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15th ISSAT International Conference on Reliability and Quality in Design

作者： Aguirre, Oswaldo Villanueva, Delia Taboada, Heidi Univ Texas El Paso Dept Ind Engn El Paso TX 79968 USA

ISBN: (纸本)9780976348658

In the present paper a multi-objective evolutionary algorithm is developed to solve three different network reliability design problems taking in account reliability, cost and weight as objective functions to be optimized simultaneously. The solution presented for each of the different problems is a set of Pareto-optimal solutions. The three problems considered are the all-terminal, the k-terminal, and the two-terminal network reliability problems. To solve these well-known network reliability problems, a multiple objective evolutionary algorithm was developed. Examples are presented for each of the different problems to show the performance of the algorithm.

关键词： All-terminal k-terminal two-terminal network reliability multi-objective evolutionary algorithms

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Comparison of evolutionary multi-objective Optimization algorithms on the Tuning of PI Controllers for Electric Drives 8

Comparison of Evolutionary Multi-Objective Optimization Algo...

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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： Dos Santos, Guilherme F. Da Silva, Wander G. Pickert, Volker De Paula, Geyverson T. School of Electrical Mechanical and Computer Engineering Federal University of Golás Goiânia Brazil School of Engineering Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798350321128

This paper presents a comparison of the two most used evolutionary algorithms for multi-objective problems, Strength Pareto evolutionary Algorithm 2 (SPEA2) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), applied to the position control of a DC motor drive. The DC motor drive is considered with three cascade-controlled closed loops: armature current, speed and position. The outputs of the PI armature current and speed controllers are limited to the rated armature voltage and current, respectively. Because of this, higher gains for the controllers can be used, resulting in faster responses. However, windup phenomenon can arise. To avoid this, anti-windup circuits are used and, consequently, the system becomes non-linear, making the optimal tuning of the controllers even more challenging. To address this problem, a couple of multi-objective evolutionary algorithms have been employed to tune all the controllers simultaneously, together with the anti-windup window. In this paper two of them, SPEA2 and NSGA-II are investigated. They are implemented in MATLAB, while the electric drive model was developed in the SIMULINK environment. Simulation results demonstrate the algorithm's effectiveness in achieving optimal tuning for all three PI controllers while satisfying multiple conflicting objectives. Statistical tests were carried out demonstrating the advantage of SPEA2 over NSGA-II. © 2023 IEEE.

关键词： DC motor electric drives multi-objective evolutionary algorithms NSGA-II position control SPEA2

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When Is Non-deteriorating Population Update in MOEAs Beneficial? 13th

When Is Non-deteriorating Population Update in MOEAs Benefi...

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13th International Conference on evolutionary multi-Criterion Optimization, EMO 2025

作者： Zhang, Qiaozhi Li, Miqing Tang, Ke Yao, Xin The University of Birmingham Birmingham United Kingdom Southern University of Science and Technology Shenzhen China Lingnan University China

ISBN: (纸本)9789819635375

Population update in multi-objective evolutionary algorithms (MOEAs) can be seen as an archiving process, namely, updating the population by comparing it with new solutions and deciding which ones to keep and which ones to discard. It thus may be useful for a population update mechanism to have some desirable archiving properties;for example, non-deteriorating, i.e., the population does not accept solutions that are inferior to solutions eliminated in the past. Unfortunately, none of mainstream MOEAs hold such a property. On the other hand, different from the archiving process which focuses on how to choose the best solutions from a set of candidate solutions, population update in MOEAs also cares about where these candidate solutions come from and what they are used for. It has been reported recently that allowing some dominated solutions in the population may help the search jump out of local optima. In this paper, we investigate the above seemingly "contradictory" observations, and aim to answer the question of whether holding the property non-deteriorating is beneficial or detrimental to MOEAs. We examine three representative MOEAs and modify their population update rules to make them non-deteriorating. We find that holding it is not necessarily beneficial for all the algorithms, but is generally useful, particularly for those whose population update mechanism does not hold any other desirable properties (e.g., set-monotone and limit-stable) like NSGA-II. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Archiving Elitism multi-objective evolutionary algorithms Population update

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The influence of food bloggers toward consumer’s attitude in restaurant selection: a multi-objective metaheuristic approach

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Soft Computing 2025年 1-27页

作者： Tunga, Harinandan Pal, Surjendu Kar, Samarjit Giri, Debasis Bausys, Romualdas Department of Computer Science and Engineering RCC Institute of Information Technology Kolkata India Department of Mathematics National Institute of Technology Durgapur India Department of Information Technology Maulana Abul Kalamazad University of Technology Kolkata India Department of Graphical Systems Vilnius Gediminas Technical University Vilnius Lithuania

Restaurant selection by a consumer is a challenging task. The main problem is the proper optimization of ratings by food bloggers and the cost of food in the restaurant. In this paper, we propose an approach for a restaurant recommendation system that uses food blogger ratings and the cost of food items in the restaurant. The aim is to maximize the restaurant rating with minimization of overall cost. For this, we construct a multi-objective optimization problem to get restaurant recommendations that are appropriate for the customer's budget and desired rating. Three evolutionary optimization algorithms, namely Nondominated Sorting Genetic Algorithm II (NSGA II), Strength Pareto evolutionary Algorithm 2 (SPEA 2), and Indicator-Based Evolution Algorithm (IBEA) have been used to identify approximated Pareto solutions for our proposed model. The effectiveness of the three evolutionary algorithms under consideration is given and evaluated against several performance indicators. Using Zomato restaurant data, we compare the results in terms of convergence and diversity, which confirms our suggestion for standardization. The suggested work contributes to an analytical approach based on evolutionary algorithm solutions to create a multi-objective restaurant recommendation system, which goes beyond the scope of previous works. We also present a comparative result analysis with the existing Zomato rating and proposed approach using statistical tools. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： multi-objective evolutionary algorithms Pareto front error Restaurant rating Restaurant recommendation

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evolutionary multi-objective optimization for RIS-aided MU-MISO communication systems

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SOFT COMPUTING 2023年第12期27卷 8091-8106页

作者： Li, Mengke Yan, Bai Zhang, Jin Southern Univ Sci & Technol Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Guangdong Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Brain inspired Intelligent Shenzhen 518055 Guangdong Peoples R China

We study a multi-user multiple-input single-output downlink system aided by a reconfigurable intelligent surface (RIS). Users' sum rate and transmit power are two important performance indicators in such systems. However, most existing works only optimize one of them, resulting in severe performance degradation of the other. Motivated by this, in this paper, we formulate a multi-objective optimization problem to maximize the sum rate of users and minimize the transmit power simultaneously. According to our early work on fitness landscape analysis of sum rate maximization problems, the proposed problem is inferred to be multi-modal. To solve this non-convex and multi-modal problem, we propose a novel multi-objective evolutionary hybrid beamforming (MEHB) framework to find different trade-off solutions between the two conflicting objectives. In particular, we employ different kinds of multi-objective evolutionary algorithms and multi-modal multi-objective evolutionary algorithms as the baseline of MEHB framework, so as to design the passive beamforming. And the active beamforming at the base station is optimized by the classical zero-forcing method. The simulation results have verified the effectiveness of the dominance-based evolutionary algorithms in handling hybrid beamforming problems.

关键词： Reconfigurable intelligent surface (RIS) Hybrid beamforming multi-objective evolutionary algorithms multi-objective multi-modal optimization

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A novel multi-objective evolutionary algorithm based on subpopulations for the bi-objective traveling salesman problem

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SOFT COMPUTING 2019年第15期23卷 6157-6168页

作者： Moraes, Deyvid Heric Sanches, Danilo Sipoli Rocha, Josimar da Silva Caldonazzo Garbelini, Jader Maikol Castoldi, Marcelo Favoretto Univ Tecnol Fed Parana Av Alberto Carazzai Cornelio Procopio PR Brazil

This paper presents a new approach called MOEA/NSM (multi-objective evolutionary algorithm integrating NSGA-II, SPEA2 and MOEA/D features). This paper combines the main characteristics of the NSGA-II, SPEA2 and MOEA/D algorithms, and also including 2-opt local search technique to improve the objective space search. The MOEA/NSM algorithm was compared to the other classical approaches using 9 datasets for the bi-objective traveling salesman problem. In addition, experiments were carried out applying the local search in the classical approaches, resulting in a considerable improvement in the results for these algorithms. From the Pareto frontiers resulting from the experiments, we applied the evaluation metrics by hypervolume, Epsilon (E), EAF and Shapiro-Wilk statistical hypothesis test. The results showed a better performance of the MOEA/NSM when compared with NSGA-II, SPEA2 and MOEA/D.

关键词： multi-objective evolutionary algorithms Bi-objective traveling salesman problem NSGA-II MOEA D Local search

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Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2009年第7期50卷 1066-1080页

作者： Antonelli, Michela Ducange, Pietro Lazzerini, Beatrice Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz Elettron Informat Tele I-56122 Pisa Italy

In this paper we propose a multi-objective evolutionary algorithm to generate Mamdani fuzzy rule-based systems with different good trade-offs between complexity and accuracy. The main novelty of the algorithm is that both rule base and granularity of the uniform partitions defined on the input and output variables are learned concurrently. To this aim, we introduce the concepts of virtual and concrete rule bases: the former is defined on linguistic variables, all partitioned with a fixed maximum number of fuzzy sets, while the latter takes into account, for each variable, a number of fuzzy sets as determined by the specific partition granularity of that variable. We exploit a chromosome composed of two parts, which codify the variables partition granularities, and the virtual rule base, respectively. Genetic operators manage virtual rule bases, whereas fitness evaluation relies on an appropriate mapping strategy between virtual and concrete rule bases. The algorithm has been tested on two real-world regression problems showing very promising results. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Mamdani fuzzy rule-based systems multi-objective evolutionary algorithms Partition granularity learning Accuracy-interpretability trade-off

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DMEA-II: the direction-based multi-objective evolutionary algorithm-II

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SOFT COMPUTING 2014年第11期18卷 2119-2134页

作者： Long Nguyen Bui, Lam T. Abbass, Hussein A. Le Quy Don Tech Univ Fac Informat Technol Hanoi Vietnam Univ New S Wales Australian Def Force Acad Canberra ACT Australia

This paper discusses the use of direction of improvement in guiding multi-objective evolutionary algorithms (MOEAs) during the search process towards the area of Pareto optimal set. We particularly propose a new version of the Direction based multi-objective evolutionary Algorithm (DMEA) and name it as DMEA-II. The new features of DMEA-II includes (1) an adaptation of the balance between convergence and spreading by using an adaptive ratio between the convergence and spreading directions being selected over time;(2) a new concept of ray-based density for niching;and (3) a new selection scheme based on the ray-based density for selecting solutions for the next generation. To validate the performance of DMEA-II, we carried out a case study on a wide range of test problems and comparison with other MOEAs. It obtained quite good results on primary performance metrics, namely the generation distance, inverse generation distance, hypervolume and the coverage set. Our analysis on the results indicates the better performance of DMEA-II in comparison with the most popular MOEAs.

关键词： multi-objective evolutionary algorithms Direction-based MOEAs Performance measurement DMEA

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Hyper-spherical inversion transformations in multi-objective evolutionary optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期177卷 1678-1702页

作者： Large, J. W. Jones, D. F. Tamiz, M. Univ Portsmouth Dept Math Portsmouth PO1 3HE Hants England

multi-objective evolutionary algorithms (MOEAs) are widely considered to have two goals: convergence towards the true Pareto front and maintaining a diverse set of solutions. The primary concern here is with the first goal of convergence, in particular when one or more variables must converge to a constant value. Using a number of well known test problems, the difficulties that are currently impeding convergence are discussed and then a new method is proposed that transforms the decision space using the geometric properties of hyper-spherical inversions to converge towards/onto the true Pareto front. Future extensions of this work and its application to multi-objective optimisation is discussed. (c) 2005 Elsevier B.V. All rights reserved.

关键词： inversion transformation crossover convergence multi-objective evolutionary algorithms

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multi-objective evolutionary algorithm for donor-recipient decision system in liver transplants

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第2期222卷 317-327页

作者： Cruz-Ramirez, Manuel Hervas-Martinez, Cesar Carlos Fernandez, Juan Briceno, Javier de la Mata, Manuel Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain Hosp Reina Sofia CIBERehd Liver Transplantat Unit Cordoba Spain

This paper reports on a decision support system for assigning a liver from a donor to a recipient on a waiting-list that maximises the probability of belonging to the survival graft class after a year of transplant and/or minimises the probability of belonging to the non-survival graft class in a two objective framework. This is done with two models of neural networks for classification obtained from the Pareto front built by a multi-objective evolutionary algorithm - called MPENSGA2. This type of neural network is a new model of the generalised radial basis functions for obtaining optimal values in C (Correctly Classified Rate) and MS (Minimum Sensitivity) in the classifier, and is compared to other competitive classifiers. The decision support system has been proposed using, as simply as possible, those models which lead to making the correct decision about receptor choice based on efficient and impartial criteria. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks Generalised radial basis function Liver transplantation multi-objective evolutionary algorithms Organ allocations Rule-based system

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