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Efficient risk-based inspection framework: Balancing safety and budgetary constraints

RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 253卷

作者： Javid, Y. Kharazmi Univ Engn Fac Ind Engn Dept Tehran Iran

Efficient equipment maintenance is paramount across various industries to mitigate energy wastage and avert potential disasters such as hazardous emissions, fires, and explosions. Within this context, the adoption of risk- based inspection strategies has emerged as a crucial method for assessing equipment integrity. This study integrates the principles of Risk-Based Inspection (RBI) with a novel two-objective mathematical model, resulting in a comprehensive framework for equipment inspection programs. The primary aim of this framework is to reduce overall risk exposure while optimizing inspection expenditures. Unlike conventional approaches, this methodology eliminates the necessity to define threshold risk levels. By integrating inspection costs into the model, the assessment of Failure Consequences, and thereby, the decision-making process has been streamlined. This innovative algorithm effectively balances the reduction of failure likelihood with the minimization of inspection costs, enhancing decision-making capabilities. Importantly, this approach offers significant protection against energy wastage and the occurrence of leaks through robust risk management strategies. The algorithm employs specialized operators to expedite the discovery of optimal solutions. Empirical validation through a case study conducted at a Petrochemical Plant highlights the practicality and effectiveness of the proposed framework.

关键词： Inspection program Risk-based inspection multi-objective genetic algorithm Risk reduction

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Optimization of inlet and outlet parameters of circular recirculating aquaculture tank based on CFD-ML-MOGA

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2025年第3期13卷

作者： Zhang, Guangpeng Yu, Guanghui Li, Daoliang Wang, Yang China Agr Univ Natl Innovat Ctr Digital Fishery Beijing 100083 Peoples R China Minist Agr & Rural Affairs Key Lab Smart Farming Technol Aquat Anim & Livesto Beijing 100083 Peoples R China China Agr Univ Beijing Engn & Technol Res Ctr Internet Things Agr Beijing 100083 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China State Key Lab Efficient Utilizat Agr Water Resourc Beijing 100083 Peoples R China

Recirculating aquaculture system (RAS) is a highly efficient freshwater aquaculture model that can improve aquaculture efficiency, conserve resources, and reduce wastewater discharge. The flow rate in the aquaculture water body is the main factor affecting the aquaculture environment, and maintaining its stability can significantly improve the growth efficiency of fish fry in recirculating aquaculture tanks. Combining computational fluid dynamics (CFD) with machine learning (ML) and multi-objective genetic algorithm (MOGA) is an effective, low-cost, and implementable optimization method for aquaculture tank parameters, but most of the studies have been studied for octagonal tank. Therefore, we took circular recirculating aquaculture tank as the study object, constructed CFD simulation models, used pearson correlation analysis to assess the degree of parameter influence, built ANN models for mean flow rate and standard deviation (STD) of the flow rate, and optimized the parameter combinations using the non-dominated sorting genetic algorithm-II (NSGA-II) algorithm. The results show that the three parameters that have a greater influence on the mean velocity and STD of velocity are the inlet velocity, inlet diameter and inlet angle. The optimal parameter combinations are an inlet velocity of 0.91 m/s, an inlet diameter of 9.4 mm, and an inlet angle of 30.7 degrees, which increases the mean flow rate by 75 % and reduces the STD of the flow rate by 2 % relative to the baseline model. Finally, combining CFD with ML and MOGA is important in water homogeneity improvement and energy saving in recirculating aquaculture system.

关键词： Computational fluid dynamics Circular recirculating aquaculture tank multi-objective genetic algorithm Machine learning Inlet and outlet parameters

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multi-objective optimisation design of stator slot parameters for bearingless induction motors

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INTERNATIONAL JOURNAL OF ELECTRONICS 2025年

作者： Shen, Zijun Yang, Zebin Sun, Xiaodong Pan, Wei Jiangsu Univ Sch Elect Informat Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Automot Engn Res Inst Zhenjiang Peoples R China

In bearingless induction motors (BIM), the performance is significantly influenced by two sets of windings embedded in the stator slots for suspension and torque. To enhance the suspension and torque performance of BIMs, a multi-objective optimisation design method for BIM stator slot parameters was proposed in this paper. Firstly, based on the analysis of the mechanism for generating suspension force in BIMs and the identification of optimisation parameters for the motor's stator slots, high-sensitivity optimisation parameters had been determined through experiments using the Taguchi method and sensitivity analysis. Secondly, the BIM stator slots were modelled using the response surface methodology, combined with an enhanced multi-objective non-dominated sorting genetic algorithm (NSGA-II). A comprehensive optimisation was conducted for the high-sensitivity parameters, namely slot width Hs0, slot opening depth Hs1, and slot depth Bs0, to identify the optimal parameter combination that maximises both suspension and torque performance. The effectiveness of stator parameter optimisation was verified by comparing the motor's torque and suspension force performance before and after optimisation. Finally, a physical prototype of the optimised motor was constructed, and an experimental platform was established to test the performance of the optimised motor. Both simulation and experimental results indicated that the optimised motor experienced an increase in average suspension force, with reductions in both torque and suspension force ripple, leading to a significant enhancement in BIM performance.

关键词： Bearingless induction motor response surface modelling multi-objective genetic algorithm optimization design Taguchi method

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multi-objective optimization of comprehensive performance in airfoil channel PCHEs using Bezier curves

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APPLIED THERMAL ENGINEERING 2025年 261卷

作者： Liu, Rui Zhang, Chuanliang Zhao, Bin Chen, Jiaxiang Wang, Ziyi Changsha Univ Sci & Technol Coll Energy & Power Engn Changsha 410114 Peoples R China Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha 410114 Peoples R China

The airfoil fin (AFF) Printed circuit heat exchanger (PCHE) has attracted significant attention for its excellent comprehensive performance. This study proposes an optimized design for AFF PCHE to enhance the comprehensive performance by integrating Bezier curves, computational fluid dynamics (CFD), and multi-objective genetic algorithm (MOGA). A set of 12 Bezier curve-based variables is utilized to define and control the airfoil geometry, with optimization targets set on two comprehensive evaluation criteria: the first enhanced ratio (n1) and the third enhanced ratio (n3). The MOGA-generated Pareto front reveals the evolution of AFF structures in relation to n1 and n3. Results show that as the leading and trailing edges of the AFFs become sharper and the thickness decreases, the n1 of the PCHE channel gradually increases, while n3 decreases. Conversely, as the thickness of the AFFs increases and the trailing edge shape transitions from blunt to elliptical and finally to round, n3 significantly increases while n1 decreases. Furthermore, when changes focus mainly on the leading edge of the AFFs, n3 improves without markedly affecting n1. Compared to the traditional airfoil channel, the n1 of the Fin-b channel increases by 3.1%-10.8%, demonstrating its greater suitability under identical flow rate conditions. Similarly, the n3 of the Fin-g channel is 1.4%-11.6% higher than that of the traditional airfoil channel, highlighting its superior performance under identical pumping power conditions. The present work provides a valuable reference for optimizing the design of AFF PCHEs under identical flow rate and pumping power conditions.

关键词： Airfoil fin PCHE Bezier curves Pareto front multi-objective genetic algorithm Comprehensive performance

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Augmented genetic algorithm v3 for multi-objective PDN Decap Optimization

Augmented Genetic Algorithm v3 for Multi-Objective PDN Decap...

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Joint International Symposium on Electromagnetic Compatibility, Signal & Power Integrity: EMC Japan/AsiaPacific International Symposium on Electromagnetic Compatibility (EMC/APEMC)

作者： Manoharan, Haran Juang, Jack Wang, Hanfeng Pan, Jingnan Gao, Xu Qiu, Kelvin Hwang, Chulsoon Missouri Univ Sci & Technol EMC Lab Rolla MO 65409 USA Google Inc Mountain View CA USA

ISBN: (纸本)9784885523472;9798350349498

This paper proposes a novel approach for generating the initial population, termed disproportionate initialization, to enhance the genetic algorithm's efficiency in optimizing the number of decaps. This method is specifically designed to accelerate convergence, improving the algorithm's ability to find optimal solutions. The algorithm is improved to simultaneously reduce the number of decoupling capacitors and achieve the lowest cost, aiming to identify the global minima for comprehensive PDN optimization.

关键词： multi-objective genetic algorithm disproportionate initial population population diversity decap optimization

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Shape Optimization in Product Design Using Interactive genetic algorithm Integrated with multi-objective Optimization 10th

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10th multi-Disciplinary International Workshop on Artificial Intelligence (MIWAI)

作者： Kielarova, Somlak Wannarumon Sansri, Sunisa Naresuan Univ Fac Engn ID3 Ind Design Decis & Dev Res Unit Phitsanulok Thailand Naresuan Univ Fac Engn Dept Ind Engn Phitsanulok Thailand

ISBN: (纸本)9783319493978;9783319493961

This paper proposes an interactive genetic algorithm (IGA) integrated with multi-objective genetic algorithm (MOGA) in development of a generative design system. IGA is used in initializing and handling single dimensionally qualitative objectives. MOGA is used in optimizing two quantitative objectives. Qualitative factors are considered as design objectives to be optimized together with quantitative criteria. The multi-objective optimization is regarded to concurrent handling of two quantitative criteria. Shape of product is modeled by parametric modeling with Rhinoceros and Grasshopper. IGA is processed using Galapagos in Grasshopper. Shape optimization of the product is processed by using MOGA in MATLAB and linked to Grasshopper. Pareto-optimal front is generated to show the optimal solutions, which is able to support designers in decision making. The perfume bottle design is used as an illustration of the proposed framework, but the framework is applicable to other design problems.

关键词： Generative design Interactive genetic algorithm multi-objective genetic algorithm Qualitative multi-objective optimization Pareto-optimal front Bottle design

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An Improved genetic algorithm for multi-objective Flexible Job-shop Scheduling Problem

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International Conference on Manufacturing Science and Engineering (ICMSE 2009)

作者： Zhang, Chaoyong Wang, Xiaojuan Gao, Liang Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Dept Ind & Mfg Syst Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9780878492800

Flexible job shop scheduling problem (FJSP) is an extended traditional job shop scheduling problem, which more approximates to real scheduling problems. This paper presents a multi-objective genetic algorithm (GA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved multi-objective GA, the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Advanced crossover and mutation operators are proposed to adapt to this special chromosome structure. The proposed algorithm is evaluated on three representative instances and the computational results and comparison with some other approaches show that the proposed multi-objective algorithm is effective and potential.

关键词： Flexible job shop scheduling multi-objective genetic algorithm Pareto-optimality immune and entropy principle

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APPLICATION OF THE multi-objective PARETO-BASED genetic algorithm IN SOLVING GEOSPATIAL OPTIMISATION PROBLEMS

APPLICATION OF THE MULTI-OBJECTIVE PARETO-BASED GENETIC ALGO...

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9th International multidisciplinary Scientific Geo-Conference and Expo

作者： Ponjavic, Mirza Avdagic, Zikrija Karabegovic, Almir Univ Sarajevo Dept Comp Sci & Informat Fac Elect Engn Sarajevo 71000 Bosnia & Herceg Univ Sarajevo Fac Civil Engn Dept Geodesy Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9789549181814

This work studies the development and application of the multi-objective genetic algorithm based on the Pareto approach, as a tool for the decision making support in the geospatial analysis. Pareto-based evolutionary mechanism developed as an approach to multi-objective geospatial optimisation operates with fixed parameters of genetic operators. It can be used as efficient tool for multi-objective planning both for their power and flexibility and the fact that they generate a whole set of good solutions rather than just one "optimal" solution. The implementation of the suggested multi-objective Pareto based genetic algorithm over selected geospatial optimisation problem of fire station location demonstrates its ability of the discovery of multiple compromise solutions in a real spatial problem domain.

关键词： multi-objective genetic algorithm geospatial analysis geoinformation system location-allocation problem

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multi-objective Optimization of PID Controller for Coupled-Tank Liquid-Level Control System Using genetic algorithm 2

Multi-Objective Optimization of PID Controller for Coupled-T...

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2nd International Conference on Soft Computing for Problem Solving (SocProS)

作者： Singh, Sanjay Kr Katal, Nitish Modani, S. G. Amity Univ Dept ECE ASET Jaipur Rajasthan India Anand Int Coll Engn Dept ECE Jaipur Rajasthan India Malaviya Natl Inst Technol Jaipur Rajasthan India

ISBN: (纸本)9788132216025;9788132216018

The main aim of this chapter is to obtain optimal gains for a PID controller using multi-objective genetic algorithm used in a coupled-tank liquid-level control system. Liquid level control system is a nonlinear system and finds a wide application in petrochemical, food processing, and water treatment industries, and the quality of control directly affects the quality of products and safety. This chapter employs the use of multi-objective genetic algorithm for the optimization of the PID gains for better plant operations in contrast to conventional tuning methods and GA. The simulations indicate that better performance is obtained in case of multi-objective genetic algorithm-optimized PID controller.

关键词： PID controller multi-objective genetic algorithm PID optimization Liquid level control

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CBGA-ES: A Cluster-Based genetic algorithm with Elitist Selection for Supporting multi-objective Test Optimization 10

CBGA-ES: A Cluster-Based Genetic Algorithm with Elitist Sele...

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10th IEEE International Conference on Software Testing, Verification and Validation (ICSTW)

作者： Pradhan, Dipesh Wang, Shuai Ali, Shaukat Yue, Tao Liaaen, Marius Certus V&V Ctr Simula Res Lab Oslo Norway Univ Oslo Oslo Norway Cisco Syst Oslo Norway

ISBN: (纸本)9781509060313

multi-objective search algorithms (e.g., non dominated sorting genetic algorithm II (NSGA-II)) have been frequently applied to address various testing problems requiring multi-objective optimization such as test case selection. However, existing multi-objective search algorithms have certain randomness when selecting parent solutions for producing offspring solutions. In the worse case, suboptimal parent solutions may result in offspring solutions with bad quality, and thus affect the overall quality of the next generation. To address such a challenge, we propose a cluster-based genetic algorithm with elitist selection (CBGA-ES) with the aim to reduce such randomness for supporting multi-objective test optimization. We empirically compared CBGA-ES with random search, greedy (as baselines) and four commonly used multi-objective search algorithms (e.g., NSGA-II) using two industrial and one real world test optimization problem, i.e., test suite minimization, test case prioritization, and test case selection. The results showed that CBGA-ES significantly outperformed the baseline algorithms (e.g., greedy), and the four selected search algorithms for all the three test optimization problems. CBGA-ES managed to outperform more than 75% of the objectives for all the four algorithms in each test optimization problem. Moreover, CBGA-ES was able to improve the quality of the solutions for an average of 32.5% for each objective as compared to the four algorithms for the three test optimization problems.

关键词： multi-objective test optimization Search multi-objective genetic algorithm Cluster

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