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A multi-objective model for facility location-allocation problem with immobile servers within queuing framework

COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期74卷 1-10页

作者： Rahmati, Seyed Habib A. Ahmadi, Abbas Sharifi, Mani Chambari, Amirhossain Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran Islamic Azad Univ Fac Ind & Mech Engn Qazvin Branch Qazvin Iran Islamic Azad Univ Young Researcher Club Qazvin Branch Qazvin Iran

This research investigates a practical bi-objective model for the facility location allocation (BOFLA) problem with immobile servers and stochastic demand within the M/M/1/K queue system. The first goal of the research is to develop a mathematical model in which customers and service providers are considered as perspectives. The objectives of the developed model are minimization of the total cost of server provider and minimization of the total time of customers. This model has different real world applications, including locating bank automated teller machines (ATMs), different types of vendor machines, etc. For solving the model, two popular multi-objective evolutionary algorithms (MOEA) of the literature are implemented. The first algorithm is non-dominated sorted genetic algorithm (NSGA-II) and the second one is non-dominated ranked genetic algorithm (NRGA). Moreover, to illustrate the effectiveness of the proposed algorithms, some numerical examples are presented and analyzed statistically. The results indicate that the proposed algorithms provide an effective means to solve the problems. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Queuing Location-allocation Immobile servers multi-objective evolutionary algorithms Statistical tests

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μG2-ELM: An upgraded implementation of μ G-ELM

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NEUROCOMPUTING 2016年 171卷 1302-1312页

作者： Lacruz, B. Lahoz, D. Mateo, P. M. Univ Zaragoza Dept Metodos Estadist E-50009 Zaragoza Spain

mu G-ELM is a multiobjective evolutionary algorithm which looks for the best (in terms of the MSE) and most compact artificial neural network using the ELM methodology. In this work we present the mu G2-ELM, an upgraded version of JIG-ELM, previously presented by the authors. The upgrading is based on three key elements: a specifically designed approach for the initialization of the weights of the initial artificial neural networks, the introduction of a re-sowing process when selecting the population to be evolved and a change of the process used to modify the weights of the artificial neural networks. To test our proposal we consider several state-of-the-art Extreme Learning Machine (ELM) algorithms and we confront them using a wide and well-known set of continuous, regression and classification problems. From the conducted experiments it is proved that the mu G2-ELM shows a better general performance than the previous version and also than other competitors. Therefore, we can guess that the combination of evolutionary algorithms with the ELM methodology is a promising subject of study since both together allow for the design of better training algorithms for artificial neural networks. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks Extreme Learning Machines multi-objective evolutionary algorithms Decision theory

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Non-dominated sorting genetic-based algorithm for exploiting a large-sized fuzzy outranking relation

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第2期293卷 615-631页

作者： Leyva Lopez, Juan Carlos Solano Noriega, Jesus Jaime Rui Figueira, Jose Liu, Jun Gastelum Chavira, Diego Alonso Univ Autonoma Occidente Dept Econ & Management Sci Culiacan 80120 Sinaloa Mexico Univ Lisbon Inst Super Tecn CEG IST Lisbon Portugal Ulster Univ Comp Sci Res Inst Newtownabbey Antrim North Ireland

ELECTRE III is a well-known multiple criteria decision aiding method based on pairwise comparisons. However, it cannot be applied to ranking problems involving many alternatives, because the number of pairwise comparisons can then be rather large. In this paper, we present an evolution-based approach for exploiting large fuzzy outranking relations and deriving a crisp outranking relation with desirable properties. Therefore, the utilization of a fuzzy outranking relation is modeled as a three-objective optimization problem, which is solved by an evolutionary algorithm. The proposed ranking algorithm is a hybrid of the elitist non-dominated sorting genetic algorithm-II (NSGA-II) and a reference point method with the repeated use of a choice mechanism. In addition, a method that portrays the obtained ranking in a Hasse diagram is used for recommendation purposes. We designate the new method RP2-NSGA-II+H. In our experiments, the proposed ranking procedure demonstrates a better performance in terms of ranking error rates than other ranking procedures based on multi-objective evolutionary algorithms. Our experimental results also demonstrate that, with the new procedure, this method can be scaled for hundreds of alternatives. (C) 2020 Elsevier B.V. All rights reserved.

关键词： multiple criteria analysis Fuzzy outranking relations multi-objective evolutionary algorithms Ranking procedures Electre III

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Pareto adaptive penalty-based boundary intersection method for multi-objective optimization

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INFORMATION SCIENCES 2017年 414卷 158-174页

作者： Ming, Mengjun Wang, Rui Zha, Yabing Zhang, Tao Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China Foshan Univ Math & Big Data Foshan 528000 Peoples R China

Penalty-based boundary intersection (PBI) method is a frequently used scalarizing method in decomposition based multi-objective evolutionary algorithms (MOEAs). It works well when a proper penalty value is provided, however, the determination of a suitable penalty value depends on the problem itself, more precisely, the Pareto optimal front (PF) shape. As the penalty value increases, the PBI method becomes less effective in terms of convergence, but is more capable of handling various PF shapes. In this study, a simple yet effective method called Pareto adaptive PBI (PaP) is proposed by which a suitable penalty value can be adaptively identified, which therefore can maintain fast convergence speed, meanwhile, leading to a good approximation of the PF. The PaP strategy integrated into the state-of-the-art decomposition algorithm, MOEA/D, denoted as MOEA/D-PaP, is examined on a set of multi-objective benchmarks with different PF shapes. Experimental results show that the PaP strategy is more effective than the weighted sum, the weighted Tcheby-cheff and the PBI method with (representative) fixed penalty values in general. In addition, the MOEA/D-PaP is examined on a real-world problem multi-objective optimization of a hybrid renewable energy system whose PF is unknown. The outcome of the experiment further confirms its feasibility and superiority. (C) 2017 Elsevier Inc. All rights reserved.

关键词： multi-objective evolutionary algorithms Scalarizing method Pareto adaptive PEI method Hybrid renewable energy system

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A Bi-Level Techno-Economic Optimal Reactive Power Dispatch Considering Wind and Solar Power Integration

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IEEE ACCESS 2023年 11卷 62799-62819页

作者： Ali, Aamir Abbas, Ghulam Keerio, Muhammad Usman Touti, Ezzeddine Ahmed, Zahoor Alsalman, Osamah Kim, Yun-Su Quaid E Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah 67450 Sindh Pakistan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Univ Tunis Elect Engn Dept Lab Ind Syst Engn & Renewable Energies ENSIT Tunis 1008 Tunisia Balochistan Univ Engn & Technol Khuzdar Dept Elect Engn Khuzdar 89100 Pakistan King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia Gwangju Inst Sci & Technol GIST Grad Sch Energy Convergence Gwangju 61005 South Korea

With urban and rural infrastructure development, the power system is being forced to operate at or near its full capacity. This paper proposes four new methodologies to find the solution to the optimal reactive power dispatch (ORPD) problem, considering the capabilities of modern DFIG-based WTs and VSI-based solar PV. The proposed formulation considers the techno-economic objective functions, specifically the minimization of the active and reactive power cost and the maximization of reactive power reserve. This leads to an effective solution to the probabilistic multi-objective ORPD (PMO-ORPD) problem, especially in the context of modern wind farms (WFs) and solar PV. The proposed formulations are necessary for effectively managing power systems with renewable energy sources and contribute to developing efficient and sustainable power systems. Additionally, this study employs probabilistic mathematical modeling that incorporates Weibull, lognormal, and normal probability distribution functions (PDFs) to represent uncertainties in the wind, solar, and load demand. Monte-Carlo simulation (MCS) is employed to generate probabilistic scenarios, allowing for a comprehensive analysis of the PMO-ORPD problem. A new two-phase (ToP) multi-objective evolutionary algorithm is proposed, which incorporates the superiority of feasibility constraints to effectively solve the probabilistic multi-objective optimal reactive power dispatch (PMO-ORPD) problem. From the analysis and comparison of simulation results, it has been observed that the proposed algorithm effectively solves the deterministic and PMO-ORPD problems.

关键词： Constraint domination principle multi-objective evolutionary algorithms optimal reactive power dispatch reactive power reserve renewable energy sources

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Dynamic economic emission dispatch based on group search optimizer with multiple producers

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ELECTRIC POWER SYSTEMS RESEARCH 2012年 86卷 8-16页

作者： Guo, C. X. Zhan, J. P. Wu, Q. H. Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China Univ Liverpool Dept Elect Engn Liverpool L69 3BX Merseyside England

This paper presents a new method for dynamic economic emission dispatch (DEED) of power systems, using a novel multi-objective evolutionary algorithm, group search optimizer with multiple producers (GSOMP) that includes a constraint handling scheme introduced to deal with complex constraints. The DEED is divided into 24 decomposed DEEDs, which are then solved hour by hour in the time sequence. A technique for order preference similar to an ideal solution (TOPSIS), is then developed to determine the final solution from the Pareto-optimal solutions considering a decision maker's preference. The performance of GSOMP has been evaluated on the DEEDs of the IEEE 30-bus and 118-bus systems, respectively, in comparison with those of multi-objective particle swarm optimizer (MOPSO) and non-dominated sorting genetic algorithm-II (NSGA-II). The simulation results show that DEED is well solved by the proposed method as a set of widely distributed Pareto-optimal solutions can be obtained and that GSOMP has better convergence performance than MOPSO and NSGA-II and consumes much less time than NSGA-II. All the NOx, CO2 and SO2 are integrated into the emission objective function of the DEED, on which the solution obtained can have relatively low emission of each pollutant. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Dynamic economic dispatch Dynamic economic emission dispatch Group search optimizer with multiple producers multi-objective evolutionary algorithms Constraint handling multi-criterion decision making

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An improved multi-objective population-based extremal optimization algorithm with polynomial mutation

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INFORMATION SCIENCES 2016年 330卷 49-73页

作者： Zeng, Guo-Qiang Chen, Jie Li, Li-Min Chen, Min-Rong Wu, Lie Dai, Yu-Xing Zheng, Chong-Wei Wenzhou Univ Dept Elect & Elect Engn Wenzhou 325035 Peoples R China S China Normal Univ Sch Comp Guangzhou 510631 Guangdong Peoples R China

As a recently developed evolutionary algorithm inspired by far-from-equilibrium dynamics of self-organized criticality, extremal optimization (EO) has been successfully applied to a variety of benchmark and engineering optimization problems. However, there are only few reported research works concerning the applications of EO in the field of multi-objective optimization. This paper presents an improved multi-objective population-based EO algorithm with polynomial mutation called IMOPEO-PLM to solve multi-objective optimization problems (MOPs). Unlike the previous multi-objective versions based on EO, the proposed IMOPEO-PLM adopts population-based iterated optimization, a more effective mutation operation called polynomial mutation, and a novel and more effective mechanism of generating new population. From the design perspective of multi-objective evolutionary algorithms (MOEAs), IMOPEO-PLM is relatively simpler than other reported competitive MOEAs due to its fewer adjustable parameters and only mutation operation. Furthermore, the extensive experimental results on some benchmark MOPs show that IMOPEO-PLM performs better than or at least competitive with these reported popular MOEAs, such as MOPEO, MOEO, NSGA-II, A-MOCLPSO, PAES, SPEA, SPEA2, SMS-EMOA, SMPSO, and MOEA/D-DE, by using nonparametric statistical tests, e.g., Kruskal-Wallis test, Mann-Whitney U test, Friedman and Quade tests, in terms of some commonly-used quantitative performance metrics, e.g., convergence, diversity (spread), hypervolume, generational distance, inverted generational distance. (C) 2015 Elsevier Inc. All rights reserved.

关键词： multi-objective optimization Extremal optimization multi-objective evolutionary algorithms Mutation operators

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multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets

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SOFT COMPUTING 2010年第7期14卷 713-728页

作者： Ducange, Pietro Lazzerini, Beatrice Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

We exploit an evolutionary three-objective optimization algorithm to produce a Pareto front approximation composed of fuzzy rule-based classifiers (FRBCs) with different trade-offs between accuracy (expressed in terms of sensitivity and specificity) and complexity (computed as sum of the conditions in the antecedents of the classifier rules). Then, we use the ROC convex hull method to select the potentially optimal classifiers in the projection of the Pareto front approximation onto the ROC plane. Our method was tested on 13 highly imbalanced datasets and compared with 2 two-objective evolutionary approaches and one heuristic approach to FRBC generation, and with three well-known classifiers. We show by the Wilcoxon signed-rank test that our three-objective optimization approach outperforms all the other techniques, except for one classifier, in terms of the area under the ROC convex hull, an accuracy measure used to globally compare different classification approaches. Further, all the FRBCs in the ROC convex hull are characterized by a low value of complexity. Finally, we discuss how, the misclassification costs and the class distributions are fixed, we can select the most suitable classifier for the specific application. We show that the FRBC selected from the convex hull produced by our three-objective optimization approach achieves the lowest classification cost among the techniques used as comparison in two specific medical applications.

关键词： Genetic fuzzy rule-based classifiers multi-objective evolutionary algorithms Imbalanced datasets ROC curves Convex hull method

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A hybrid project scheduling and material ordering problem: Modeling and solution algorithms

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APPLIED SOFT COMPUTING 2017年 58卷 700-713页

作者： Zoraghi, N. Shahsavar, A. Niaki, S. T. A. Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin Iran Sharif Univ Technol Dept Ind Engn Tehran Iran

A novel combination of a multimode project scheduling problem with material ordering, in which material procurements are exposed to the total quantity discount policy is investigated in this paper. The study aims at finding an optimal Pareto frontier for a triple objective model derived for the problem. While the first objective minimizes the makespan of the project, the second objective maximizes the robustness of the project schedule and finally the third objective minimizes the total costs pertaining to renewable and nonrenewable resources involved in a project. Four well-known multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm II (NSGAII), strength Pareto evolutionary algorithm II (SPEAII), multi objective particle swarm optimization (MOPSO), and multi objective evolutionary algorithm based on decomposition (MOEAD) solve the developed triple-objective problem. The parameters of algorithms are tuned by the response surface methodology. The algorithms are carried out on a set of benchmarks and are compared based on five performance metrics evaluating their efficiencies in terms of closeness to the optimal frontier, diversity, and variance of results. Finally, a statistical assessment is conducted to analyze the results obtained by the algorithms. Results show that the NSGAII considerably outperforms others in 4 out of 5 metrics and the MOPSO performs better in terms of the remaining metric. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Material ordering multi-mode project scheduling multi-objective evolutionary algorithms Pareto frontier Total quantity discount

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SMTIBEA: a hybrid multi-objective optimization algorithm for configuring large constrained software product lines

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SOFTWARE AND SYSTEMS MODELING 2019年第2期18卷 1447-1466页

作者： Guo, Jianmei Liang, Jia Hui Shi, Kai Yang, Dingyu Zhang, Jingsong Czarnecki, Krzysztof Ganesh, Vijay Yu, Huiqun East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada Shanghai Dianji Univ Sch Elect Informat Shanghai Peoples R China Chinese Acad Sci Inst Biochem & Cell Biol Inst Biol Sci Shanghai Peoples R China

A key challenge to software product line engineering is to explore a huge space of various products and to find optimal or near-optimal solutions that satisfy all predefined constraints and balance multiple often competing objectives. To address this challenge, we propose a hybrid multi-objective optimization algorithm called SMTIBEA that combines the indicator-based evolutionary algorithm (IBEA) with the satisfiability modulo theories (SMT) solving. We evaluated the proposed algorithm on five large, constrained, real-world SPLs. Compared to the state-of-the-art, our approach significantly extends the expressiveness of constraints and simultaneously achieves a comparable performance. Furthermore, we investigate the performance influence of the SMT solving on two evolutionary operators of the IBEA.

关键词： Software product lines Search-based software engineering multi-objective evolutionary algorithms Constraint solving Feature models

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