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A Methodology Based on evolutionary algorithms to Solve a Dynamic Pickup and Delivery Problem Under a Hybrid Predictive Control Approach

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TRANSPORTATION SCIENCE 2015年第2期49卷 239-253页

作者： Munoz-Carpintero, Diego Saez, Doris Cortes, Cristian E. Nunez, Alfredo Univ Chile Dept Elect Engn Santiago 8370451 Chile Univ Chile Dept Civil Engn Santiago 8370449 Chile Delft Univ Technol Sect Rd & Railway Engn NL-2628 CN Delft Netherlands

This paper presents a methodology based on generic evolutionary algorithms to solve a dynamic pickup and delivery problem formulated under a hybrid predictive control approach. The solution scheme is designed to support the dispatcher of a dial-a-ride service, where quick and efficient real-time solutions are needed. The scheme considers different configurations of particle swarm optimization and genetic algorithms within a proposed ad-hoc methodology to solve in real time the nonlinear mixed-integer optimization problem related with the hybrid predictive control approach. These consist of different techniques to handle the operational constraints (penalization, Baldwinian, and Lamarckian repair) and encodings (continuous and integer). For parameter tuning, a new approach based on multiobjective optimization is proposed and used to select and study some of the evolutionary algorithms. The multiobjective feature arises when deciding the parameters with the best trade-off between performance and computational effort. Simulation results are presented to compare the different schemes proposed and to advise conditions for the application of the method in real instances.

关键词： predictive control dynamic pickup and delivery problem evolutionary algorithms

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Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using evolutionary algorithms

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IEEE ACCESS 2023年 11卷 3044-3061页

作者： Fernandez, Eduardo Navarro, Jorge Solares, Efrain Coello, Carlos A. Coello Diaz, Raymundo Flores, Abril Univ Autonoma Coahuila Fac Contaduria & Adm Torreon 27000 Mexico Univ Autonoma Sinaloa Fac Informat Culiacan 80040 Mexico Ctr Invest & Estudios Avanzados IPN Dept Comp Ciudad De Mexico 07360 Mexico Tecnol Monterrey Sch Finance & Adm Monterrey 64849 Mexico

Multicriteria sorting involves assigning the objects of decisions (actions) into $a$ priori known ordered classes considering the preferences of a decision maker (DM). Two new multicriteria sorting methods were recently proposed by the authors. These methods are based on a novel approach called interval-based outranking which provides the methods with attractive practical and theoretical characteristics. However, as is well known, defining parameter values for methods based on the outranking approach is often very difficult. This difficulty arises not only from the large number of parameters and the DM's lack of familiarity with them, but also from imperfectly known (even missing) information. Here, we address: i) how to elicit the parameter values of the two new methods, and ii) how to incorporate imperfect knowledge during the elicitation. We follow the preference disaggregation paradigm and use evolutionary algorithms to address it. Our proposal performs very well in a wide range of computational experiments. Interesting findings are: i) the method restores the assignment examples with high effectiveness using only three profiles in each limiting boundary or representative actions per class;and ii) the ability to appropriately assign unknown actions can be greatly improved by increasing the number of limiting profiles.

关键词： evolutionary algorithms imperfect information multiple criteria analysis multiple criteria ordinal classification outranking methods

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CO₂ storage well rate optimisation in the Forties sandstone of the Forties and Nelson reservoirs using evolutionary algorithms and upscaled geological models

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INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL 2016年 50卷 1-13页

作者： Babaei, Masoud Pan, Indranil Korre, Anna Shi, Ji-Quan Govindan, Rajesh Durucan, Sevket Quinn, Martyn Univ London Imperial Coll Sci Technol & Med Royal Sch Mines Dept Earth Sci & Engn London SW7 2BP England British Geol Survey Murchison House Edinburgh EH9 3LA Midlothian Scotland

Optimisation is particularly important in the case of CO2 storage in saline aquifers, where there are various operational objectives to be achieved. The storage operation design process must also take various uncertainties into account, which result in adding computational overheads to the optimisation calculations. To circumvent this problem upscaled models with which computations are orders of magnitude less time-consuming can be used. Nevertheless, a grid resolution, which does not compromise the accuracy, reliability and robustness of the optimisation in an upscaled model must be carefully determined. In this study, a 3D geological model based on the Forties and Nelson hydrocarbon fields and the adjacent saline aquifer, is built to examine the use of coarse grid resolutions to design an optimal CO2 storage solution. The optimisation problem is to find optimal allocation of total CO2 injection rate between existing wells. A simulation template of an area encompassing proximal-type reservoirs of the Forties-Montrose High is considered. The detailed geological model construction leads to computationally intensive simulations for CO2 storage design, so that upscaling is rendered unavoidable. Therefore, an optimal grid resolution that successfully trades accuracy against computational run-time is sought after through a thorough analysis of the optimisation results for different resolution grids. The analysis is based on a back-substitution of the optimisation solutions obtained from coarse-scale models into the fine-scale model, and comparison between these back-substitution models and direct use of fine-scale model to conduct optimisation. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： CO2 storage optimisation evolutionary algorithms Upscaled models Optimal grid resolution Forties sandstone Forties and Nelson reservoirs

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A computational scaling analysis of multiobjective evolutionary algorithms in long-term groundwater monitoring applications

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ADVANCES IN WATER RESOURCES 2007年第3期30卷 408-419页

作者： Kollat, J. B. Reed, P. M. Penn State Univ Dept Civil & Environm Engn University Pk PA 16802 USA

This study contributes a detailed assessment of how increasing problem sizes (measured in terms of the number of decision variables being considered) impacts the computational complexity of using multiple objective evolutionary algorithms (MOEAs) to solve long-term groundwater monitoring (LTM) applications. The epsilon-dominance non-dominated sorted genetic algorithm II (epsilon-NSGAII), which has been shown to be an efficient and reliable MOEA, was chosen for the computational scaling study. Four design objectives were chosen for the analysis: (i) sampling cost, (ii) contaminant concentration estimation error, (iii) local uncertainty, and (iv) contaminant mass estimation error. The true Pareto-optimal solution set was generated for 18-25 well LTM test cases in order to provide for rigorous algorithm performance assessment for problems of increasing size. Results of the study indicate that the epsilon-NSGAII exhibits quadratic computational scaling with increasing LTM problem size. However, if the user is willing to accept an approximation to the Pareto-optimal solution set, F-dominance can be used to reduce the computational scaling of MOEAs to be linear with increasing problem sizes. This study provides a basis for advancing the size and scope of water resources problems that can be effectively solved using MOEAs. (C) 2006 Published by Elsevier Ltd.

关键词： long-term groundwater monitoring evolutionary algorithms multi-objective optimization computational scaling

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An Optimized Nonlinear Controller Design for Boiler-Turbine System Using evolutionary algorithms

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IETE JOURNAL OF RESEARCH 2018年第4期64卷 451-462页

作者： Piraisoodi, T. Iruthayarajan, M. Willjuice Kadhar, K. Mohaideen Abdul Natl Engn Coll Dept Elect & Elect Engn Kovilpatti Tamil Nadu India Dr Mahalingam Coll Engn & Technol Pollachi Tamil Nadu India

This paper investigates an optimized design of newly developed nonlinear controller called finite-time convergent controller to a third-order boiler-turbine dynamics. The third-order boiler-turbine dynamics only includes highly nonlinear and critical parameters of thermal plant like drum pressure, electric power and drum level;the design of controller is always a complicated task. The present work explores the possibility of application of newly developed finite-time convergent controller to a third-order boiler-turbine dynamics. The novelty of the newly developed finite-time convergent controller is complete utilization of system nonlinearities instead of cancelling or linearizing them. Also, the finite-time convergent controller ensures robustness and fast convergence. To achieve optimal performance, the tuning parameters involved in finite-time convergent controller have been optimized using evolutionary algorithm techniques. To validate the control performance of an optimized nonlinear controller design, simulations have been conducted using various evolutionary algorithm techniques and the results are reported as various case studies. To compare the performance of proposed optimized finite-time convergent controller, the fuzzy logic controller has also been designed using ANFIS for boiler-turbine system and the results are reported.

关键词： Boiler-turbine system evolutionary algorithms Finite-time convergent controller Fuzzy logic controller: Optimal tuning parameters

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Supersaturated experimental designs.: New approaches to building and using it -: Part I.: Building optimal supersaturated designs by means of evolutionary algorithms

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2000年第2期52卷 167-182页

作者： Cela, R Martínez, E Carro, AM Univ Santiago Compostela Fac Quim Dept Quim Anal Nutr & Bromatol E-15706 Santiago Spain

A new procedure for the building and selection of supersaturated design matrices is presented. The procedure is useful in generating screening experimental designs in the range 8-22 runs. An evolutionary algorithm is used to select between all possible candidate columns, which in turn, are a Function of the selected run number, those producing the optimal matrix. Optimality, as defined by three sequentially applied common criteria (Es-2. n0, m0), is used as fitness functions in the evolution algorithm. The problem in the construction of an optimal design matrix as a particular subset of a much larger universal set of potential solutions needs specially problem-adapted genetic operators. Several have been tested and applied. To make the procedure practical, a toolkit has bern developed which allows, in a reasonable computation time, to build and select well characterised experimental supersaturated designs for a given run and factor numbers. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： supersaturated experimental designs optimal matrix evolutionary algorithms

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Decision-Making with Multiple Interacting Criteria: An Indirect Elicitation of Preference Parameters Using evolutionary algorithms

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2022年第1期21卷 381-404页

作者： Fernandez, Eduardo Navarro, Jorge Solares, Efrain Univ Autonoma Coahuila Fac Contaduria & Adm Blvd Revoluc Oriente 151 Torreon 27000 Mexico Univ Autonoma Sinaloa Fac Informat Josefa Ortiz de Dominguez S-N Culiacan 80040 Sinaloa Mexico

Decision-making problems often require characterization of alternatives through multiple criteria. In contexts where some of these criteria interact, the decision maker (DM) must consider the interaction effects during the aggregation of criteria scores. The well-known ELECTRE (ELimination Et Choix Traduisant la REalite) methods were recently improved to deal with interacting criteria fulfilling several relevant properties, addressing the main types of interaction, and retaining most of the fundamental characteristics of the classical methods. An important criticism to such a family of methods is that defining its parameter values is often difficult and can involve significant challenges and high cognitive effort for the DM;this is exacerbated in the improved version whose parameters are even less intuitive. Here, we describe an evolutionary-based method in which parameter values are inferred by exploiting easy-to-make decisions made or accepted by the DM, thereby reducing his/her cognitive effort. A genetic algorithm is proposed to solve a regression-inspired nonlinear optimization problem. To the best of our knowledge, this is the first paper addressing the indirect elicitation of the ELECTRE model's parameters with interacting criteria. The proposal is assessed through both in-sample and out-of-sample experiments. Statistical tests indicate robustness of the proposal in terms of the number of criteria and their possible interactions. Results show almost perfect effectiveness to reproduce the DM's preferences in all situations.

关键词： Multi-criteria decision outranking methods evolutionary algorithms interacting criteria

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Swarm and evolutionary algorithms in Image Compression by F-Transform

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IEEE ACCESS 2023年 11卷 25991-26003页

作者： Linh, Nguyen Le Toan Nhat Diep, Quoc Bao Ton Duc Thang Univ Fac Math & Stat Appl Anal Res Grp Ho Chi Minh City Vietnam Van Lang Univ Fac Mech Elect & Comp Engn Sch Technol Ho Chi Minh City 700000 Vietnam

This article investigates the application of swarm and evolutionary algorithms, namely the SOMA, DE, and GA, for optimizing the F-transform-based image compression. To do this, we introduce the cost function, evaluating the approximation of decompressed images to the original image, concerning the parameters that control the approximation quality of the F-transform. This function is then minimized by the selected algorithms to find optimal settings for image compression and decompression. We design experiments to compare the performance of the original F-transform method and the methods optimized by SOMA, DE, and GA on a dataset of 10 pictures. In all considered cases, the results obtained with the optimized method completely surpass those obtained by the original one. We also apply a statistical test (called Wilcoxon signed-rank test) for ranking the performance of selected algorithms in this issue. The results show that the SOMA and DE perform well in cases where the compressed image sizes are small. However, the GA algorithm shows outperformance in comparison with the others in more complicated cases where the compressed image size is bigger. The outperformance of the GA is in terms of decompression quality and computation time. Finally, we provide a visual comparison between the original F-transform based method and the method optimized by the GA, tested on a 128 x 128 picture. The decompressed image by the latter is much sharper and more detailed than that obtained by the former.

关键词： Image compression swarm intelligence evolutionary algorithms numerical optimization

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A framework for evolutionary algorithms based on Charles Sanders Peirce's evolutionary semiotics

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INFORMATION SCIENCES 2013年 236卷 93-108页

作者： Akhtar, Junaid Koshul, Basit Bilal Awais, Mian Muhammad Lahore Univ Management Sci Syed Babar Ali Sch Sci & Engn Dept Comp Sci Lahore 54000 Pakistan Lahore Univ Management Sci Mushtaq Ahmad Gurmani Sch Humanities Social Sci Dept Humanities & Social Sci Lahore 54000 Pakistan

One of the objectives of evolutionary Computation (EC) has been to understand the processes of natural evolution and then model them algorithmically. Hans-Paul Schwefel, in his 1997 paper on the future challenges for EC argues that the more an algorithm models natural evolution at work in the universe, the better it will perform (even in terms of function optimization). There is enough data to suggest that slight differences in the understanding of the natural evolution can cause the associated evolutionary algorithms (EA) to change characteristically. The present paper tests Schwefel's hypothesis against Charles Sanders Peirce's theory which places semiotics, the theory of signs, at the heart of universal evolution. This course is followed because of three primary reasons. Firstly, Peirce has not been seriously tested in EC, although there have been EA based on other theories and sub-theories. Secondly, Peirce's universal theory, by not being restricted to biological evolution alone, qualifies for Schwefel's hypothesis, perhaps more than most other theories that have already been modeled algorithmically. But most importantly because, in experimental terms, it warrants an original claim that Peirce's insights are useful in improving the existing EA in computer science, as Peircean EA can potentially solve some of the major problems in this area such as the loss of diversity, stagnation, or premature convergence. This paper. provides a novel framework and consequently a simple algorithm based on Peirce's theory of evolution, and tests it extensively against a benchmark set of mathematical problems of varying dimensions and complexity. Comparative results with classical and advanced EA form another significant part of the paper, and help in strengthening the viability of Schwefel-Peirce hypothesis for EC. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Charles Sanders Peirce evolutionary algorithms Semiotics Stagnation

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Parameter tuning evolutionary algorithms for runtime versus cost trade-off in a cloud computing environment

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SIMULATION MODELLING PRACTICE AND THEORY 2018年 89卷 195-205页

作者： Andersson, Martin Ng, Amos H. C. Univ Skovde Sch Engn Sci S-54128 Skovde Sweden

The runtime of an evolutionary algorithm can be reduced by increasing the number of parallel evaluations. However, increasing the number of parallel evaluations can also result in wasted computational effort since there is a greater probability of creating solutions that do not contribute to convergence towards the global optimum. A trade-off, therefore, arises between the runtime and computational effort for different levels of parallelization of an evolutionary algorithm. When the computational effort is translated into cost, the trade-off can be restated as runtime versus cost. This trade-off is particularly relevant for cloud computing environments where the computing resources can be exactly matched to the level of parallelization of the algorithm, and the cost is proportional to the runtime and how many instances that are used. This paper empirically investigates this trade-off for two different evolutionary algorithms, NSGA-II and differential evolution (DE) when applied to a multi-objective discrete-event simulation (DES) problem. Both generational and steady-state asynchronous versions of both algorithms are included. The approach is to perform parameter tuning on a simplified version of the DES model. A subset of the best configurations from each tuning experiment is then evaluated on a cloud computing platform. The results indicate that, for the included DES problem, the steady-state asynchronous version of each algorithm provides a better runtime versus cost trade-off than the generational versions and that DE outperforms NSGA-II.

关键词： evolutionary algorithms NSGA-II Differential evolution Discrete-event simulation

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