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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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Data driven multi-objective optimization of the scheduling for towing a floating offshore wind turbine between assembly port and installation location throughout a year

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APPLIED OCEAN RESEARCH 2025年 157卷

作者： Le Pivert, Frederic Roberts, Adam Lopez-Santander, Adan Craven, Matthew J. Kazemi, Saeid Univ Plymouth Sch Engn Comp & Math Maritime Simulat Lab Drake Circus Plymouth PL4 8AA England Univ Plymouth Ctr Math Sci Sch Engn Comp & Math Drake Circus Plymouth PL4 8AA England

High demand for the installation of floating offshore wind turbines over the coming years is likely to place significant pressure on ports and installation vessels. Optimization of the routes between ports and farms and the towing schedule when transporting equipment is therefore critical to reducing operation timescales and carbon emissions. This paper presents two series of multi-objective optimizations for minimizing the timescale and carbon emissions for the case of an IEA 15 MW turbine on a VolturnUS-S platform being wet towed through the English Channel to the Celtic Sea. The study makes use of the Maritime Simulation Laboratory (MSL) Ship Simulator to develop an empirical model of the floating offshore wind turbine being towed under different wind conditions. This is then combined with bathymetry data and historical metocean data from the year 2021 to perform the optimizations. The optimization results are used to feed a second optimization that creates a schedule reducing both emissions and cumulated towing time during a whole year for different number of floating offshore wind turbines.

关键词： Towing Routing optimization multi-objective evolutionary algorithms Floating offshore wind Scheduling

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Development of a multi-objective decision support system for eco-hydrological forest management that quantifies and optimizes ecosystem services related to Carbon, Water, Fire-risk and Eco-resilience (CAFE)

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025年 380卷 125103页

作者： Perez-Romero, J. Gonzalez-Sanchis, M. Blanco-Cano, L. Del Campo, A. D. Univ Politecn Valencia Res Grp Forest Sci & Technol Re ForeST Cami Vera S-N Valencia 46022 Spain Univ Cordoba Dept Psychol Cordoba 14071 Spain Univ Politecn Valencia Dept Hydraul Engn & Environm Cami Vera S-N Valencia 46022 Spain

Sustainable forest management (FOMA) requires explicit knowledge of the ecosystem services (ES) provided by forests and how they can be improved by different FOMA alternatives, especially when threatened by climate change and the productivity/profitability is low. Decision Support Systems (DSS) have evolved as powerful tools that facilitate decision-making in multi-objective FOMA. However, their use is often limited, as they are monoobjective, are based on empirical relationships, consider limited ES or do not define which silvicultural actions can be deployed, where and when. Thus, decision-makers and forest planners cannot adjust them to local conditions or specific criteria. CAFE (Carbon, Aqua, Fire & Eco-resilience) is a multi-objective DSS for FOMA that quantifies and optimizes different ES that stem from forest management. Its main contribution is coupling ecohydrological process-based models and multi-objective optimization with genetic evolutionary algorithms. The output of the DSS, shown in a user-friendly interface, is a selection of the best-performing solutions (Pareto Optimal Front) of FOMA that optimize the user-selected ES. This tool allows designing and planning silvicultural operations such as thinning or planting required for meeting multiple objectives in FOMA by answering four fundamental questions: How much (thinning intensity or plantation density), where (spatial allocation), when (year of next intervention) and how (target forest strata in which the stand is vertically divided) thinning. This paper presents the design and components of CAFE, and a practical demonstration in contrasted forests. CAFE might contribute to addressing current challenges of meeting global environmental policy goals by stating baselines and additionality in FOMA.

关键词： Process-based models multi-objective evolutionary algorithms Quantification Optimization Forest management

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Methodology for Designing Injection Molds: Data Mining and multi-objective Optimization

Methodology for Designing Injection Molds: Data Mining and M...

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28th European Conference on Applications of evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Gaspar-Cunha, António Melo, João Marques, Tomás Pontes, António IPC-Institute for Polymers and Composites University of Minho Guimarães Portugal

ISBN: (纸本)9783031900617

Injection molding is a complex process where effective mold design is essential to avoid defects like incomplete parts, flash, sink marks, weld lines, air bubbles, warping, and shrinkage. Proper mold design helps minimize these defects, but it is challenging due to the number of variables across stages such as plasticization, filling, packing, and cooling. This study focuses on optimizing the cooling phase of injection molding using numerical simulation tools (Moldex3D) to evaluate the impact of design variables on process performance. Due to the complexity and multi-objective nature of the problem, Artificial Intelligence (AI) techniques, including data mining, Artificial Neural Networks (ANN), and multi-objective evolutionary algorithms (MOEAs), were used to explore solutions effectively. The optimization aimed to improve the thermal efficiency of conformal cooling systems, critical for ensuring part quality and minimizing cycle time. A case study with a cylindrical part and multiple cooling systems initially defined 34 objectives, including temperature gradients, cycle time, and defects. These objectives were reduced to four using Principal Component Analysis (PCA) to facilitate optimization. The results showed significant improvements in temperature uniformity and defect reduction, confirming the effectiveness of integrating AI-based optimization techniques with numerical modeling for advanced cooling system design in injection molds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial Neural Networks Data Mining Injection Molding multi-objective evolutionary algorithms

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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 new methodology for calculating roadway lighting design based on a multi-objective evolutionary algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第6期40卷 2156-2164页

作者： Gomez-Lorente, Daniel Rabaza, O. Espin Estrella, A. Pena-Garcia, A. Univ Granada Dept Civil Engn E-18071 Granada Spain

This paper presents a new method for calculating the design of roadway lighting. Apart from its accuracy, this method, which is based on a multi-objective evolutionary algorithm, has the added advantage of enhancing the energy efficiency of lighting installations. This is positive because the economic use of energy resources is evidently a priority in the world today. In our study, an exhaustive calibration process was used to fine-tune the accuracy and precision of the new method presented. The results obtained were then compared with those of DIALUX, a well-known free software program that is frequently used for the design of lighting installations. In the second phase of this research, the lighting installation was made more complex in order to verify the applicability of this new method to a wide range of different contexts. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithms Numerical optimization Roadway lighting Installation efficiency

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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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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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