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Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point pareto frontier

Computers, Materials & Continua 2024年第4期79卷 1281-1299页

作者： JunWang Linxi Zhang Hao Zhang Funan Peng Mohammed A.El-Meligy Mohamed Sharaf Qiang Fu College of Computer Science and Technology Shenyang University of Chemical TechnologyShenyang110142China Key Laboratory of Intelligent Technology for Chemical Process Industry of Liaoning Province Shenyang University of Chemical TechnologyShenyang110142China Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia

The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and *** multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective *** seen,a variety of decision variable groupingalgorithms have been ***,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the pareto *** solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointpareto frontier(MOEA-DV/EPF)is *** algorithm adopts a preprocessing rule to solve the paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision *** the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the *** validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.

关键词： Multi-objective evolutionary optimization algorithm decision variables grouping extreme point pareto frontier

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On ε-Constraint Based Methods for the Generation of pareto frontiers

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Journal of Mechanics Engineering and Automation 2013年第5期3卷 279-289页

作者： Kenneth Chircop David Zammit-Mangion Department of Electronic Systems Engineering University of Malta Msida MSD2080 Malta

Over the years, a number of methods have been proposed for the generation of uniform and globally optimal pareto frontiers in multi-objective optimization problems. This has been the case irrespective of the problem definition. The most commonly applied methods are the normal constraint method and the normal boundary intersection method. The former suffers from the deficiency of an uneven pareto set distribution in the case of vertical （or horizontal） sections in the pareto frontier, whereas the latter suffers from a sparsely populated pareto frontier when the optimization problem is numerically demanding （ill-conditioned）. The method proposed in this paper, coupled with a simple pareto filter, addresses these two deficiencies to generate a uniform, globally optimal, well-populated pareto frontier for any feasible bi-objective optimization problem. A number of examples are provided to demonstrate the performance of the algorithm.

关键词： pareto frontier multiobjective optimization scalarization methods ε-constraint methods design optimization.

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Single-Machine Preemptive Scheduling with Release Dates Involving the Total Weighted Late Work Criterion

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Journal of the Operations Research Society of China 2023年第3期11卷 693-706页

作者： Zhi-Chao Geng Yuan Zhang School of Mathematics and Statistics Zhengzhou UniversityZhengzhouHenan450001China

This paper investigates the preemptive scheduling with release dates on a single machine to minimize the total weighted late *** firstly present an O(nlogn)-time algorithm for the single-agent problem with disagreeable weights and due *** then,we extendedly study the two-agent pareto-scheduling problem with jobs having a common due date to minimize each agent's total weighted late work,and give a polynomial-time algorithm that is based on the parameters analysis to generate the pareto frontier.

关键词： Preemptive scheduling Total weighted late work Two agent pareto frontier

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Optimal scheduling of islanded microgrid based on improved MOSSA

Optimal scheduling of islanded microgrid based on improved M...

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第43届中国控制会议

作者： Lai Huanhuan Pei Qingqi Liu Meiqin Dong Shanling Xie Zhiyun Zheng Jian Qu Hezuo Zhang Senlin Wenzhou Branch of State Grid College of Electrical Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Zhejiang University Jinhua Research Institute of Zhejiang University

Microgrid systems are widely used in various scenarios. However, there is great uncertainty in the output of wind and solar units. How to maximize both the economic and environmental benefits of microgrid is an important issue. To achieve this,considering consumption of renewable energy and total cost, a multi-objective optimization model for islanded microgrid is established. The classical optimization algorithm has insufficient ability to approach the optimal solution in each dimension when facing multi-objective optimization. Thus, this paper introduces the fuzzy clustering mechanism and the non-dominated sorting method to obtain the improved multi-objective sparrow search algorithm(MOSSA). Taking data of renewable energy units and loads from a certain microgrid as test case, the pareto frontier is searched by improved MOSSA. The experimental results show that the proposed method can effectively reduce the economic cost and improve the absorption capacity of wind power and photovoltaic energy.

关键词： microgrid multi-objective optimization pareto frontier improved MOSSA

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Solvability Region of AC Power Flow with Volatile Renewable Generation and Operating Constraints

Solvability Region of AC Power Flow with Volatile Renewable ...

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第40届中国控制会议

作者： Qianli Ma Wei Wei Wei Chai Shengwei Mei State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua University Electric Power Research Institute Shanghai Municipal Electric Power Company

Integration of large-scale renewable energy in the power system helps reduce carbon dioxide emission. However, their volatile nature also challenges the security operation of power systems, which boils down to the feasibility of network constrained power flow under a given nodal injection pattern. This paper studies the solvability of alternating-current（AC） power flow with volatile renewable generation and operating constraints. The set of all admissible injection patterns of renewable power is defined as the dispatchable region, which contains essential and visualizable information on system security. This paper characterizes the dispatchable region through several multi-objective optimization problems;the pareto frontiers constitute the boundary of the dispatchable region. A ε-constrained method is developed to compute and visualize the dispatchable region. Case studies on the IEEE 9 bus system validate the proposed method.

关键词： Dispatchable region renewable generation uncertainty multi-objective optimization pareto frontier

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