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A weighted knowledge extraction strategy for dynamic multi-objective optimization

SWARM AND evolutionary COMPUTATION 2025年 92卷

作者： Xie, Yingbo Qiao, Junfei Wang, Ding Henan Polytech Univ Sch Elect Engn & Automat Henan Key Lab Intelligent Detect & Control Coal Mi Jiaozuo 454003 Peoples R China Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

Multi-objective evolutionary algorithms suffer from performance degradation when solving dynamic multi- objective optimization problems (DMOPs) with a new conditional configuration from scratch, which motivates the research on knowledge extraction. However, most knowledge extraction strategies only focus on obtaining effective information from a single knowledge source, while ignoring the useful information from other knowledge sources with similar properties. Motivated by this, a weighted multi-source knowledge extraction strategy-based dynamic multiobjective evolutionary algorithm is proposed. First, a similarity criterion based on angle information is constructed to quantify similarity between different source domains and the target domain. Second, a knowledge extraction technique is developed to select a specific number of individuals from each source domain using a distance metric. Third, a generation strategy based on dynamic weighting mechanism is proposed, which generates a certain number of individuals and merges these individuals into the initial population within the new environment. Finally, the comprehensive experiments are conducted on public DMOP benchmarks and demonstrate the devised method significantly outperforms the state-of-the-art competing algorithms.

关键词： Dynamic multiobjective optimization Change response evolutionary algorithms evolutionary environment

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Chaotic dynamics in an overlapping generations model: Forecasting and regularization

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CHAOS SOLITONS & FRACTALS 2025年 196卷

作者： Alexeeva, Tatyana A. Kuznetsov, Nikolay V. Mokaev, Timur N. Zelinka, Ivan HSE Univ Sch Comp Sci Phys & Technol Kantemirovskaya ul 3 St Petersburg 194100 Russia St Petersburg Univ Fac Math & Mech St Petersburg 198504 Russia RAS Inst Problems Mech Engn VO Bolshoj pr 61 St Petersburg 199178 Russia VSB TUO Fac Elect Engn & Comp Sci Dept Comp Sci 17 listopadu 2172-15 Ostrava 70800 Czech Republic VSB TUO IT4Innovat Natl Supercomp Ctr Ostrava 70800 Czech Republic

Irregular dynamics (especially chaotic) is often undesirable in economics because it presents challenges for predicting and controlling the behavior of economic agents. In this paper, we used an overlapping generations (OLG) model with a control function in the form of government spending as an example, to demonstrate an effective approach to forecasting and regulating chaotic dynamics based on a combination of classical control methods and artificial intelligence algorithms. We showed that in the absence of control variables, both regular and irregular (including chaotic) behavior could be observed in the model. In the case of irregular dynamics, a small control action introduced in the model allows modifying the behavior of economic agents and switching their dynamics from irregular to regular mode. We used control synthesis by the Pyragas method to solve the problem of regularizing the irregular behavior and stabilizing unstable periodic orbits (UPOs) embedded in the chaotic attractor of the model. To maximize the basin of attraction of stabilized UPOs, we used several types of evolutionary algorithms (EAs). We compared the results obtained by applying these EAs in numerical experiments and verified the outcomes by numerical simulation. The proposed approach allows us to improve the forecasting of dynamics in the OLG model and make agents' expectations more predictable.

关键词： Overlapping generations (OLG) model Forecasting Control Chaos Artificial intelligence evolutionary algorithms

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Preventing Premature Convergence and Proving the Optimality in evolutionary algorithms 11

Preventing Premature Convergence and Proving the Optimality ...

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11th Biennial International Conference on Artificial Evolution (EA)

作者： Vanaret, Charlie Gotteland, Jean-Baptiste Durand, Nicolas Alliot, Jean-Marc Ecole Natl Aviat Civile Lab Math Appl Informat & Automat Aerien Toulouse France Inst Rech Informat Toulouse Toulouse France

ISBN: (纸本)9783319116839;9783319116822

evolutionary algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their exponential complexity and their inability to quickly compute a good approximation of the global minimum. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a branch and bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and is highly competitive with both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality.

关键词： evolutionary algorithms

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Hybrid evolutionary Approach to Team Building using PROMETHEE II 24th

Hybrid Evolutionary Approach to Team Building using PROMETHE...

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24th International Conference on Group Decision and Negotiation (GDN)

作者： Stavrou, Georgios Adamidis, Panagiotis Papathanasiou, Jason Tarabanis, Konstantinos Univ Macedonia Dept Informat & Elect Engn Thessaloniki 57400 Greece Int Hellen Univ Egnatias 156 Thessaloniki 54636 Greece Univ Macedonia Egnatias 156 Thessaloniki 54636 Greece

ISBN: (纸本)9783031593727;9783031593734

Multi-Criteria Decision Making (MCDM) methods, such as PROMETHEE II, play a crucial role in complex decision-making scenarios, including team formation. However, they face scalability challenges as the number of criteria and options increases. This paper introduces a novel Hybrid evolutionary Algorithm integrated with PROMETHEE II, specifically designed for team formation. This hybrid approach combines the exploration power of evolutionary algorithms and the decision-making capabilities of PROMETHEE II, aiming to improve both performance and scalability in decision-making processes. Initial experiments demonstrate significant improvements in both solution quality and scalability compared to existing methods facing similar challenges. This research enables the creation of more efficient and effective team formation in complex decision-making scenarios.

关键词： Multi-Criteria Decision Making Team Formation evolutionary algorithms PROMETHEE II Algorithmic Efficiency Computational Optimization Team Composition Analysis Decision Support Systems

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A computational model for multiobjective optimization of multipolar stimulation in cochlear implants: An enhanced focusing approach

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 280卷

作者： Hernandez-Gil, Marcos Ramos-de-Miguel, Angel Greiner, David Benitez, Domingo Ramos-Macias, Angel Escobar, Jose M. Univ Las Palmas Gran Canaria Inst Intelligent Syst & Numer Applicat Engn SIANI Campus Univ Tafira Baja Las Palmas Gran Canaria 35017 Las Palmas Spain Complejo Hosp Univ Insular Materno Infantil Gran C Dept Otolaryngol Las Palmas Gran Canaria 35002 Las Palmas Spain

Multipolar stimulation has been demonstrated to improve auditory perception in individuals with cochlear implants by generating more focused electric fields through simultaneous activation of multiple electrodes. In this study, we propose a novel approach to multipolar stimulation that aims to achieve the narrowest possible pattern of current densities at target neurons. Our goal is to find the optimal profile of currents delivered by the electrodes that maximizes the focusing for a specific power consumption, or alternatively, which minimizes the power for a given focusing. To this end, we have designed two objective functions which are optimized through multiobjective evolutionary algorithms. These objective functions are evaluated using a patient-specific finite element volume conduction model that replicates the cochlear geometry and electrical behavior of the implant. Experimental results demonstrate that this approach achieves tighter current density focusing compared to phased-array stimulation, albeit with higher power consumption. Additionally, it is possible to reach non-dominated solutions that simultaneously improve the focusing and power consumption of both monopolar and phased-array stimulation.

关键词： Cochlear implant Multipolar stimulation Finite element method evolutionary algorithms Multiobjective optimization Optimum design

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An evolutionary Approach for Determining Electric Vehicle Charging Infrastructure Location: A Case Study in Montevideo 7th

An Evolutionary Approach for Determining Electric Vehicle Ch...

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7th Iberoamerican Congress on Smart Cities-ICSC CITIES

作者： Gordienko, Rodrigo Scaletti, Joaquin Nesmachnow, Sergio Toutouh, Jamal Univ Republica Montevideo Uruguay Univ Malaga Malaga Spain

ISBN: (纸本)9783031853234;9783031853241

This article presents an approach applying an evolutionary algorithm for the problem of locating the charging infrastructure for electric vehicles, a very relevant problem in the context of smart cities. An automatic method employing a multi-objective evolutionary algorithm, specifically NSGA-II, is developed with the objective of maximizing energy demand fulfillment while minimizing the associated costs. Furthermore, the approach is restricted to guaranteeing effective coverage of the designated area. A real case study is addressed using existing gas station locations and different service demand scenarios. The results demonstrate the effectiveness of the proposed approach to optimize the location of chargers according to the established criteria and highlight the potential of evolutionary optimisation techniques to solve complex urban planning problems.

关键词： electric vehicle charging infrastructure renewable energies evolutionary algorithms computational intelligence location smart cities

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Multi-Scale Features Are Effective for Multi-Modal Classification: An Architecture Search Viewpoint

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第2期35卷 1070-1083页

作者： Fu, Pinhan Liang, Xinyan Qian, Yuhua Guo, Qian Zhang, Yayu Huang, Qin Tang, Ke Shanxi Univ Inst Big Data Sci & Ind Taiyuan 030006 Shanxi Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab Comp Intelligence Shenzhen 518055 Guangdong Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China

Multi-modal neural architecture search (MNAS) is an effective approach to obtain task-adaptive multi-modal classification models. Deep neural networks, as currently main-stream feature extractors, can provide hierarchical features for each modality. Existing MNAS methods face difficulty in exploiting such hierarchical features due to their different form coexistence such as tensorial multi-scale features and vectorized penultimate features. Moreover, existing methods always focus on the evolution of fusion operators or vectorized features of all modalities, constraining search space. In this paper, a novel two-stage method called multi-modal multi-scale evolutionary neural architecture search (MM-ENAS) is proposed. The first stage unifies the representation form of hierarchical features by the proposed evolutionary statistics strategy. The second stage identifies the optimal combination of basic fusion operations for all unified hierarchical features by the evolutionary algorithm. MM-ENAS increases search space by simultaneously searching for feature statistical extraction methods, basic fusion operators and feature representation set consisting of tensorial multi-scale features and vectorized penultimate features. Experimental results on three multi-modal tasks demonstrate that the proposed method achieves competitive performance in terms of accuracy, search time, and number of parameters compared to existing representative MNAS methods. Additionally, the method exhibits fast adaptation to various multi-modal tasks.

关键词： Feature extraction Vectors Computer architecture Adaptation models Tensors Computational modeling Manuals Data mining Circuits and systems Transforms Multi-modal fusion feature statistics neural architecture search evolutionary algorithms

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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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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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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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