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Constrained-multiobjective evolutionary algorithm for Distribution System Reconfiguration under Severe Constraints

IFAC-PapersOnLine 2024年第13期58卷 62-67页

作者： Shinya Sekizaki Ichiro Nishizaki Tomohiro Hayashida Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima Hiroshima Japan

Distribution system reconfiguration problems pose significant challenges due to their multiple objectives, high computational complexity, and severe constraints. These challenges become even more pronounced with the integration of renewable energy sources, which introduce uncertainties and further tighten the constraints. This paper proposes a constrained multiobjective optimization evolutionary algorithm (CMOEA) based on a dual-stage approach with a dual-population genetic operator. The proposed CMOEA is applied to a case study of a distribution system reconfiguration problem with severe constraints, uncertainties, and high computational demands, and it shows good performance in finding infeasible solutions with small constraint violations and feasible solutions with small objectives.

关键词： Distribution system Reconfiguration problem multiobjective optimization multiobjective evolutionary algorithm Constrained multiobjective optimization problem evolutionary algorithm

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A Constrained multiobjective evolutionary algorithm Based on a Hybrid Constraint Handling Technique Using Population Trimming Strategy

A Constrained Multiobjective Evolutionary Algorithm Based on...

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2017 2nd International Conference on Electrical and Electronics: Techniques and Applications（EETA2017）

作者： You-xun KAN Xing-long JIANG The China University of Geosciences Shanghai Engineering Center for Microsatellites

For constrained multiobjective optimization problems (CMOPs), one of the fundamental issues faced by researchers is how to make comparisons between individuals within the population that results in a balanced selection of better individuals. The selection of better individuals need to be balanced between elitism, diversity and feasibility. In this paper, we propose a hybrid constraint handling technique of population trimming strategy and adaptive penalty function for multiobjective evolutionary algorithm NSGA-II to solve CMOPs. In our approach, a method of objectivization of constraint violations and proportional reduction is used to compare two individuals and trim the population, and as a result the new parent population consisted of the optimal feasible individuals and good infeasible individuals is obtained. To our knowledge, the distant matrix in proportional reduction procedure is firstly proposed for comparison and maintaining diversity in infeasible individuals. Furthermore, an adaptive penalty function method is utilized to give the fitness of individuals in parent population. Numerical simulations indicate that the proposed algorithm outperforms the current state-of-the-art algorithms, e.g. NSGA-II-CD,NAGA-II-WTY, in both convergence and diversity.

关键词： Constrained multiobjective optimization problems Constraint handling Adaptive penalty function multiobjective evolutionary algorithm Population trimming strategy

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Variable Reconstruction for evolutionary Expensive Large-Scale multiobjective Optimization and Its Application on Aerodynamic Design

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IEEE-CAA JOURNAL OF AUTOMATICA SINICA 2025年第4期12卷 719-733页

作者： Lin, Jianqing He, Cheng Tian, Ye Pan, Linqiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

Expensive multiobjective optimization problems (EMOPs) are complex optimization problems exacted from real-world applications, where each objective function evaluation (FE) involves expensive computations or physical experiments. Many surrogate-assisted evolutionary algorithms (SAEAs) have been designed to solve EMOPs. Nevertheless, EMOPs with large-scale decision variables remain challenging for existing SAEAs, leading to difficulties in maintaining convergence and diversity. To address this deficiency, we proposed a variable reconstruction-based SAEA (VREA) to balance convergence enhancement and diversity maintenance. Generally, a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables. Thus, the population can be rapidly pushed towards the Pareto set (PS) by optimizing low-dimensional weight variables with the assistance of surrogate models. Population diversity is improved due to the cluster-based variable reconstruction strategy. An adaptive search step size strategy is proposed to balance exploration and exploitation further. Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task. Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.

关键词： Optimization Computational modeling Iron Convergence Linear programming Predictive models Particle swarm optimization Kernel Costs Clustering algorithms Aerodynamic design large-scale optimization multiobjective evolutionary algorithm surrogate model variable reconstruction

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multiobjective backbone network architecture search based on transfer learning in steel defect detection

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NEUROCOMPUTING 2025年 635卷

作者： Zhao, Tianchen Wang, Xianpeng Song, Xiangman Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Shenyang Peoples R China Northeastern Univ Key Lab Data Analyt & Optimizat Smart Ind Minist Educ Shenyang Peoples R China Northeastern Univ Liaoning Engn Lab Data Analyt & Optimizat Smart In Shenyang Peoples R China Northeastern Univ Liaoning Key Lab Mfg Syst & Logist Optimizat Shenyang Peoples R China

In recent years, steel defect detection methods based on deep learning have been widely used. However, due to the shape specificity of steel defects and data scarcity, using existing convolutional neural network architectures for training requires significant expertise and time to fine-tune the hyperparameters. Transfer learning effectively tackles the challenges of data scarcity or limited computing resources by transferring domain knowledge from source tasks to related target tasks, reducing the resource consumption of model training from scratch. In this paper, we propose a transfer learning-based multiobjective backbone network architecture search method (TMBNAS). First, TMBNAS formulates defect detection network search as a multiobjective problem while optimizing its detection accuracy and model complexity. Second, an effective variable-length encoding strategy is designed to represent different building blocks and unpredictable optimal depths in convolutional neural networks, and targeted improvements are made to the crossover and mutation operators. For the specificity of the steel defect detection task, a transfer learning strategy based on similar knowledge is used to transfer the architecture and weight parameters obtained from the search in the source task to the target task, and adjust and optimize them. Finally, a dynamic adjustment mechanism based on actual constraints is designed during the search process to gradually approximate the optimal non-dominated solution set with higher detection accuracy without losing its population diversity. The proposed method is tested on the continuous casting slab and workpiece defect datasets. The experimental results show that the model searched by the proposed method can achieve better detection performance compared with manually designed deep learning algorithms and classical network architecture search methods.

关键词： Deep learning Neural architecture search (NAS) Defect detection multiobjective evolutionary algorithm Transfer learning

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Enhancing multiobjective Genetic algorithms for Pharmaceutical Batch Scheduling: A Study on Partitioned Selection with Constraints and Mutation with Greedy Local Search Strategy 34th

Enhancing Multiobjective Genetic Algorithms for Pharmaceutic...

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34th Brazilian Conference on Intelligent Systems

作者： Kohara, Debora T. de Oliveira, Gina M. B. Martins, Luiz G. A. Univ Fed Uberlandia Fac Comp Uberlandia MG Brazil

ISBN: (纸本)9783031790348;9783031790355

Product Manufacturing scheduling or Batch Sequencing (BS) in real-world pharmaceutical manufacturing poses a complex multiobjective optimization challenge, intensified by constraints and uncertain demands. These problems are typically non-linear, may leading to low convergence of feasible solutions, and the computationally intensive evaluation functions may hinder the adoption of certain techniques. multiobjective evolutionary algorithms (MOEAs) have been explored due to their flexible and efficient operators that can be applied to various non-linear multiobjective problems. This study proposes enhancements to MOEA operators, specifically a selection strategy with constraint-based partition for reinsertion to improve diversity and exploration, and a greedy local search strategy incorporated into the mutation operator to enhance the exploitation of the most promising regions. These enhancements were integrated into two models, ps-re and bl-bat, both of which demonstrated significant improvements in the performance metrics compared to a reference model.

关键词： multiobjective evolutionary algorithm Constrained Non-linear Optimization Genetic algorithms Batch Scheduling

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Software requirement optimization using a multiobjective swarm intelligence evolutionary algorithm

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KNOWLEDGE-BASED SYSTEMS 2015年第1期83卷 105-115页

作者： Chaves-Gonzalez, Jose M. Perez-Toledano, Miguel A. Navasa, Amparo Univ Extremadura Dept Comp Sci Caceres Spain

The selection of the new requirements which should be included in the development of the release of a software product is an important issue for software companies. This problem is known in the literature as the Next Release Problem (NRP). It is an NP-hard problem which simultaneously addresses two apparently contradictory objectives: the total cost of including the selected requirements in the next release of the software package, and the overall satisfaction of a set of customers who have different opinions about the priorities which should be given to the requirements, and also have different levels of importance within the company. Moreover, in the case of managing real instances of the problem, the proposed solutions have to satisfy certain interaction constraints which arise among some requirements. In this paper, the NRP is formulated as a multiobjective optimization problem with two objectives (cost and satisfaction) and three constraints (types of interactions). A multiobjective swarm intelligence metaheuristic is proposed to solve two real instances generated from data provided by experts. Analysis of the results showed that the proposed algorithm can efficiently generate high quality solutions. These were evaluated by comparing them with different proposals (in terms of multiobjective metrics). The results generated by the present approach surpass those generated in other relevant work in the literature (e.g. our technique can obtain a HV of over 60% for the most complex dataset managed, while the other approaches published cannot obtain an HV of more than 40% for the same dataset). (C) 2015 Elsevier B.V. All rights reserved.

关键词： Next release problem multiobjective evolutionary algorithm Software requirement selection Search-based software engineering Swarm intelligence Artificial bee colony

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SWAT -MOEA: SWAT Optimization Tool for Decision-Making in Agricultural Production Systems Among Competing Objectives

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JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 2025年第2期61卷

作者： Cram, Ana C. Moriasi, Daniel N. Moglen, Glenn E. Steiner, Jean L. Aguirre, Oswaldo F. Verser, J. Alan Xiang, Zaichen USDA ARS Natl Lab Agr & Environm Ames IA 50011 USA Univ North Carolina Charlotte Dept Civil & Environm Engn Charlotte NC USA Kansas State Univ Dept Agron Manhattan KS USA Texas A&M Univ Kingsville Mech & Ind Engn Dept Kingsville TX USA USDA ARS Oklahoma & Cent Plains Agr Res Ctr EI Reno OK USA Oklahoma State Univ Dept Biosyst & Agr Engn Stillwater OK USA

Identifying agricultural production systems that balance economic viability, conservation of soil and water resources, and a clean environment requires significant time and financial investments. This paper presents the SWAT-MOEA, a user-friendly standalone tool, which couples the Soil and Water Assessment Tool (SWAT) with a Multi-Objective evolutionary algorithm (MOEA) to determine Pareto optimal solutions for sustainable watershed management within a reasonable timeframe. The SWAT-MOEA is unique compared with existing tools in that it is publicly available for download along with a manual, it is user customizable, and can be used for studies of varying spatial scales from field to very large watersheds and daily to annual temporal scales. This integration enables simultaneous optimization of multiple objectives for existing SWAT projects. The tool identifies the placement of management strategies that achieve optimal trade-offs among competing objectives. Key SWAT-MOEA features include the ability to create and execute management practices directly through the SWAT-MOEA interface and to select objectives that reflect regional priorities. Results are stored in Excel for efficient display. SWAT-MOEA creates and stores Pareto graphs and geospatial maps of suggested solutions. A case study in the Fort Cobb Reservoir Experimental watershed located in Oklahoma is provided to demonstrate its use. The SWAT-MOEA executable and user's manual are available at SWAT-MOEA.

关键词： best management practices multiobjective evolutionary algorithm soil and water assessment tool sustainable intensification of agriculture watershed management

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Designing highway horizontal alignment considering construction cost and land use sustainability using the Envision rating system and NSGA-II

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2025年第3期10卷 1-16页

作者： Allahyari, Sina Nourzad, Seyed Hossein Hosseini Univ Tehran Dept Construct & Project Management Enghelab St Tehran Iran

Civil infrastructures are crucial for modern societies, yet they may negatively impact sustainability. The adverse impacts of linear infrastructures such as highways can be even more pronounced. One of the critical aspects of highways that affect sustainability is their horizontal alignment, which must be optimized to decrease their negative impact on their surroundings. This study presents a framework based on the Envision sustainability rating system, Nondominated Sorting Genetic algorithm II (NSGA-II), and Geographic Information System (GIS) that facilitates considerations of sustainability principles in addition to construction costs in highway alignment design. To this end, in this study, an Envision-based sustainability violation objective function was developed and embedded into NSGA-II alongside the construction cost objective. A combination of the Douglas-Pucker line simplification algorithm (DPA) and Least-cost path analysis (LCPA) was used to generate initial solutions and set up the optimization algorithm considering the problem's context. Then, considering three sample criteria from Envision, associated with land use, the framework was applied to a case study in three scenarios, and then their results were compared. These comparisons demonstrate that the Envision sustainability rating system can be used for highway alignment optimization with appropriate adjustments. This approach decreases the complexity of considering multiple sustainability criteria.

关键词： Highway development Sustainable infrastructure Land use optimization Sustainability assessment framework multiobjective evolutionary algorithm

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multiobjective Tuning of a Multitarget Tracking algorithm using an evolutionary algorithm

Multiobjective Tuning of a Multitarget Tracking Algorithm us...

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IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making

作者： Secrest, Barry R. Lamont, Gary B. USAF Inst Technol Grad Sch Engn & Management Dept Elect & Comp EngnWPAFB Dayton OH 45429 USA

ISBN: (纸本)9781424427642

Multitarget tracking MTT algorithms have been tuned by a variety of optimization methods using a single objective, but only recently have they been tuned with multiobjectives techique. The desire to compare single-objective MTT algorithms using numerous metrics is well documented in the literature for over a decade. We discuss an experiment to quantify the need or lack of need for Monte Carlo (MC) runs in tuning the parameters of a MTT algorithm using some of these metrics. The extreme computational requirement of running a MTT MC experiment for each individual evaluation function drives the need to determine the worth of doing so. The results of using a single run are compared to that of using a MC evaluation with multiple runs as compared to a multiobjective evolutionary algorithm approach. Additional analysis is performed on the search space demonstrating other useful information the decision maker may use to select an optimal operating point from a calculated Pareto Front.

关键词： Multitarget Tracking multiobjective evolutionary algorithm Monte Carlo Analysis Kalman Filter

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ISPEA: Improvement for the Strength Pareto evolutionary algorithm for multiobjective optimization with immunity 5

ISPEA: Improvement for the Strength Pareto Evolutionary Algo...

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5th International Conference on Computational Intelligence and Multimedia Applications

作者： Meng, HY Liu, SY Xidian Univ Dept Appl Math Xian 710071 Peoples R China

ISBN: (纸本)0769519571

Recently, there arise some important multiobjective evolutionary algorithms (MOEAs) over the years, among these MOEAs, Strength Pareto evolutionary algorithm (SPEA) seems the most effective technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems with several characteristics. Unfortunately, there are always some basic and obvious characteristics or knowledge in pending problem, where the loss due to this negligence is sometimes considerable in dealing with complex problems. Based on these reasons, an improvement on SPEA with immunity is given to restrain degeneracy of the evolution process, where the immune operator is realized by vaccine extraction, vaccination and immune selection in turn. Simulations show the ISPEA is effective and feasible.

关键词： evolutionary computation Pareto optimisation operations research ISPEA multiobjective evolutionary algorithm MOEA strength Pareto evolutionary algorithm SPEA multiobjective optimization Pareto-optimal set evolution process

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