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A multi-objective memetic inverse solver reinforced by local optimization methods

JOURNAL OF COMPUTATIONAL SCIENCE 2017年 18卷 85-94页

作者： Gajda-Zagorska, Ewa Schaefer, Robert Smolka, Maciej Pardo, David Alvarez-Aramberri, Julen IST Austria Klosterneuburg Austria AGH Univ Sci & Technol Krakow Poland AGH Univ Sci & Technol Dept Comp Sci Fac Comp Sci Elect & Telecommun Krakow Poland AGH Univ Sci & Technol Dept Comp Sci Adapt Algorithms & Syst Grp Krakow Poland Univ Basque Country UPV EHU Dept Appl Math Stat & Operat Res Leioa Bizkaia Spain Basque Ctr Appl Math Bilbao Spain Basque Ctr Appl Math Computat & Appl Math Bilbao Spain Ikerbasque Basque Fdn Sci Bilbao Spain

We propose a new memetic strategy that can solve the multi-physics, complex inverse problems, formulated as the multi-objective optimization ones, in which objectives are misfits between the measured and simulated states of various governing processes. The multi-deme structure of the strategy allows for both, intensive, relatively cheap exploration with a moderate accuracy and more accurate search many regions of Pareto set in parallel. The special type of selection operator prefers the coherent alternative solutions, eliminating artifacts appearing in the particular processes. The additional accuracy increment is obtained by the parallel convex searches applied to the local scalarizations of the misfit vector. The strategy is dedicated for solving ill-conditioned problems, for which inverting the single physical process can lead to the ambiguous results. The skill of the selection in artifact elimination is shown on the benchmark problem, while the whole strategy was applied for identification of oil deposits, where the misfits are related to various frequencies of the magnetic and electric waves of the magnetotelluric measurements. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Inverse problems multi-objective optimization methods Memetic algorithms

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A hybrid multi-objective solution approach for a reliable truck-drone routing problem integrated with pickup and delivery services

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2025年第2期17卷 230-248页

作者： Khalaj Rahimi, Sanaz Rahmani, Donya KN Toosi Univ Technol Dept Ind Engn Tehran Iran

Humanitarian logistics aims to reduce the arrival time of relief goods and optimize services in disaster-affected regions. This paper introduces a multi-objective hybrid truck-drone routing problem with pickup and delivery services, considering reliability (MHTDRP-PD-R) to enhance relief operations within humanitarian logistics. The model is formulated as a mixed-integer linear programming problem with the simultaneous goals of minimizing transportation time and maximizing reliability through routing and inventory decisions made by multiple trucks and drones. An adaptive large neighborhood search (ALNS) algorithm, coupled with heuristic technique, is developed to tackle the problem. The ALNS algorithm refines initial solutions through destroy and repair operators, integrated with multi-objective optimization methods to create a unified objective function. Small and large-scale test problems are then employed to evaluate solution quality using multi-objective optimization. The calculation results show the superiority of the developed ALNS integrated with the epsilon-constraint method regarding the objective value and solution time.

关键词： Relief distribution truck-drone delivery systems pickup and delivery services reliability metaheuristic multi-objective optimization methods

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A competitive new multi-objective optimization genetic algorithm based on apparent front ranking

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 132卷

作者： Neghina, Mihai Dicoiu, Alina-Ioana Chis, Radu Florea, Adrian Lucian Blaga Univ Sibiu Comp Sci & Elect Engn Dept E Cioran Str 4 Sibiu 550025 Romania

Evolutionary algorithms built on Pareto-dominance suffer from a loss of selection pressure as the number of objectives increases and the probability of finding non -dominated solutions in the population decreases. Furthermore, Pareto-dominance is computationally expensive because of the pairwise comparisons necessary to rank the individuals of a population. The paper introduces a new genetic algorithm for multi -objective optimization based on Apparent Front Ranking and crowding distance, called Controlled Apparent Front Zones Genetic Algorithm (CAFZGA). To avoid pairwise comparisons, CAFZGA first generates the main Apparent Front Boundary (AFB) with the help of a small set of support vectors and creates secondary AFBs as shifted versions of the main AFB. The space of objectives is divided into zones by the AFBs. The zones play a similar role in CAFZGA as the non -dominated fronts do in the Controlled Non -dominated Sorting Genetic Algorithm CNSGA-II. The zone ranking becomes the main criterion in differentiating individuals, with crowding distance being the tiebreak criterion. The method is shown to be extremely flexible because the AFBs are adjusted for each generation. Computationally, CAFZGA is more efficient than GAs using Pareto-dominance because the set of support vectors for generating the AFBs is significantly smaller than the population. CAFZGA is applied to the problem of optimizing the configuration of a Grid ALU Processor (GAP) and is shown to be competitive and sometimes even better than well -established GAs like CNSGA-II and Fuzzy -Dominance -Driven Genetic Algorithm FDD-GA.

关键词： multi-objective optimization methods Genetic algorithm Controlled selection Apparent front ranking Grid ALU processor optimization

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Cooling supply with a new type of evacuated solar collectors: a techno-economic optimization and analysis

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第12期31卷 18171-18187页

作者： Teles, Mavd P. R. Sadi, Meisam Ismail, Kamal A. R. Arabkoohsar, Ahmad Silva, Brenda V. F. Kargarsharifabad, Hadi Shoeibi, Shahin Univ Estadual Campinas Fac Mech Engn Energy Dept Sao Paulo Brazil Aalborg Univ Energy Dept Esbjerg Denmark Islamic Azad Univ Dept Engn Shahrood Branch Shahrood Iran Tech Univ Denmark Dept Civil & Mech Engn Lyngby Denmark Islamic Azad Univ Prod & Recycling Mat & Energy Res Ctr Qom Branch Qom Iran Islamic Azad Univ Energy & Sustainable Dev Res Ctr Semnan Branch Semnan Iran

Renewable cooling via absorption chillers being supplied by various green heat technologies such as solar collectors has been widely studied in the literature, but it is still challenging to get positive economic outcomes from such systems due to the large expenses of solar thermal systems. This study offers the use of a new generation of solar collectors, so-called eccentric reflective solar collectors, for driving single-effect absorption chillers and thereby reducing the levelized cost of cooling. This article develops the most optimal design of this system (based on several different scenarios) using multi-objective optimization techniques and employs them for a case study in Brazil to assess its proficiency compared to conventional solar-driven cooling methods. For making the benchmarking analyses fair, the conventional system is also rigorously optimized in terms of design and operation features. The results show that the eccentric solar collector would enhance the cost-effectiveness by 29%. In addition, using optimally sized storage units would be necessary to get acceptable economic performance from the system, no matter which collector type is used. For the case study, at the optimal sizing and operating conditions, the levelized cost of cooling will be 124 USD/MWh and an emission level of 18.97 kgCO(2)/MWh.

关键词： Solar cooling Absorption chiller Eccentric reflective solar collector Techno-economic analysis multi-objective optimization methods

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Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations

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JOURNAL OF COMPUTATIONAL SCIENCE 2019年第May期34卷 48-54页

作者： Sawicki, Jakub Los, Marcin Smolka, Maciej Alvarez-Aramberri, Julen AGH Univ Sci & Technol Dept Comp Sci Krakow Poland AGH Univ Sci & Technol Dept Comp Sci Adapt Algorithms & Syst Grp Krakow Poland Columbia Univ Lamont Doherty Earth Observ Palisades NY USA BCAM Bilbao Spain

Many global optimization problems arising naturally in science and engineering exhibit some form of intrinsic ill-posedness, such as multimodality and insensitivity. Severe ill-posedness precludes the use of standard regularization techniques and necessitates more specialized approaches, usually comprised of two separate stages-global phase, that determines the problem's modality and provides rough approximations of the solutions, and a local phase, which refines these approximations. In this work, we attempt to improve one of the most efficient currently known approaches-Hierarchic Memetic Strategy (HMS)-by incorporating the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) into its local phase. CMA-ES is a stochastic optimization algorithm that in some sense mimics the behavior of population-based evolutionary algorithms without explicitly evolving the population. This way, it avoids, to an extent, the associated cost of multiple evaluations of the objective function. We compare the performance of the HMS on relatively simple multimodal benchmark problems and on an engineering problem. To do so, we consider two configurations: the CMA-ES and the standard SEA (Simple Evolutionary Algorithm). The results demonstrate that the HMS with CMA-ES in the local phase requires less objective function evaluations to provide the same accuracy, making this approach more efficient than the standard SEA. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Inverse problems multi-objective optimization methods Memetic algorithms Ill-posed problems

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Integration of Energy Storage in Distribution Grids

Integration of Energy Storage in Distribution Grids

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IEEE-Power-and-Energy-Society General Meeting

作者： Geth, Frederik Tant, Jeroen Haesen, Edwin Driesen, Johan Belmans, Ronnie Katholieke Univ Leuven Dept Elect Engn Res Grp ELECTA B-3001 Leuven Belgium

ISBN: (纸本)9781424483570

Electrical energy storage services can bring benefit to multiple stakeholders in the distribution grid. Energy storage owners maximize their profit on an external energy market. This can cause a conflict with the distribution system operator because a grid is designed in terms of peak power, not energy. The subject of this paper is a optimization method for the siting and sizing of energy storage in distribution grids. The optimization is implemented multi-objective as to visualize the trade-offs between storage stakeholders and distribution system operators.

关键词： Battery storage plants Power distribution planning Distributed energy resources multi-objective optimization methods Load How analysis

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Ensemble Classifiers with Improved Overfitting 1

Ensemble Classifiers with Improved Overfitting

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1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Pourtaheri, Zeinab Khatoun Zahiri, Seyed Hamid Univ Birjand Fac Elect & Comp Engn Birjand Iran

ISBN: (纸本)9781467387378

Overfitting has been always considered as a challenging problem in designing and training of ensemble classifiers. Obviously, the use of complex multiple classifiers may increase the success of ensemble classifier in feature space division with intertwined data and also may decrease the training error to minimum value. However, this success does not exist on the test data. Ensemble classifiers are more prone to overfitting than single classifiers because ensemble classifiers have been formed of several base classifiers and overfitting occurrence in each base classifier can transfer the problem to the final decision of the ensemble. In this paper, after quantitative and qualitative analysis of overfitting, a solution for improving overfitting is proposed by using heuristic algorithms. In this way, multi-objective Inclined Planes optimization (MOIPO) and multi-objective Particle Swarm optimization (MOPSO) are used and their results are compared with each other. Simulation results show that the simultaneous minimization of ensemble size and error rate in the training phase, can lead to a significant reduction in the amount of overfitting. In fact, with this approach in the training phase, the ensemble classifier is required to minimize the error with the most simple and minimum number of base classifiers and therefore overfitting is prevented. However, previous researches related to overfitting have ignored the ensemble size as an objective function.

关键词： Ensemble classifier Overfitting multi-objective optimization methods Classifier selection Ensemble size

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multi-objective Hierarchic Memetic Solver for Inverse Parametric Problems

Multi-objective Hierarchic Memetic Solver for Inverse Parame...

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15th Annual International Conference on Computational Science (ICCS)

作者： Gajda-Zagorska, Ewa Smolka, Maciej Schaefer, Robert Pardo, David Alvarez-Aramberri, Julen AGH Univ Sci & Technol Dept Comp Sci PL-30059 Krakow Poland Univ Basque Country UPV EHU Dept Appl Math Stat & OR Leioa Spain BCAM Bilbao Spain Ikerbasque Basque Fdn Sci Bilbao Spain Univ Pau UPPA Pau France

We propose a multi-objective approach for solving challenging inverse parametric problems. The objectives are misfits for several physical descriptions of a phenomenon under consideration, whereas their domain is a common set of admissible parameters. The resulting Pareto set, or parameters close to it, constitute various alternatives of minimizing individual misfits. A special type of selection applied to the memetic solution of the multi-objective problem narrows the set of alternatives to the ones that are sufficiently coherent. The proposed strategy is exemplified by solving a real-world engineering problem consisting of the magnetotelluric measurement inversion that leads to identification of oil deposits located about 3 km under the Earth's surface, where two misfit functions are related to distinct frequencies of the electric and magnetic waves.

关键词： inverse problems multi-objective optimization methods memetic algorithms

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Optimal scale selection approach for classification based on generalized multi-scale formal context

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APPLIED SOFT COMPUTING 2024年 152卷

作者： Wang, Fei Li, Jinhai Yu, Chongchong Beijing Technol & Business Univ Fac Comp & Artificial Intelligence Beijing 100048 Peoples R China Beijing Technol & Business Univ Inst Syst Sci Beijing 100048 Peoples R China Kunming Univ Sci & Technol Fac Sci Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Data Sci Res Ctr Kunming 650500 Yunnan Peoples R China

The classification of multi-scale data is an important research topic in granular computing. Its research goal is to determine the most appropriate scale and achieve better classification performance. However, determining the optimal scale is often a difficult problem due to lacking better metrics and optimization methods. In order to solve this problem, this paper proposes the optimal scale selection criteria for generalized multi-scale formal contexts. That is, the optimal scale uses the coarsest conditional attributes and finest decision attributes to optimize the combination of granularities of attributes. We combine these criteria with multi-objective optimization methods for developing an algorithm to fast compute the optimal scale. Experiments show that for the selected 14 data sets and 11 comparative classification methods, there are 9 classification methods with higher classification accuracies on more than 9 data sets. Therefore, the optimal scale selection method proposed in this paper is feasible and can effectively improve the performance of the classification method.

关键词： Generalized multi-scale formal context Optimal scale selection criteria multi-objective optimization methods Classification

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Fuzzy multi-objective programming: A systematic literature review

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期196卷 116663-116663页

作者： Karimi, Negar Feylizadeh, Mohammad Reza Govindan, Kannan Bagherpour, Morteza Islamic Azad Univ Dept Ind Engn Shiraz Branch Shiraz Iran Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai 201306 Peoples R China Univ Southern Denmark Ctr Sustainable Supply Chain Engn Danish Inst Adv Study Dept Technol & Innovat Campusvej 55 Odense M Denmark Yonsei Univ Yonsei Frontier Lab Seoul South Korea Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

multi-objective programming is commonly used in the literature when conflicted objectives arise in solving optimization problems. Over the past decades, classical optimization methods have been developed as useful tools to discover optimal solutions for multi-objective problems (MOPs). In recent years, under uncertainty, multi-objective optimization (MOO) has received much attention due to its practical applications in real-world problems. However, many studies have been conducted on this matter. Some of which ignored the effects of uncertainty on optimization problems. This paper systematically reviews and summarizes various multi-objective methods applied to the problems with more than one objective in uncertain environments where uncertainty is expressed using fuzzy sets. In this paper, 439 articles on fuzzy multi-objective programming published from 1978 to 2021 are reviewed using corresponding texts, charts, and tables. Finally, the basic features of MOO are briefly presented, along with a prologue of MOO techniques and current trends. Recommendations for further research are also is provided.

关键词： Fuzzy environment Fuzzy multi-objective programming multi-objective optimization methods Asystematic literature review

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