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CMOPEO-OP: constrained multi-objective population extremal optimization-based optimal planning of standalone microgrids

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 92卷

作者： Zeng, Guo-Qiang Qin, Zhen Lu, Kang-Di Li, Li-Min Wenzhou Univ Natl Local Joint Engn Res Ctr Digitalize Elect Des Wenzhou 325035 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China

A standalone microgrid is crucial for delivering electricity to residential and remote regions by combining various distributed generation sources. A major challenge in academia and engineering is developing an optimal planning approach for distributed generations and storage system in a standalone microgrid, taking into account multi-objective performance metrics like reliability, cost and environment. In this paper, we propose a novel constrained multi-objective population extremal optimization-based optimal planning approach for standalone microgrids, called CMOPEO-OP. We first transform the optimal planning problem into a constrained multi-objective optimization problem, where annualized cost of the system, loss of power supply probability (LPSP), and fuel emissions are considered as the three objective functions to be minimized simultaneously. To ensure the reliability of system operation, we use LPSP as a constraint. The decision variables include the number of components in standalone microgrids, e.g., photovoltaics (PV) panels, wind turbines, batteries, and diesel generators, the height of the wind turbine tower, and the inclination angle of PV panels, where the first four are integer variables and the last two are real variables. Then, to effectively and efficiently deal with this problem, we propose the epsilon adaptive trade-off model (epsilon-ATM) to deal with constraint and combine a population extremal optimization mechanism. In the search process, due to the introduction of epsilon, epsilon-ATM mechanism can better balance the relationship between constraints and objective values. Additionally, an elaborated hybrid mutation operation and an external archive update mechanism are developed in CMOPEO-OP. The performance of the CMOPEO-OP method is illustrated on a standalone microgrid by comparing with multi-objective particle swarm optimization-based optimal planning (MOPSO-OP) method and multi-objective differential evolution-based optimal planning (MOD

关键词： Optimal planning Standalone microgrid Constraint handling mechanism constrained multi-objective optimization Population extremal optimization

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Membrane computing for IoT task offloading: An efficient multi-objective constrained optimization framework

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APPLIED SOFT COMPUTING 2025年 169卷

作者： Tuo, Shouheng Huyan, Yihao Fan, Ting Zhao, Yong Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Shaanxi Peoples R China Xian Key Lab Big Data & Intelligent Comp Xian 710121 Shaanxi Peoples R China

The computational tasks associated with Internet of Things (IoT) applications have become increasingly complex, with IoT devices (IoTDs) now being utilized in a multitude of contexts across people's daily lives. In view of the restricted computing resources and battery life of IoT devices, an increasing number of computing tasks are being handled by cloud servers. However, the restricted communication range of cloud servers gives rise to significant communication costs. Consequently, these complex computational tasks are transferred to edge servers situated in close proximity to IoT devices, with the objective of mitigating transmission delay and costs. To address the challenge of offloading decisions and obtain a multi-objective offloading decision that satisfies multiple constraints in an edge-cloud scenario, this study presents a framework of multi-objective constrained evolutionary algorithms based on membrane computing for solving constrained multi-objective computational offloading problems (MCMS-CMOEA). The framework draws inspiration from the independent parallelism observed within membrane computing membranes and the information exchange between them. Its objective is to minimize latency and energy consumption. The framework is structured into three phases, with the aim of exploring the solution space and increasing the diversity of the final solution set in a stepwise manner. In order to investigate the performance of the MCMS-CMOEA algorithm, a comparative analysis was conducted, in which the proposed algorithm was tested against nine state-of-the-art constrained multi-objective Evolutionary Algorithms (CMOEAs) in three constrained multi-objective benchmark suites with different characteristics and challenges. Furthermore, the MCMS-CMOEA is compared with four offloading schemes on computational offloading problems of varying dimensions. The convergence and diversity of the algorithm are evaluated by two different comprehensive metrics: inverted generation

关键词： Edge computing Membrane computing (MC) constrained multi-objective optimization Internet of things (IoT)

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optimization of Built-In Self-Test test chain configuration in 2.5D Integrated Circuits Using constrained multi-objective Evolutionary Algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 143卷

作者： Yang, Zhe Deng, Libao Li, Chunlei Zhang, Lili Harbin Inst Technol Sch Informat Sci & Engn Weihai 264299 Peoples R China Dublin City Univ Sch Comp Dublin D09V209 Ireland Natl Univ Ireland Dept Comp Sci Maynooth W23F2H6 Dublin Ireland

2.5D Integrated Circuit (2.5D IC) is an advanced packaging technology. This technology facilitates the dense integration of multiple dies by adding passive components to the silicon interposer. However, the high- density stacking of dies has increased the complexity of IC. Its highly interconnected nature presents a series of challenges for IC testing. As for 2.5D IC testing utilizing the Built-In Self-Test(BIST) architecture, a dual-objective optimal model is established for test chain configuration. The model aims to minimize both hardware testing costs and test time, considering constraints related to maximum power consumption and maximum testing time. Subsequently, A generic encoding and decoding method is devised to enable the direct application of existing multi-objective Evolutionary Algorithms (MOEAs) and constrained multi-objective Evolutionary Algorithms (CMOEAs) to solve the test chain configuration problem. Simultaneously, a dual- stage dual-population CMOEA is devised, integrating diverse local search strategies and adaptively adjusting them based on a successful incentive mechanism. Additionally, a constraint handling framework is introduced, utilizing the relationship between the constrained Pareto Front (CPF) and Unconstrained Pareto Front (UPF) to assist in searching for solutions that satisfy constraints. This framework also adaptively allocates computational resources by assessing the collaborative effects of auxiliary tasks. multiple experiments are designed and conducted to demonstrate the correctness and effectiveness of the proposed model and algorithm. Comparative analysis with 8 MOEAs or CMOEAs reveals that the proposed algorithm outperforms in solving the test chain configuration problem.

关键词： 2.5D Integrated Circuit Test chain configuration problem constrained multi-objective optimization problems Constraint handling techniques Built-In Self-Test

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A multi-Period constrained multi-objective Evolutionary Algorithm with Orthogonal Learning for Solving the Complex Carbon Neutral Stock Portfolio optimization Model

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Journal of Systems Science & Complexity 2023年第2期36卷 686-715页

作者： CHEN Yinnan YE Lingjuan LI Rui ZHAO Xinchao School of Science Beijing University of Posts and TelecommunicationsBeijing 100876China Harbin Institute of Technology(Shenzhen) Shenzhen 518055China

Financial market has systemic complexity and *** investors,return and risk often *** to rationally allocate funds into different assets and achieve excess returns with effectively controlling risk are main problems to be solved in the field of portfolio optimization(PO).At present,due to the influence of modeling and algorithm solving,the PO models established by many researchers are still mainly focused on single-stage single-objective models or single-stage multiobjective *** is actually considered as a multi-stage multi-objective optimization problem in real investment *** is more difficult than the previous single-stage PO model for meeting the realistic *** this paper,the authors proposed a mean-improved stable tail adjusted return ratio-maximum drawdown rate(M-ISTARR-MD)PO model which effectively characterizes the real investment *** order to solve the multi-stage multi-objective PO model with complex multi-constraints,the authors designed a multi-stage constrained multi-objective evolutionary algorithm with orthogonal learning(MSCMOEA-OL).Comparing with four well-known intelligence algorithms,the MSCMOEA-OL algorithm has competitive advantages in solving the M-ISTARR-MD model on the proposed constructed carbon neutral stock *** paper provides a new way to construct and solve the complex PO model.

关键词： constrained multi-objective optimization carbon-neutral multi-period constrained multiobjective evolutionary algorithm orthogonal learning portfolio optimization

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constrained multi-objective Water Evaporation optimization Algorithm Based on Decomposition With ε-Constraint Handling Technology

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IEEE ACCESS 2020年 8卷 130986-131004页

作者： Wang, Yan-Jiao Zhou, Sheng-Nan Che, Xiang-Yang Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China

In order to solve the constrained multi-objective problem more effectively, the constrained multi-objectivewater evaporation optimization algorithm based on decomposition with epsilon-constraint handling technology (MOEA/D-WEO-epsilon CHT) is proposed in this paper. MOEA/D-WEO-epsilon CHT uses the MOEA/D framework to deal with multi-objective, epsilon-constraint handling technology to handle constraints and WEO algorithm to perform the main body evolution operation. Firstly, an external memory population in which the abandoned excellent parent can be kept is established for each subproblem, which make the abandoned parent be fully utilized by participating in the subsequent evolution. Secondly, some strategies of WEO, such as calculation and normalization method of individual fitness and calculation method of step size S, are improved to make WEO suitable for solving constrained multi-objective problems and helpful to obtain the Pareto solution set with better convergence and distribution. Thirdly, the epsilon(t) calculation method and retention mechanism of individuals are improved to reasonably coordinate feasible and infeasible individuals in different evolution stages. To evaluate the performance of the proposed algorithm, the CTP and CF benchmark instances and an engineering optimization problem are studied. The experimental results illustrate that MOEA/D-WEO-epsilon CHT is more effective than other related algorithms on these benchmark instances, in terms of convergence and distribution of the obtained solution sets.

关键词： constrained multi-objective optimization external memory population water evaporation optimization epsilon-constraint

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A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization

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APPLIED SOFT COMPUTING 2018年 70卷 680-700页

作者： Zain, Mohamad Zihin bin Mohd Kanesan, Jeevan Chuah, Joon Huang Dhanapal, Saroja Kendall, Graham Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia Univ Malaya Fac Engn Expert Syst & Optimizat Grp Kuala Lumpur 50603 Malaysia Univ Nottingham Sch Comp Sci Jubilee Campus Nottingham NG8 1BB England

Due to increased search complexity in multi-objective optimization, premature convergence becomes a problem. Complex engineering problems poses high number of variables with many constraints. Hence, more difficult benchmark problems must be utilized to validate new algorithms performance. A well-known optimizer, multi-objective Particle Swarm Optimizer (MOPSO), has a few weakness that needs to be addressed, specifically its convergence in high dimensional problems and its constraints handling capability. For these reasons, we propose a modified MOPSO (M-MOPSO) to improve upon these aspects. M-MOPSO is compared with four other algorithms namely, MOPSO, multi-objective Grey Wolf Optimizer (MOGWO), multi-objective Evolutionary Algorithm based on Decompositions (MOEA/D) and multi-objective Differential Evolution (MODE). M-MOPSO emerged as the best algorithm in eight out of the ten constrained benchmark problems. It also shows promising results in bioprocess application problems and tumor treatment problems. In overall, M-MOPSO was able to solve multi-objective problems with good convergence and is suitable to be used in real world problem. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multi-objective particle swarm optimization Swarm intelligence constrained multi-objective optimization Fed-batch fermentation Tumor treatment

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A double auxiliary optimization constrained multi-objective evolutionary algorithm

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MATHEMATICS AND COMPUTERS IN SIMULATION 2024年 220卷 567-579页

作者： Yang, Yongkuan Yan, Bing Kong, Xiangsong Zhao, Jing Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Key Lab Frontier Elect Power Equipment & Intellige Xiamen 361024 Peoples R China

In evolutionary constrained multi-objective optimization, the use of auxiliary optimization is gradually attracting attention. It is noted that different forms of auxiliary optimization have different advantages. Combining these advantages in an appropriate manner can further improve the algorithm's performance. Motivated by this inspiration, we propose a double auxiliary optimization constrained multi-objective evolutionary algorithm, namely DAO. In DAO, two auxiliary optimizations, i.e., the unconstrained optimization and the (M+1)-objective optimization, are applied in a proposed tri-population co-evolution strategy. In this strategy, three populations are used to optimize the core optimization and the two auxiliary optimizations in an interactive evolution form. Furthermore, DAO develops a (M+1)-objective environmental selection strategy to deeply explore the boundary between the feasible and infeasible regions. In experimental studies, the performance of DAO and four other state -of -the -art algorithms is evaluated on three distinct benchmark test suites and two intricate real -world problem scenarios. The outcome of the comprehensive evaluation shows the competitive of DAO solving constrained multi-objective optimization problems.

关键词： Evolutionary algorithms constrained multi-objective optimization multi-auxiliary optimization Tri-population co-evolution

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An Integrated Approach to Designing Robust Gas-Bearing Supported Turbocompressors Through Surrogate Modeling and constrained All-At-Once multi-objective optimization

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JOURNAL OF MECHANICAL DESIGN 2024年第12期146卷 121706-121706页

作者： Massoudi, Soheyl Picard, Cyril Schiffmann, Juerg Ecole Polytech Fed Lausanne EPFL Lab Applied Mech Design CH-1015 Lausanne Switzerland MIT Dept Mech Engn Cambridge MA 02139 USA

This research introduces an innovative framework to engineering design to tackle the challenges of robustness against manufacturing deviations and holistic optimization simultaneously in a multi-disciplinary, multi-subsystems context. The methodology is based on an application of ensemble artificial neural networks, which significantly accelerates computational processes. Coupled with the non-dominated sorting genetic algorithm III, this approach facilitates efficient multi-objective optimization, yielding a comprehensive Pareto front and high-quality design solutions. Here, the framework is applied to the design of gas-bearing-supported turbocompressors. These systems are challenging due to their sensitivity to manufacturing variations, particularly in the gas-bearing geometry, which can lead to rotordynamic instability. Additionally, the interdependencies between the subsystems, such as axial and journal bearings, rotor, compressor impellers, and magnets, necessitate a multidisciplinary approach that spans aerodynamics, structural dynamics, rotordynamics, mechanics, loss analyses, and more. A clear tradeoff between system efficiency, mass-flow range, and robustness has been identified for the compressor design. Higher nominal compressor mass-flows, i.e., increased nominal power, is suggested to decrease the hypervolume of feasible manufacturing deviations. Hence, there is a sweet power spot for gas-bearing supported turbomachinery. Further, the framework's computational efficiency is on par with that of a university cluster, while only employing a desktop computer equipped with a consumer-grade graphics card. This work demonstrates a significant advancement in the design of complex engineering systems and sets a new standard for speed and efficiency in computational engineering design.

关键词： multidisciplinary design optimization robust design surrogate modeling artificial neural networks turbocompressor design micro-turbomachinery gas-bearings herringbone grooved journal bearings manufacturing deviations constrained multi-objective optimization robustness metrics computational efficiency artificial intelligence data-driven design design automation design for manufacturing design integration design of multiscale systems design optimization machine learning metamodeling

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constrained multi-objective trans-media maneuver trajectory optimization for morphing unmanned aerial-underwater vehicles

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OCEAN ENGINEERING 2024年 299卷

作者： Chen, Guoming Zhang, Wei Qi, Le Xue, Huihui Huang, Wei Hu, Junhua AF Engn Univ Aviat Maintenance NCO Sch Xinyang 464000 Henan Peoples R China AF Engn Univ Aeronaut Engn Coll Xian 710038 Shaanxi Peoples R China

In this study, the challenge of optimizing trans -media maneuver trajectories is addressed through the introduction of a constrained multi -objective Grey Wolf Optimizer (CMOGWO). The objective function has been refined to assess solutions more accurately based on their feasibility and adherence to constraints. Three distinct Archives have been employed for categorizing and optimizing dominant, non -dominant, and feasible nondominant solutions, each benefiting from specifically tailored optimization strategies. A hybrid search method, complemented by a watch -based strategy, is proposed for effectively guiding the population towards the Pareto front, ensuring the discovery of potential optimal solutions. Investigations have been conducted into the impact of initial altitude on trajectory optimization, with comparative simulations revealing the effectiveness of the proposed CMOGWO in managing unmanned aerial -underwater vehicle trajectories. The results demonstrate the potential of the CMOGWO in enhancing the reliability and feasibility of trajectory optimization in complex environmental conditions.

关键词： Unmanned aerial-underwater vehicles Trans-media maneuver constrained multi-objective optimization Hybrid search strategy Watch-based strategy

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Novel constrained multi-objective biogeography-based optimization algorithm for robot path planning

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Journal of Beijing Institute of Technology 2014年第1期23卷 96-101页

作者： XU Zhi-dan MO Hong-wei Automation College Harbin Engineering University Harbin 150001 China Institute of Basic Science Harbin University of Commerce Harbin 150028 China

A constrained multi-objective biogeography-based optimization algorithm （CMBOA） was proposed to solve robot path planning （RPP）. For RPP, the length and smoothness of path were taken as the optimization objectives, and the distance from the obstacles was constraint. In CMBOA, a new migration operator with disturbance factor was designed and applied to the feasible population to generate many more non-dominated feasible individuals; meanwhile, some infeasible individuals nearby feasible region were recombined with the nearest feasible ones to approach the feasibility. Compared with classical multi-objective evolutionary algorithms, the current study indicates that CM- BOA has better performance for RPP.

关键词： constrained multi-objective optimization biogeography-based optimization robot pathplanning

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