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A Comprehensive System to Support Decision Making in Highly Complex Project Portfolio Situations

IEEE ACCESS 2025年 13卷 38115-38132页

作者： Solares, Efrain Fernandez, Eduardo Coello Coello, Carlos A. Segura Lozano, Xochitl Moreno-Cepeda, Reimundo Diaz, Raymundo Univ Autonoma Coahuila Fac Contaduria & Adm Torreon 27000 Mexico Ctr Invest & Estudios Avanzados IPN Departmento Comp Mexico City 07360 Mexico Univ Autonoma Coahuila Fac Ciencias Adm Saltillo 25225 Mexico Univ Autonoma Coahuila Fac Contaduria & Adm Saltillo 25280 Mexico Tecnol Monterrey Monterrey 64849 Mexico

This paper describes a new approach in project portfolio selection (PPS) problems, emphasizing the need to overcome traditional deficiencies with respect to multicriteria decision-making and multiobjective optimization. While existing methods typically allow the solving of partial aspects of the PPS problem, the proposed approach seeks to provide a holistic framework dealing with aspects like interdependence between projects, interaction among criteria, the incorporation of both cardinal and ordinal information, and a hierarchical multiobjective optimization. Unlike approaches that optimize portfolios neglecting superiority of some projects, or those that only assess individual projects without considering overall portfolio performance, the proposal allows for a compromise between both objectives. A case study is given, proving the application of the proposal for developing well-balanced portfolios aligned with strategic organizational goals and stakeholder preferences. The results point to significant improvements in the efficiency and effectiveness of decision-making, especially in complex project environments. This research contributes not only to the advancement of the theoretical framework of PPS but also to practical implications for portfolio management in a wide variety of organizational contexts.

关键词： Portfolios Proposals Optimization MCDM evolutionary computation Vectors Stakeholders Object recognition Costs Benchmark testing Decision making evolutionary algorithms multicriteria analysis multiobjective optimization outranking methods project portfolio selection

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QRS Detection Optimization in Stress Test Recordings Using evolutionary algorithms 41

QRS Detection Optimization in Stress Test Recordings Using E...

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41st Computing in Cardiology Conference (CinC)

作者： Hernando, David Bailon, Raquel Almeida, Rute Hernandez, Alfredo Univ Zaragoza IIS Aragon Inst Engn Res I3A Biomed Signal Interpretat & Computat Simulat BSIC Zaragoza Spain CIBER BBN Zaragoza Spain Univ Porto Fac Ciencias P-4100 Oporto Portugal CMUP Oporto Portugal INSERM U1099 F-35000 Rennes France Univ Rennes 1 LTSI F-35000 Rennes France

ISBN: (纸本)9781479943463

QRS detection in exercise stress test recordings remains a challenging task, because they are highly non-stationary and contaminated with noises, such as large baseline wander and muscular noise, among others. The aim of this work is to find an optimal set of parameters for QRS detection in very noisy ECG signals, such as those acquired during stress tests. Parameter optimization was addressed by an evolutionary algorithm. A training database was created using 48 ECG recordings with reference QRS complexes. Each ECG recording is artificially contaminated with 3 types of real noise. A cost function combining the detection error probability, the mean detection jitter, and its standard deviation was defined, in order to obtain a quantitative performance evaluation of the detector. Evaluation was performed on an exercise stress test database composed of 54 real ECG recordings, with annotated QRS. The detector was configured with default parameter values, and also with the optimal values obtained from the evolutionary algorithm. The QRS detector with its optimized parameters showed a mean improvement of 4.6% compared to its performance with the default parameters. Furthermore, the use of optimized parameters led to at least the same performance than the initial parameters for all records, and the improvement was higher (up to 19.36 %) in noisy records, demonstrating the advantages of the optimized parameters in noisy environments.

关键词： electrocardiography medical signal detection muscle optimisation signal denoising QRS detection optimization baseline wander cost function evolutionary algorithms mean detection jitter muscular noise noises noisy ECG signals parameter optimization stress test recordings Databases Detectors Electrocardiography Noise Noise measurement Optimization Stress

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Modeling the Offloading of Different Types of Mobile Applications by Using evolutionary algorithms 1

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17th European Conference on Applications of evolutionary Computation (EvpApplications)

作者： Folino, Gianluigi Pisani, Francesco S. ICAR CNR Inst High Performance Comp & Networking Arcavacata Di Rende Italy

ISBN: (数字)9783662455234

ISBN: (纸本)9783662455234;9783662455227

Modern smartphones permit to run a large variety of applications, i.e. multimedia, games, social network applications, etc. However, this aspect considerably reduces the battery life of these devices. A possible solution to alleviate this problem is to offload part of the application or the whole computation to remote servers, i.e. Cloud Computing. The offloading cannot be performed without considering the issues derived from the nature of the application (i.e. multimedia, games, etc.), which can considerably change the resources necessary to the computation and the type, the frequency and the amount of data to be exchanged with the network. This work shows a framework for automatically building models for the offloading of mobile applications based on evolutionary algorithms and how it can be used to simulate different kinds of mobile applications and to analyze the rules generated. To this aim, a tool for generating mobile datasets, presenting different features, is designed and experiments are performed in different usage conditions in order to demonstrate the utility of the overall framework.

关键词： evolutionary algorithms

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Automated design of neural networks with multi-scale convolutions via multi-path weight sampling

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PATTERN RECOGNITION 2025年 165卷

作者： Huang, Junhao Xue, Bing Sun, Yanan Zhang, Mengjie Yen, Gary G. Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington 6140 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China

The performance of convolutional neural networks (CNNs) relies heavily on the architecture design. Recently, an increasingly prevalent trend in CNN architecture design is the utilization of ingeniously crafted building blocks, e.g., the MixConv module, for improving the model expressivity and efficiency. To leverage the feature learning capability of multi-scale convolution while further reducing its computational complexity, this paper presents a computationally efficient yet powerful module, dubbed EMixConv, by combining parameter-free concatenation-based feature reuse with multi-scale convolution. In addition, we propose a one-shot neural architecture search (NAS) method integrating the EMixConv module to automatically search for the optimal combination of the related architectural parameters. Furthermore, an efficient multi-path weight sampling mechanism is developed to enhance the robustness of weight inheritance in the supernet. We demonstrate the effectiveness of the proposed module and the NAS algorithm on three popular image classification tasks. The developed models, dubbed EMixNets, outperform most state-of-the-art architectures with fewer parameters and computations on the CIFAR datasets. On ImageNet, EMixNet is superior to a majority of compared methods and is also more compact and computationally efficient.

关键词： Multi-scale convolutional neural networks One-shot neural architecture search evolutionary algorithms Weight inheritance

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A dynamic multi-objective optimization algorithm based on probability-driven prediction and correlation-guided individual transfer

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-47页

作者： Ge, Fangzhen Zhao, Xuan Chen, Debao Shen, Longfeng Liu, Huaiyu Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China Huaibei Normal Univ Anhui Engn Res Ctr Intelligent Comp & Applicat Cog Huaibei 235000 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China

The primary challenge in addressing dynamic multi-objective optimization problems (DMOPs) is the rapid tracking of optimal solutions. Although methods based on transfer learning have shown remarkable performance in tackling DMOPs, most existing methods overlook the potential relationships between individuals within the population and those from historical environments. Consequently, they fail to adequately exploit historical information. To this end, this study proposes a dynamic multi-objective optimization algorithm based on probability-driven prediction and correlation-guided individual transfer (PDP&CGIT), which consists of two strategies: probability-driven prediction (PDP) and correlation-guided individual transfer (CGIT). Specifically, the PDP strategy analyzes the distribution of population characteristics and constructs a discriminative predictor based on a probability-annotation matrix to classify high-quality solutions from numerous randomly generated solutions within the decision space. Moreover, from the perspective of individual evolution, the CGIT strategy analyzes the correlation between current elite individuals and those from the previous moment. It learns the dynamic change pattern of the individuals and transfers this pattern to new environments. This is to maintain the diversity and distribution of the population. By integrating the advantages of these two strategies, PDP&CGIT can efficiently respond to environmental changes. Extensive experiments were performed to compare the proposed PDP&CGIT with five state-of-the-art algorithms across the FDA, F, and DF test suites. The results demonstrated the superiority of PDP&CGIT.

关键词： Dynamic multi-objective optimization Transfer learning evolutionary algorithms Prediction

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An adaptive helper and equivalent objective evolution strategy for constrained optimization

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INFORMATION SCIENCES 2025年 690卷

作者： Xu, Tao Chen, Hongyang He, Jun Zhejiang Lab Hangzhou 311121 Peoples R China Nottingham Trent Univ Dept Comp Sci Nottingham NG11 8NS England

The matrix adaptation evolution strategy is a simplified covariance matrix adaptation evolution strategy with reduced computational cost. Using it as a search engine, several algorithms have been recently proposed for constrained optimization and have shown excellent performance. However, these algorithms require the simultaneous application of multiple techniques to handle constraints, and also require gradient information. This makes them inappropriate for handling non-differentiable functions. This paper proposes a new matrix adaption evolutionary strategy for constrained optimization using helper and equivalent objectives. The method optimizes two objectives but with no need for special handling of infeasible solutions and without gradient information. A new mechanism is designed to adaptively adjust the weights of the two objectives according to the convergence rate. The efficacy of the proposed algorithm is evaluated using computational experiments on the IEEE CEC 2017 Constrained Optimization Competition benchmarks. Experimental results demonstrate that it outperforms current state-ofthe-art evolutionary algorithms. Furthermore, this paper provides sufficient conditions for the convergence of helper and equivalent objective evolutionary algorithms and proves that using helper objectives can reduce the likelihood of premature convergence.

关键词： Constrained optimization evolutionary algorithms Algorithm analysis Algorithm design Multi-objective optimization

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Historical evolution of structural optimization techniques for steel skeletal structures including industrial design applications

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ENGINEERING OPTIMIZATION 2025年第1期57卷 69-129页

作者： Saka, M. P. Hasancebi, O. Eser, H. Geem, Zong Woo Middle East Tech Univ Dept Engn Sci Ankara Turkiye Middle East Tech Univ Dept Civil Engn Ankara Turkiye Gachon Univ Dept Smart City Seongnam South Korea

This study covers the review of algorithms developed for the optimum design of steel skeletal structures from the first article published in 1960 until to date. The paper initially describes the mathematical formulation of a simple truss structural design problem. The early optimum design algorithms that were based on mathematical programming techniques where the design variables are assumed to be continuous are reviewed. The optimum design of steel framed structures necessitates the selection of steel profiles from the standard list of discrete steel sections and requires the satisfaction of design code provisions. Both mathematical programming and optimality criteria techniques need more capability to produce solution to this type of optimum design problems. Soft computing techniques emerged recently provide solution directly without needing any approximation. These techniques are classified and reviewed and their use in obtaining the optimum solution of actual industrial steel design applications is given.

关键词： Structural optimization mathematical programming techniques metaheuristics evolutionary algorithms swarm intelligence methods

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ON DEDICATED evolutionary algorithms FOR LARGE NON-LINEAR CONSTRAINED OPTIMIZATION PROBLEMS IN APPLICATION TO RESIDUAL STRESSES ANALYSIS 11

ON DEDICATED EVOLUTIONARY ALGORITHMS FOR LARGE NON-LINEAR CO...

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11th World Congress on Computational Mechanics (WCCM) / 5th European Conference on Computational Mechanics (ECCM) / 6th European Conference on Computational Fluid Dynamics (ECFD)

作者： Orkisz, Janusz E. Glowacki, Maciej A. Cracow Univ Technol Inst Computat Civil Engn PL-31155 Krakow Poland

ISBN: (纸本)9788494284472

This work considers development of dedicated evolutionary algorithms (EA) with several new specialized acceleration techniques introduced. Our long-term research is oriented towards development of efficient solution methods for a wide class of large, non-linear, constrained optimization problems. We are presenting here a preliminary application of the improved EA to sample benchmark problems of residual stresses analysis. The final objective of our research is such analysis done for railroad rails, and vehicle wheels. Knowledge of the tensile residual stresses is crucial for reliable prediction of rails and wheels service life resulting from their fatigue failure. Both the theoretical and experimental investigations of residual stresses may be expressed in terms of large, non-linear, constrained optimization problems. Due to the size and complexity of the optimization problems involved, our research is focused, first of all, on the EA efficiency increase.

关键词： evolutionary algorithms Large Constrained Optimization Problems Calculation Efficiency Increase

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PCA-ENHANCED METAMODEL-ASSISTED evolutionary algorithms FOR AERODYNAMIC OPTIMIZATION 11

PCA-ENHANCED METAMODEL-ASSISTED EVOLUTIONARY ALGORITHMS FOR ...

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11th World Congress on Computational Mechanics (WCCM) / 5th European Conference on Computational Mechanics (ECCM) / 6th European Conference on Computational Fluid Dynamics (ECFD)

作者： Asouti, Varvara G. Kyriacou, Stylianos A. Giannakoglou, Kyriakos C. Natl Tech Univ Athens Parallel CFD & Optimizat Unit Iroon Polytechniou 9 Athens 15780 Greece

ISBN: (纸本)9788494284472

This paper deals with evolutionary algorithms (EAs) assisted by surrogate evaluation models or metamodels (Metamodel-Assisted EAs, MAEAs) which are further accelerated by exploiting the Principal Component Analysis (PCA) of the elite members of the evolving population. PCA is used to (a) guide the application of evolution operators and (b) train metamodels, in the form of radial basis functions networks, on patterns of smaller dimension. Compared to previous works by the same authors, this paper also proposes a new way to apply the PCA technique. In particular, the front of non-dominated solutions is divided into sub-fronts and the PCA is applied "locally" to each sub-front. The proposed method is demonstrated in multi-objective, constrained, aerodynamic optimization problems.

关键词： evolutionary algorithms Metamodels Principal Component Analysis Optimization Aerodynamics

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A Non-uniform Clustering Based evolutionary Algorithm for Solving Large-Scale Sparse Multi-objective Optimization Problems 18th

A Non-uniform Clustering Based Evolutionary Algorithm for So...

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18th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

作者： Shao, Shuai Tian, Ye Zhang, Xingyi Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

ISBN: (纸本)9789819722716;9789819722723

evolutionary algorithms have shown their effectiveness in solving sparse multi-objective optimization problems (SMOPs). However, for most of the existing multi-objective optimization algorithms (MOEAs) for solving SMOPs, their search granularity keeps the same for all the decision variables, which leads to significant performance deterioration when dealing with SMOPs in high-dimensional decision spaces. To tackle the issue, in this paper, a non-uniform clustering based evolutionary algorithm, termed NUCEA, is proposed for solving large-scale SMOPs. The proposed algorithm divides the decision variables into multiple groups with varying sizes, so as to reduce the search space with different granularity. These clustering outcomes inspire the development of new genetic operators, which have been proven to efficiently perform dimensionality reduction when approximating sparse Pareto optimal solutions. Experimental results on both benchmark and real-world SMOPs have shown that the proposed algorithm has significant advantages in comparison with the state-of-the-art evolutionary algorithms.

关键词： Large-scale multi-objective optimization Sparse Pareto optimal solutions evolutionary algorithms Variable clustering

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