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8th Annual Genetic and evolutionary Computation Conference

作者： Oesterreicher, Ingo Mitschele, Andreas Schlottmann, Frank Seese, Detlef Univ Karlsruhe TH Inst AIFB D-76128 Karlsruhe Germany GILLARDON AG Financial Software D-75015 Bretten Germany

ISBN: (纸本)9781595931863

Our paper concerns optimal combinations of different types of reinsurance contracts. We introduce a novel approach based on the Mean-Variance-Criterion to solve this task. Two state-of-the-art MOEAs are used to perform an optimization of yet unresolved problem instances. In addition to that, we focus on finding a dense set of solutions to derive analogies to theoretic results of easier problem instances.

关键词： multi-objective evolutionary algorithm optimal reinsurance mean-variance-criterion value-at-risk

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Optimal Testing Resource Allocation for Modular Software Systems based-on multi-objective evolutionary algorithms with Effective Local Search Strategy

Optimal Testing Resource Allocation for Modular Software Sys...

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IEEE 2nd Workshop on Memetic Computing (MC)

作者： Yu Shuaishuai Fei Dong Bin Li USTC Dep Elect Sci & Technol Hefei Peoples R China

ISBN: (纸本)9781467358910

Software testing is a very important part in software projects. As a key issue in software testing, Optimal Testing Resource Allocation Problems (OTRAPs) have drawn more and more attention recently. Along with the rapid increasing of the scale and complexity of software systems, the problems become more and more difficult to solve. Although some single objective optimization approaches had been used to solve such problems, quite a number of flaws were observed with these approaches, such as trapping into local optima, high computational complexity and few available optimal solutions. In this paper, to solve the problem of few available optimal solutions, an effective local search (ELS) is introduced into two effective multi-objective evolutionary algorithms: Nondominated Sorting Genetic algorithm II (NSGA-II) and Harmonic Distance Based multi-objective evolutionary algorithm (HaD-MOEA), advantages of this strategy over pure multi-objective approaches are testified on two OTRAPs with parallel-series modular software systems. To deal with the problem of high computational complexity, the proposed ELS is also embedded into another effective multi-objective algorithm, multi-objective evolutionary algorithm based on Decomposition (MOEA/D) to solve OTRAPs. Comprehensive experimental studies show the better performance over the state-of-the-art multi-objective approaches for OTRAPs.

关键词： multi-objective evolutionary algorithm parallel-series modular software system effective local search (ELS) NSGA-II MOEA/D software testing reliability and testing cost

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Surrogate model based multi-objective optimisation of supercritical CO2 ejectors

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JOURNAL OF SUPERCRITICAL FLUIDS 2025年 218卷

作者： Paul, Sanjoy Srikar, R. P. Rao, Srisha M., V Kumar, Pramod Indian Inst Sci Interdisciplinary Ctr Energy Res Bangalore 560012 India Indian Inst Sci Dept Aerosp Engn Bangalore 560012 India Indian Inst Sci Dept Mech Engn Bangalore 560012 India

A supercritical CO2 (sCO(2)) supersonic ejector improves the coefficient of performance (COP) in combined power and cooling systems by compressing a secondary stream through entrainment and mixing with a high-momentum primary stream. The performance of the ejector is crucial to system efficiency and is influenced by complex gas dynamic shock interactions and shear layers which is further complicated by the rapid variations in thermophysical of sCO(2). In this regard, aerodynamic duct shaping plays a pivotal role in optimizing ejector efficiency. The present paper seeks to optimize a sCO(2) ejector using a surrogate model derived from computational fluid dynamics data. The model relies on a comprehensive dataset generated using a simulation tool coupled with REFPROP database to account for variations in thermophysical properties of sCO(2). Subsequently, a genetic aggregation technique is used to train and improve the model via supervised machine learning. The influence of critical design parameters such as radius of mixing section, nozzle exit point, and mixing duct length on the performance of the ejector is enabled by a sensitivity analysis study facilitated by design space exploration. Finally, a multi-objective evolutionary algorithm is incorporated in the surrogate model to optimize the ejector performance by maximizing entrainment ratio and compression ratio, while minimizing entropy generation. It is found that stagnation temperature ratio is a key influencing parameter responsible for enhancing mixing layer growth to improve the ejector performance. The optimized ejector shows an enhanced efficiency of similar to 25 % compared to a non-optimized ejector.

关键词： Supercritical CO 2 ejector Surrogate model multi-objective evolutionary algorithm Stagnation temperature ratio CFD

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Edge-Rotated Cone Orders in multi-objective evolutionary algorithms for Improved Convergence and Preference Articulation

Edge-Rotated Cone Orders in Multi-objective Evolutionary Alg...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Wang, Yali Deutz, Andre Back, Thomas Emmerich, Michael Leiden Univ LIACS Leiden Netherlands

ISBN: (纸本)9781728125473

The Pareto dominance relation is a special case of a cone order. Cone orders and the Pareto order are translation invariant and also multiplication invariant by any positive real. Employment of more general cone orders instead of the Pareto order gives rise to interesting exploration opportunities for algorithm design. In this paper, the standard Pareto dominance relation has been extended to cone dominance with a pointed convex obtuse cone, a superset of the Pareto dominance cone, i.e., the non-negative orthant, by rotating the edges of the Pareto order cone. The basic idea is in line with earlier work on coneorders in multicriteria decision nuking and here, in particular, the cone-order is used (1) to increase solutions' dominance area and hence improve the convergence speed of the algorithm and (2) to formulate trade-off constraints. The minima of the obtuse cone order are also Pareto optimal. However. not all minima of the Pardo order are also minima of the obtuse cone order. Therefore we use the edge-rotated cone in an alternating manner with the standard Pareto cone to guarantee coverage of the entire Pareto front. The edge-rotated cones have been integrated in several state-of-the-art multi-objective evolutionary algorithms (MOEAs) and proven to be able to improve the performance of MOEAs on multi-objective optimization problems with the linear, concave, convex and disconnected Pareto front. Furthermore, when the edges of the cone are rotated by different angles, the algorithm obtains solution sets located in different areas of the Pareto front. This behavior can be used to take the preference of the targeted region on the Pareto front into the algorithm. In particular, by using obtuse cones, regions where trade-off is very unbalanced can be discarded. This is quantified by showing a relationship between the angle and the trade-off rate corresponding to this angle.

关键词： Cone order Pareto domination multi-objective evolutionary algorithm preference-based optimization algorithm

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Sizing mixed-mode circuits by multi-objective evolutionary algorithms

Sizing mixed-mode circuits by multi-objective evolutionary a...

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53rd Midwest Symposium on Circuits and Systems (MWSCAS 2010)

作者： Guerra-Gomez, I. Tlelo-Cuautle, E. McConaghy, Trent de la Fraga, Luis G. Gielen, Georges Reyes-Salgado, G. Munoz-Pacheco, J. M. INAOE Department of Electronics. M&#x00E9 xico

ISBN: (纸本)9781424477739

We show the behavior of the generations of two multi-objective evolutionary algorithms (MOEAs) for the optimal sizing of two mixed-mode circuits. The non-sorting genetic algorithm (NSGA-II), and the MOEA based on decomposition (MOEA/D) are used to size a second generation current conveyor (CCII+) and a current-feedback operational amplifier (CFOA). Both MOEAs take into account design constraints, and link HSPICE to evaluate the electrical characteristics of the CCII+ and CFOA. Differential evolution is used as genetic operator to show the behavior of the generations of the two MOEAs.

关键词： current conveyors SIZING evolutionary algorithm Hybrid modes design constraints multi-objective evolutionary algorithm Electric circuits ELECTRICAL CHARACTERISTICS current feedback operational amplifier Genetic Operator

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A New evolutionary multi-objective algorithm for Convex Hull Maximization

A New Evolutionary Multi-objective Algorithm for Convex Hull...

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IEEE Congress on evolutionary Computation

作者： Wenjing Hong Guanzhou Lu Peng Yang Yong Wang Ke Tang USTC-Birmingham Joint Research Institute in Intelligent Computation and its Applications (UBRI) School of Computer Science and Technology University of Science and Technology of China Hefei China 230027 School of Information Science and Engineering Central South University Changsha China 410083

ISBN: (纸本)9781479974931

Many real-world problems often have several, usually conflicting objectives. Traditional multi-objective optimization problems (MOPs) usually search for the Pareto-optimal solutions for this predicament. A special class of MOPs, the convex hull maximization problems which prefer solutions on the convex hull, has posed a new challenge for existing approaches for solving traditional MOPs, as a solution on the Pareto front is not necessarily a good solution for convex hull maximization. In this work, the difference between traditional MOPs and the convex hull maximization problems is discussed and a new evolutionary Convex Hull Maximization algorithm (ECHMA) is proposed to solve the convex hull maximization problems. Specifically, a Convex Hull-based sorting with Convex Hull of Individual Minima (CH-CHIM-sorting) is introduced, as well as a novel selection scheme, Extreme Area Extract-based selection (EAE-selection). Experimental results show that ECHMA significantly outperforms the existing approaches for convex hull maximization and evolutionary multi-objective optimization approaches in achieving a better approximation to the convex hull more stably and with a more uniformly distributed set of solutions.

关键词： multi-objective optimization convex hull maximization multi-objective evolutionary algorithm

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A copula-based chance-constrained programming framework for BMPs spatial configuration planning

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JOURNAL OF HYDROLOGY 2025年 659卷

作者： Ding, Wenlu Xia, Jun Huang, Sheng Sun, Lanxin Wuhan Univ State Key Lab Water Resources & Hydropower Engn Sc Wuhan 430072 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Cycle & Related Land Surface Proc Beijing 100101 Peoples R China

Optimizing spatial pattern of best management practices (BMPs) is crucial for managing non-point source pollution at the watershed scale. However, uncertainties of BMPs effects caused by variant hydro-meteorological conditions can pose challenges to achieving water quality management goals, emphasizing the need to incorporate these uncertainties into decision-making. This study established a credibility chance-constrained programming (CCP) framework that incorporates multiple techniques, including uncertainty modelling, stochastic simulation-optimization technique, clustering and fuzzy set theory. Correlated uncertainties of BMPs effects were described by vine copulas and propagated to the watershed outlet via a Markov-based surrogate model. Fast non-dominated sorting genetic algorithm (NSGA-II), was adopted to optimize cost and pollutant reduction goals while quantifying the reliability of each solution. The developed framework can provide the best compromising solutions (BCSs) among numerous solutions generated by NSGA-II. A case study was conducted to manage Total Nitrogen (TN) and Total Phosphorus (TP) pollution in the upper Boyang River watershed, China. The results show that the system cost increased by up to 3.4 times with the increase of reduction goal (30-60%). Specially, higher credibility levels allow for slight increases in pollution loads (1.48%-5.67%) without significantly raising costs. Overall, by incorporating uncertainty into pollutant reduction scenarios, the framework enables decision-makers to balance costs and environmental benefits while ensuring robust and reliable decisions. This approach is highly adaptable to BMP planning in complex environmental systems, enhancing its practicality for multi-objective watershed management.

关键词： Non-point source pollution BMPs-related decision-making Vine Copulas Uncertainty Chance-constrained programming multi-objective evolutionary algorithm

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Statistical Decision Support for Fitness Function Formulation to multi-objective evolutionary algorithms: a Case Study on Computer Automated Exterior Lighting Design

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IEEE LATIN AMERICA TRANSACTIONS 2016年第1期14卷 281-288页

作者： Rocha, H. X. Peretta, I. S. Lima, G. F. M. Boaventura, R. S. Marques, L. G. Yamanaka, K. IFG Campus Itumbiara Itumbiara Goias Brazil Univ Fed Uberlandia Av Engenheiro Dinz 1178CP 593 BR-38400 Uberlandia MG Brazil CGWORKS Uberlandia MG Brazil IFTM Campus Uberlandia Ctr Uberlandia MG Brazil

evolutionary algorithms are stochastic heuristics which can optimize over special functions, known as fitness functions, by manipulating the structure of candidate solutions known as individuals. multi-objective evolutionary algorithms can deal with many objectives to be optimized, whether concurrent or divergent, ending by returning an optimal frontier, i.e. a set of solutions all defined as Pareto optimal. The idea behind using evolutionary algorithms to perform computer automated design is to be able to formulate a fitness function that, starting from a candidate solution, could reflect the impact the evaluated individual has on those objectives to be optimized. The case study presented in this work is an application for computer automated exterior lighting design which has some concurrent objectives to be optimized: the energy efficiency and the illumination quality. This work investigates four metrics to illumination quality and two metrics for energy efficiency as possible proposals to the fitness function formulation. Eight variations were designed as combinations of pairs from those metrics. To help in the decision process, the statistical hypothesis test known as difference of means is then used to enable comparisons between those variations. This test is performed two by two and three decision matrices is then derived, the ones about global uniformity, mean electrical power, and mean efficiency class index. The concept of Pareto's "statistical dominance", defined in this work and based on statistical evidences, indicates a final decision about which one from the previous designed variations of fitness function is the more appropriated for the presented problem.

关键词： multi-objective evolutionary algorithm Fitness Function Formulation Hypothesis Test for Difference of Means Exterior Lighting Design Illumination Quality Energy Efficiency Pareto's Statistical Dominance

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A two-phase framework of locating the reference point for decomposition-based constrained multi-objective evolutionary algorithms

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KNOWLEDGE-BASED SYSTEMS 2022年 239卷 107933-107933页

作者： Peng, Chaoda Liu, Hai-Lin Goodman, Erik D. Tan, Kay Chen South China Agr Univ Coll Math & Informat Guangzhou Guangdong Peoples R China Guangdong Univ Technol Sch Math & Stat Guangzhou Guangdong Peoples R China Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI USA Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Reference point is a key component in decomposition-based constrained multi-objective evolutionary algorithms (CMOEAs). A proper way of updating it requires considering constraint-handling techniques due to the existing constraints. However, it remains unexplored in this field. To remedy this issue, this paper firstly designs a set of benchmark problems with difficulties that a CMOEA must update the reference point effectively. Then a two-phase framework of locating the reference point is proposed to enhance performance of the current decomposition-based CMOEAs by evolving two populations- the main and external population. At the first phase, the external population evolves along with the main population to identify the approximate locations of the constrained and unconstrained Pareto front (PF). At the second phase, a location estimation mechanism is designed to estimate the best fit reference point between the two PFs for the main population by evolving the external population. Besides, a replacement strategy is used to drive the main population to the promising regions. Experimental studies are conducted on 26 benchmark problems, and the results highlight the effectiveness of the proposed framework. (c) 2021 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Referent point Decomposition Constraint-handling technique

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Sports mental health monitoring based on multi-objective evolutionary algorithms in the Internet of Things

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SOFT COMPUTING 2023年

作者： Li, Xiaoyan Shi, Yunjing Gao, Qiang Shijiazhuang Univ Appl Technol Dept Phys Educ Shijiazhuang 050081 Hebei Peoples R China Hebei Univ Chinese Med Dept Psychol Shijiazhuang 050200 Hebei Peoples R China

With the increasing popularity of sports such as football and basketball, many policy reforms are striving to strengthen the education of sports talents in China. The release of relevant policies can further promote the development of sports and enable more and more people to promote physical and mental health through sports activities. The emergence of the Internet of Things, especially the emergence of intelligent wearable devices, has brought many conveniences to the long-term detection and analysis of human health and motion parameters. By measuring human health and sports information, it is possible to quantitatively monitor the amount of exercise and physical health level of athletes, in order to achieve more effective training effects. In addition, for complex multi-objective problems, multi-objective evolutionary algorithms can effectively solve problems that are difficult to handle, suppress, and balance targets. Therefore, this article uses a multi-objective evolutionary algorithm to complete mental health monitoring based on the Internet of Things. It monitors the impact of sports on students' mental health, compares their mental health before and after the experiment, and analyzes the data changes before and after the experiment to summarize the impact of sports on students' mental health. The main characteristics of multi-objective evolutionary algorithm are excellent convergence and diversity. This article uses its main characteristics to real-time monitor the mental health level of college students during exercise through the Internet of Things, opening up a new way to improve their mental health.

关键词： multi-objective evolutionary algorithm Internet of Things Sports Mental health

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