Project scheduling and financing should be adequately integrated during the planning phase to avoid probable cost overruns and delays. Many studies addressed the achievement of integration between project financing an...
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Project scheduling and financing should be adequately integrated during the planning phase to avoid probable cost overruns and delays. Many studies addressed the achievement of integration between project financing and scheduling using multi-objective optimization in particular. However, up to the knowledge of the authors, there is no research conducted to evaluate and assess the performance of the multi-objective optimization techniques employed in this domain. Thus, the current study developed a finance-based scheduling multi-objective optimization model for multiple projects using the elitist non-dominated sorting genetic algorithm (NSGA-II). Further, the obtained results were compared with the results obtained by solving the same problem in another study from the literature using the multi-objective optimization technique of strength Pareto evolutionary algorithm (SPEA). Benchmarking was conducted based on the quality of the obtained solutions and performance. The results indicated that the NSGA-II outperformed SPEA in most aspects with achieved improvements range from 1.7% to 98.2%.
This study explores the application of evolutionary generative algorithms in music production to preserve and enhance human creativity. By integrating human feedback into Differential Evolution algorithms, we produced...
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The development of ultra-intense laser-based sources of high energy ions is an important goal, with a variety of potential applications. One of the barriers to achieving this goal is the need to maximize the conversio...
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The development of ultra-intense laser-based sources of high energy ions is an important goal, with a variety of potential applications. One of the barriers to achieving this goal is the need to maximize the conversion efficiency from laser energy to ion energy. We apply a new approach to this problem, in which we use an evolutionary algorithm to optimize conversion efficiency by exploring variations of the target density profile with thousands of one-dimensional particle-in-cell (PIC) simulations. We then compare this 'optimal' target identified by the one-dimensional PIC simulations to more conventional choices, such as with an exponential scale length pre-plasma, with fully three-dimensional PIC simulations. The optimal target outperforms the conventional targets in terms of maximum ion energy by 20% and show a noticeable enhancement of conversion efficiency to >2 MeV ions. This target geometry enhances laser coupling to the electrons, while still allowing the laser to strongly reflect from an effectively thin target. These results underscore the potential for this statistics-driven approach to guide research into optimizing laser-plasma simulations and experiments.
Multitasking evolutionary algorithm (MTEA), which solves multiple optimization tasks simultaneously in a single run, has received considerable attention in the community of evolutionary computation, and several algori...
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Multitasking evolutionary algorithm (MTEA), which solves multiple optimization tasks simultaneously in a single run, has received considerable attention in the community of evolutionary computation, and several algorithms have been proposed in the literature. Unfortunately, knowledge transfer between constituent tasks may cause negative effect on algorithm performance, especially when the optimal solutions of all tasks are in different locations of the unified search space. To address this issue, an effective variable transformation strategy and the corresponding inverse transformation are proposed in multitasking optimization scenario. After using variable transformation strategy, the estimated optimal solutions of all tasks are both near the center point of the unified search space. More importantly, this strategy can enhance the task similarity, and then the effectiveness of knowledge transfer will probably be positive in this case, which can help us to improve the algorithm performance. Keeping this in mind, a multitasking evolutionary algorithm (named MTDE-VT) is realized as an instance by embedding the proposed variable transformation strategy into multitasking differential evolution. In MTDE-VT, the individuals in the original population are first transformed into new locations by the variable transformation strategy. Once the offspring is generated in the transformed unified search space, it must be transformed back to the original unified search space. The statistical analysis of experimental results on some multitasking optimization benchmark problems illustrates the superiority of the proposed MTDE-VT algorithm in terms of solution accuracy and robustness. Furthermore, the basic principle and the good parameter combination are also provided based on massive simulated data.
Recombination is a powerful way of generating new solutions in evolutionary algorithms. There are many ways to implement recombination. Traditional recombination operators do not use information about parents, evoluti...
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Recombination is a powerful way of generating new solutions in evolutionary algorithms. There are many ways to implement recombination. Traditional recombination operators do not use information about parents, evolutionary process, or models for variable interaction in order to find better ways to recombine solutions. Some modern recombination operators use information about parents and models for variable interaction, but they cannot always be efficiently applied. We propose to use an artificial neural network to compute the recombination mask, given two parents. Here, a radial basis function network (RBFN) is trained online using past successful recombination cases obtained during the optimization performed by the evolutionary algorithm. The RBFN crossover (RBFNX) is used together with other recombination operators (here, uniform crossover is employed). Applying RBFNX has O(N) time complexity, where N is the dimension of the optimization problem. Results of experiments with genetic algorithms, applied to two binary optimization problems, and evolution strategies, applied to continuous optimization test problems, indicate that RBFNX is generally able to improve the successful recombination rates. (C) 2020 Elsevier B.V. All rights reserved.
We report on the performance of three classes of evolutionary algorithms (genetic algorithms (GA), evolution strategies (ES) and covariance matrix adaptation evolution strategy (CMA-ES)) as a means to enhance searches...
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We report on the performance of three classes of evolutionary algorithms (genetic algorithms (GA), evolution strategies (ES) and covariance matrix adaptation evolution strategy (CMA-ES)) as a means to enhance searches in the method development spaces of 1D and 2D-chromatography. After optimisation of the design parameters of the different algorithms, they were benchmarked against the performance of a plain grid search. It was found that all three classes significantly outperform the plain grid search, especially in terms of the number of search runs needed to achieve a given separation quality. As soon as more than 100 search runs are needed, the ES algorithm clearly outperforms the GA and CMA-ES algorithms, with the latter performing very well for short searches (< 50 search runs) but being susceptible to convergence to local optima for longer searches. It was also found that the performance of the ES and GA algorithms, as well as the grid search, follow a hyperbolic law in the large search run number limit, such that the convergence rate parameter of this hyperbolic function can be used to quantify the difference in required number of search runs for these algorithms. In agreement with one's physical expectations, it was also found that the general advantage of the GA and ES algorithms over the grid search, as well as their mutual performance differences, grow with increasing difficulty of the separation problem. (C) 2020 Elsevier B.V. All rights reserved.
Accelerator-based fourth-generation light sources are utilized in a wide range of interdisciplinary applications such as nanotechnology, materials science, biosciences, and medicine. A hard X-ray free-electron laser (...
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Accelerator-based fourth-generation light sources are utilized in a wide range of interdisciplinary applications such as nanotechnology, materials science, biosciences, and medicine. A hard X-ray free-electron laser (FEL), as a state-of-the-art light source, was optimized using evolutionary algorithms for dedicated user applications such as X-ray Raman scattering (XRS), resonant inelastic X-ray scattering (RIXS), and X-ray emission spectroscopies (XES). Optimal parameter sets were obtained for an in-vacuum planar undulator driven by an 8 GeV electron beam. Performance parameters of self-amplified spontaneous emission (SASE) operation (i.e. optimized SASE performance parameters through evolutionary algorithms) were found to be consistent with operating X-ray FEL facilities around the world. It is shown that FEL characteristics for specific user experiments can be optimized by finding several evolutionary algorithm solutions within the range of 5 keV to 10 keV.
This paper presents the application of two classes of evolutionary algorithms (EA) to determine optimum design of Single-Phase Switched Reluctance Machine (SPSRM). The EA used is Genetic algorithms (GA) and Differenti...
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This paper presents the application of two classes of evolutionary algorithms (EA) to determine optimum design of Single-Phase Switched Reluctance Machine (SPSRM). The EA used is Genetic algorithms (GA) and Differential Evolution (DE). Due to sensitivity of the output torque to the stator and rotor pole arcs, these are selected as design variables for a multi-objective optimization with the objective of maximizing average torque and torque density, and minimizing copper loss. The proposed optimization is tested on a 4/4 1,25 kW SPSRM, and the results of both algorithms are compared. The performance of the optimized motor is compared to the initial motor through the finite element analysis. The results show improvement in both efficiency and output torque.
In numerical computation, finding multiple roots of nonlinear equation systems (NESs) in a single run is a fundamental and difficult problem. Recently, evolutionary algorithms (EAs) have been applied to solve NESs. Ho...
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In numerical computation, finding multiple roots of nonlinear equation systems (NESs) in a single run is a fundamental and difficult problem. Recently, evolutionary algorithms (EAs) have been applied to solve NESs. However, due to the diversity preservation mechanism that EAs use, the accuracy of the roots may be reduced. To remedy this drawback, we propose a generic framework of memetic niching-based EA, referred to as MENI-EA. The main features of the framework are: i) the numerical method for a NES is integrated into an EA to obtain highly accurate roots;ii) the niching technique is employed to improve the diversity of the population;iii) different roots of the NESs are located simultaneously in a singe run;and iv) different numerical methods and different niching techniques can be used in the framework. To evaluate the performance of our approach, thirty NESs were chosen from the literature as the test suite. Experimental results show that the proposed approach is capable of yielding promising performance for different NESs in both the root ratio and success rate. (C) 2020 Elsevier Ltd. All rights reserved.
Households equipped with distributed energy resources, such as storage units and renewables, open the possibility of self-consumption of on-site generation, sell energy to the grid, or do both according to the context...
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Households equipped with distributed energy resources, such as storage units and renewables, open the possibility of self-consumption of on-site generation, sell energy to the grid, or do both according to the context of operation. In this paper, a model for optimizing the energy resources of households by an energy service provider is developed. We consider houses equipped with technologies that support the actual reduction of energy bills and therefore perform demand response actions. A mathematical formulation is developed to obtain the optimal scheduling of household devices that minimizes energy bill and demand response curtailment actions. In addition to the scheduling model, the innovative approach in this paper includes evolutionary algorithms used to solve the problem under two optimization approaches: (a) the non-parallel approach combine the variables of all households at once;(b) the parallel-based approach takes advantage of the independence of variables between households using a multi-population mechanism and independent optimizations. Results show that the parallel-based approach can improve the performance of the tested evolutionary algorithms for larger instances of the problem. Thus, while increasing the size of the problem, namely increasing the number of households, the proposed methodology will be more advantageous. Overall, vortex search overcomes all other tested algorithms (including the well-known differential evolution and particle swarm optimization) achieving around 30% better fitness value in all the cases, demonstrating its effectiveness in solving the proposed problem.
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