MOEAs) have become particularly popular, mainly because of their availability, ease of use, and flexibility. This paper presents a taxonomy and a comprehensive review of applications of MOEAs in aeronautical and aerospace design problems. The review includes both the characteristics of the specific MOEA adopted in each case, as well as the features of the problems being solved with them. The advantages and disadvantages of each type of approach are also briefly addressed. We also provide a set of general guidelines for using and designing MOEAs for aeronautical and aerospace engineering problems. In the final part of the paper, we provide some potential paths for future research, which we consider promising within this area.
Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously. The complexity of the optimal asset allocation prob...
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Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously. The complexity of the optimal asset allocation problem increases with an increase in the number of assets available to select from for investing. The optimization problem becomes computationally challenging when there are more than a few hundreds of assets to select from. To reduce the complexity of large-scale portfolio optimization, two asset preselection procedures that consider return and risk of individual asset and pairwise correlation to remove assets that may not potentially be selected into any portfolio are proposed in this paper. With these asset preselection methods, the number of assets considered to be included in a portfolio can be increased to thousands. To test the effectiveness of the proposed methods, a NormalizedMultiobjective evolutionary Algorithmbased onDecomposition (NMOEA/D) algorithmand several other commonly usedmultiobjective evolutionary algorithms are applied and compared. Six experiments with different settings are carried out. The experimental results show that with the proposed methods the simulation time is reduced while return-risk trade-off performances are significantly improved. Meanwhile, the NMOEA/D is able to outperform other compared algorithms on all experiments according to the comparative analysis.
Recent work has been devoted to study the use of multiobjective evolutionary algorithms (MOEAs) in stock portfolio optimization, within a common mean-variance framework. This article proposes the use of a more appropr...
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Recent work has been devoted to study the use of multiobjective evolutionary algorithms (MOEAs) in stock portfolio optimization, within a common mean-variance framework. This article proposes the use of a more appropriate framework, mean-semivariance framework, which takes into account only adverse return variations instead of overall variations. It also proposes the use and comparison of established technical analysis (TA) indicators in pursuing better outcomes within the risk-return relation. Results show there is some difference in the performance of the two selected MOEAs non-dominated sorting genetic algorithm II (NSGA II) and strength pareto evolutionary algorithm 2 (SPEA 2) - within portfolio optimization. In addition, when used with four TA based strategies relative strength index (RSI), moving average convergence/divergence (MACD), contrarian bollinger bands (CBB) and bollinger bands (BB), the two selected MOEAs achieve solutions with interesting in-sample and out-of-sample outcomes for the BB strategy. (C) 2017 Elsevier Ltd. All rights reserved.
Alignment of each optical element at a synchrotron beamline takes days, even weeks, for each experiment costing valuable beam time. evolutionary algorithms (EAs), efficient heuristic search methods based on Darwinian ...
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Alignment of each optical element at a synchrotron beamline takes days, even weeks, for each experiment costing valuable beam time. evolutionary algorithms (EAs), efficient heuristic search methods based on Darwinian evolution, can be utilized for multi-objective optimization problems in different application areas. In this study, the flux and spot size of a synchrotron beam are optimized for two different experimental setups including optical elements such as lenses and mirrors. Calculations were carried out with the X-ray Tracer beamline simulator using swarm intelligence (SI) algorithms and for comparison the same setups were optimized with EAs. The EAs and SI algorithms used in this study for two different experimental setups are the Genetic Algorithm (GA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). While one of the algorithms optimizes the lens position, the other focuses on optimizing the focal distances of Kirkpatrick-Baez mirrors. First, mono-objective evolutionary algorithms were used and the spot size or flux values checked separately. After comparison of mono-objective algorithms, the multi-objective evolutionary algorithm NSGA-II was run for both objectives - minimum spot size and maximum flux. Every algorithm configuration was run several times for Monte Carlo simulations since these processes generate random solutions and the simulator also produces solutions that are stochastic. The results show that the PSO algorithm gives the best values over all setups.
The aim of this paper is the development of foundations for evolutionary computations. To achieve this goal, a mathematical model of evolutionary automata is introduced and studied. The main classes of evolutionary au...
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The aim of this paper is the development of foundations for evolutionary computations. To achieve this goal, a mathematical model of evolutionary automata is introduced and studied. The main classes of evolutionary automata considered in this paper are evolutionary Turing machines and evolutionary inductive Turing machines. Various subclasses and modes of evolutionary computation are defined. Problems of existence of universal objects in these classes are explored. Relations between Turing machines, inductive Turing machines, evolutionary Turing machines, and evolutionary inductive Turing machines are investigated.
Helicopter systems are considered a complex and challenging control problem due to strong couplings and high non-linearities. In this paper, simulated annealing (SA), as one of the leading methods in search and optimi...
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Helicopter systems are considered a complex and challenging control problem due to strong couplings and high non-linearities. In this paper, simulated annealing (SA), as one of the leading methods in search and optimization, is applied to tune a multivariable controller of a lab-scale helicopter system. The lab-scale helicopter system is a multivariable experimental aerodynamic test rig that resembles the behaviour of a real helicopter. The control objectives are quickly to reach a desired position or track a trajectory. A centralized cross-coupled PID controller is used to achieve these objectives. First, SA optimizations are carried out with 24 different initial configurations. Then, the best results of these SA configurations are compared with other controllers obtained with evolutionary algorithms (EAs) of genetic algorithms (GAs), modified particle swarm optimization (MPSO) and differential evolution (DE). The comparisons are based on statistical measures of 20 independent trials, non-linear computer simulations of different input signals and real-time measurements for various commands of positions or trajectories. Results show that SA obtained the best performance index and acceptable time-domain performance on reaching hovering point, following a step command and tracking a sine trajectory compared with the investigated EAs.
In this study, a single autonomous underwater vehicle (AUV) aims to rendezvous with a submerged leader recovery vehicle through a cluttered and variable operating field. The rendezvous problem is transformed into a No...
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In this study, a single autonomous underwater vehicle (AUV) aims to rendezvous with a submerged leader recovery vehicle through a cluttered and variable operating field. The rendezvous problem is transformed into a Nonlinear Optimal Control Problem (NOCP) and then numerical solutions are provided. A penalty function method is utilized to combine the boundary conditions, vehicular and environmental constraints with the performance index that is final rendezvous time. Four evolutionary based path planning methods namely Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO), Differential Evolution (DE), and Firefly Algorithm (FA) are employed to establish a reactive planner module and provide a numerical solution for the proposed NOCP. The objective is to synthesize and analyze the performance and capability of the mentioned methods for guiding an AUV from an initial loitering point toward the rendezvous through a comprehensive simulation study. The proposed planner module entails a heuristic for refining the path considering situational awareness of environment, encompassing static and dynamic obstacles within a spatiotemporal current fields. The planner thus needs to accommodate the unforeseen changes in the operating field such as emergence of unpredicted obstacles or variability of current field and turbulent regions. The simulation results demonstrate the inherent robustness and efficiency of the proposed planner for enhancing a vehicle's autonomy so as to enable it to reach the desired rendezvous. The advantages and shortcoming of all utilized methods are also presented based on the obtained results. (C) 2017 Elsevier B.V. All rights reserved.
This paper compares the effectiveness of five state-of-the-art multiobjective evolutionary algorithms (MOEAs) together with a steady state evolutionary algorithm on the mean-variance cardinality constrained portfolio ...
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This paper compares the effectiveness of five state-of-the-art multiobjective evolutionary algorithms (MOEAs) together with a steady state evolutionary algorithm on the mean-variance cardinality constrained portfolio optimization problem (MVCCPO). The main computational challenges of the model are due to the presence of a nonlinear objective function and the discrete constraints. The MOEAs considered are the Niched Pareto genetic algorithm 2 (NPGA2), non-dominated sorting genetic algorithm II (NSGA-II), Pareto envelope-based selection algorithm (PESA), strength Pareto evolutionary algorithm 2 (SPEA2). and e-multiobjective evolutionary algorithm (e-MOEA). The computational comparison was performed using formal metrics proposed by the evolutionary multiobjective optimization community on publicly available data sets which contain up to 2196 assets. (C) 2011 Elsevier Ltd. All rights reserved.
Topology optimization has evolved rapidly since the late 1980s. The optimization of the geometry and topology of structures has a great impact on its performance, and the last two decades have seen an exponential incr...
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Topology optimization has evolved rapidly since the late 1980s. The optimization of the geometry and topology of structures has a great impact on its performance, and the last two decades have seen an exponential increase in publications on structural optimization. This has mainly been due to the success of material distribution methods, originating in 1988, for generating optimal topologies of structural elements. Previous methods suffered from mathematical complexity and a limited scope for applicability, however with the advent of increased computational power and new techniques topology optimization has grown into a design tool used by industry. There are two main fields in structural topology optimization, gradient based, where mathematical models are derived to calculate the sensitivities of the design variables, and non gradient based, where material is removed or included using a sensitivity function. Both fields have been researched in great detail over the last two decades, to the point where structural topology optimization has been applied to real world structures. It is the objective of this review paper to present an overview of the developments in non gradient based structural topology and shape optimization, with a focus on evolutionary algorithms, which began as a non gradient method, but have developed to incorporate gradient based techniques. Starting with the early work and development of the popular algorithms and focusing on the various applications. The sensitivity functions for various optimization tasks are presented and real world applications are analyzed. The article concludes with new applications of topology optimization and applications in various engineering fields.
The design of a Compact Dual-band Equatorial helix antenna using Computational Electromagnetic Methods together with multiobjective optimization algorithms is presented. These antennas are used for Telemetry, Tracking...
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The design of a Compact Dual-band Equatorial helix antenna using Computational Electromagnetic Methods together with multiobjective optimization algorithms is presented. These antennas are used for Telemetry, Tracking, and Control of satellites from the terrain base station. In order to optimize the parameters an antenna, a simulation-optimization process is shown along a real case study. The parameters of the antenna that fulfills the radiation patterns needed for the communication are obtained using a simulation tool called MONURBS together with two well-known multiobjective algorithms: NSGA-II and SPEA-2. In this paper, a comparison with previous designs and the antenna prototype is presented, showing that this approach can obtain multiple valid solutions and accelerate the design process.
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