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检索条件"主题词=Constrained multi-objective optimization"

共 165 条记录，以下是131-140 订阅

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multi-population constrained multi-objective Evolutionary Algorithm Based on Knowledge Transfer 6

Multi-population Constrained Multi-objective Evolutionary Al...

引用

6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Zhao, Shulin Hao, Xingxing Chen, Li Feng, Yahui Northwest Univ Sch Informat Sci & Technol Xian Peoples R China

ISBN: (纸本)9798350377859;9798350377842

constrained multi-objective problems face the challenge of simultaneously optimizing the objective functions and constraint satisfaction. The difficulty in addressing this challenge lies in considering convergence, feasibility, and diversity simultaneously. To better solve CMOPs, this paper proposes a multi-population constrained multi-objective evolutionary algorithm based on knowledge transfer (C-MTEA). It consists of three different populations, i.e., the main population, the archive population, and the auxiliary population, that can cooperate with each other to evolve collectively. Specifically, the main population and the archive population cooperate by utilizing different search strategies to generate complementary offspring, while the auxiliary population, which does not consider constraints, can assist the main population in convergence. In the environmental selection stage, useful information is transferred across populations by sharing offspring generated by various strategies, thus facilitating the evolution of populations. To validate the effectiveness of the proposed C-MTEA, experiments are carried out on 5 popular benchmark suites containing up to 63 instances. The results demonstrate that the proposed algorithm is competitive with state-of-the-art constrained multi-objective optimization evolutionary algorithms (CMOEAs).

关键词： constrained multi-objective optimization Evolutionary algorithm Knowledge transfer multi-population evolutionary search

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Dynamic Path optimization of Emergency Transport Based on Hierarchical Genetic Algorithm 1

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1st International Conference on Swarm Intelligence

作者： Ma, Yongjie Tian, Ye Hou, Wenjing NW Normal Univ Coll Phys & Elect Engn Lanzhou 730070 Peoples R China

ISBN: (数字)9783642134951

ISBN: (纸本)9783642134944

An efficient search algorithm. which have to take into account he time and cost on emergency transport. the hierarchical Genetic Algorithm was proposed Local optimization is achieved by Bottom GA in Subnet. global optimization is achieved by top GA in the whole network The Contradiction of Global search capability and search efficiency is solved by maintaining a balance between GA Random search and this method is narrow search The results of calculation show that this method can satisfy the demands of practical engineering of dynamic path selection in a wide range and quick fix of failure path on Emergency transport

关键词： Genetic Algorithm Emergency Transport constrained multi-objective optimization Hierarchical

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Ant-Genetic Algorithms Based on multi-objective optimization

Ant-Genetic Algorithms Based on Multi-Objective Optimization

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Wei, Xianmin Weifang Univ Comp & Commun Engn Sch Weifang Peoples R China

ISBN: (纸本)9781457715846

In this paper, a new algorithm called multi-objective ant-genetic algorithms, which is based on the continuous space optimization is presented to solve constrained multi-objective function optimization problems. For the trait of multi-objective optimization, we define the pheromone instruction inheritance searching strategy and the method of pheromone updating. Then we combine four means of pheromone instruction inheritance searching, introduction of excellent decision-making, decision set updating and changing algorithm termination condition together so that the constringent speed of searching has improved a lot and the quantity of Pareto optimal decisions were controlled, also the distributing area of decisions were enlarged, the diversity of the swarm was maintained. At the same time, the termination conditions of multi-objective ant-genetic algorithms were presented. In the end, an example was listed to prove that the algorithms were effective, and it can find a group of widely distributed Pareto optimal decisions.

关键词： constrained multi-objective optimization ant-genetic algorithms pareto optimal decisions

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Computation of Fundamental Bounds for Antennas 16

Computation of Fundamental Bounds for Antennas

引用

16th European Conference on Antennas and Propagation (EuCAP)

作者： Liska, Jakub Jelinek, Lukas Capek, Miloslav Czech Tech Univ Dept Electromagnet Field Fac Elect Engn Prague Czech Republic

ISBN: (纸本)9788831299046

Fundamental bounds play an important role in antenna design. Using method of moments and electric field integral equation, this paper shows a formulation of fundamental bounds on antenna metrics based on optimal current density. This methodology is applied to two representative and challenging examples. The first example examines Yagi-Uda antenna and compares it with performance limits on Q-factor, radiation efficiency, and directivity. The second example shows how to determine fundamental bounds when a designer has far-field constraints. In their entirety, the examples demonstrate variability and generality of this treatment and also recall the existence of an open-source computational package, which can be used for evaluation of fundamental bounds on various metrics including their mutual trade-offs.

关键词： Fundamental bounds directivity Q-factor radiation efficiency radiation pattern constrained multi-objective optimization convex optimization

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Application of constrained multi-objective Evolutionary Algorithm in a Compressed-air Station Scheduling Problem 38

Application of Constrained Multi-objective Evolutionary Algo...

引用

38th Chinese Control Conference (CCC)

作者： Liu, Jianchang Yang, Yongkuan Tan, Shubin Wang, Honghai Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9789881563972

The compressor station is the utility that generates the compressed-air. This paper aims to present the formulation of the operation scheduling optimization problem for a compressor station, and apply a constrained multi-objective evolutionary algorithm, named CMOEA/D-CDP, to solve the problem. For this problem, it can be defined as one of the nonlinear 0-1 mixed integer multi-objective optimization problems due to the decision variables including continuous variables and 0-1 integer variables. In light of the relation between the unit commitment and rotational speed of a compressor station, a special encoding scheme which decreases the dimension of decision variables is proposed to change this nonlinear 0-1 mixed integer multi-objective optimization problem into a nonlinear continuous multi-objective optimization problem. The result obtained by CMOEA/D-CDP is a set of Pareto optimal solutions, which can help a decision maker select a specific preferred solution to guide the production under the different limits of objectives. Experimental results on two typical compressor stations indicate that the proposed encoding scheme shows apparent superiority compared to the normal encoding method.

关键词： Compressed-air station CMOEA/D-CDP constrained multi-objective optimization Encoding scheme Optimal scheduling of compressor station

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Solving multi-objective optimization Problem with Complex Constraints Based on Grouping of Constraints 15

Solving Multi-objective Optimization Problem with Complex Co...

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15th International Conference on Advanced Computational Intelligence (ICACI)

作者： Xu, Biao Zheng, Yiwu Li, Ke Li, Wenji Su, Yuanping Fan, Zhun Gong, Dunwei Shantou Univ Coll Engn Shantou 515063 Peoples R China Nanchang Inst Technol Sch Energy & Mech Engn Nanchang 330013 Jiangxi Peoples R China Qingdao Univ Sci & Technol Coll Informat Sci & Technol Qingdao 266100 Peoples R China

ISBN: (纸本)9798350321456

multi-objective optimization problems with complex constraints are common in practical production. To account for this, we present a complex constrained multi-objective evolutionary optimization algorithm framework based on constraint grouping., which divides the constraints into strong constraints and weak constraints, and then constructs multiple simple optimization sub-problems, each of which corresponds to a different evolutionary population. The subproblem population co-evolves with the original problem population, which helps the original problem to cross the infeasible region and find an effective feasible solution. To verify the effectiveness of the proposed algorithm, the proposed algorithm and four constrained multi-objective evolutionary algorithms are tested and compared on 14 test functions. The results indicate that the proposed algorithm has good performance and strong competitiveness.

关键词： constrained multi-objective optimization Constraints grouping multipopulations cooperative

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multi-objective Evolutionary Algorithm Based on Competitive Swarm Optimizer and Constraint Handling Techniques 20th

Multi-objective Evolutionary Algorithm Based on Competitive ...

引用

20th International Conference on Intelligent Computing (ICIC)

作者： Zhu, Deng Li, Jun Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Peoples R China

ISBN: (纸本)9789819755776;9789819755783

A significant challenge in solving constrained multi-objective optimization Problems (CMOPs) is balancing convergence, diversity, and feasibility. Imbalance among these factors can prevent constrained multi-objective Evolutionary Algorithms (CMOEA) from converging to the constrained Pareto Front (CPF). When dealing with problems involving complex constraints and large objective spaces, most algorithms encounter difficulties. This paper proposes a novel Competitive Swarm Optimizer (CSO) with faster convergence and stronger search capabilities. To fully utilize infeasible solutions, a two-stage Constraint Handling Technique (CHT) is introduced, which leverages well-performing infeasible solutions to help the population escape local feasibility and explore feasible regions. To promote solution diversity, weak coevolution and probabilistic coevolution methods are employed during population evolution. Additionally, continual updating of the dual-archive further enhances solution convergence and diversity. Out of 23 test suites, Proposed algorithm obtained 13 of the best HV and IGD values, far more than any other algorithm. Simulation results on the LIRCMOP and DASCMOP test suites demonstrate the superiority of the proposed algorithm over other popular algorithms.

关键词： constrained multi-objective optimization Constraint Handling Techniques Competitive Swarm Optimizer Evolutionary Algorithms Coevolution

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Comparison of Constraint Handling Approaches in multi-objective optimization 17th

Comparison of Constraint Handling Approaches in Multi-object...

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17th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Chhipa, Rohan Hemansu Helbig, Marde Univ Pretoria Dept Comp Sci Pretoria South Africa

ISBN: (纸本)9783319912530;9783319912523

When considering real-world optimization problems the possibility of encountering problems having constraints is quite high. Constraint handling approaches such as the penalty function and others have been researched and developed to incorporate an optimization problem's constraints into the optimization process. With regards to multi-objective optimization, in this paper the two main approaches of incorporating constraints are explored, namely: Penalty functions and dominance based selection operators. This paper aims to measure the effectiveness of these two approaches by comparing the empirical results produced by each approach. Each approach is tested using a set of ten benchmark problems, where each problem has certain constraints. The analysis of the results in this paper showed no overall statistical difference between the effectiveness of penalty functions and dominance based selection operators. However, significant statistical differences between the constraint handling approaches were found with regards to specific performance indicators.

关键词： constrained multi-objective optimization Pareto dominance Penalty functions

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A constraint handling technique for implementing multi-objective Evolutionary Neural Networks 15

A constraint handling technique for implementing Multi-Objec...

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15th International multi-Conference on Systems, Signals and Devices (SSD)

作者： El Hamdi, R. Njah, M. Digital Res Ctr Sfax Control & Energy Management Lab CEM Sfax Tunisia

ISBN: (纸本)9781538653050

Pareto-based multi-objective algorithms have been adopted for the design of artificial neural networks and they have attracted much interest, with promising results. Such approaches are usually implemented by means of error minimization while controlling the complexity of the model. This paper proposes a novel methodology for implementing Pareto-based multi-objective optimization within evolutionary neural networks. The originality of the proposed methodology lie in the use of a new constraint handling technique based on a self-adaptive penalty method in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Experimental results show that the proposed approach outperforms other existing evolutionary neural networks approaches reported recently in the literature using the widely accepted Wisconsin breast cancer diagnosis (WBCD) database. The developed approach was also used to simulate chemical unit process and the outcomes of the results are quite encouraging.

关键词： Artificial Neural Networks constrained multi-objective optimization Pareto Dominance Criterion Differential Evolution

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multi-stage Global and Local Cooperative constrained multi-objective Evolutionary Algorithm 6

Multi-stage Global and Local Cooperative Constrained Multi-o...

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6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Wang, Shunge Qiu, Yifeng Li, Wenji Wang, Zhaojun Xu, Biao Zhuang, Jiafan Hao, Zhifeng Fan, Zhun Shantou Univ Shantou Guangdong Peoples R China UESTC Shenzhen Inst Adv Study Shenzhen Peoples R China

ISBN: (纸本)9798350377859;9798350377842

When solving constrained multi-objective optimization problems (CMOPs), the primary challenge is to quickly search for the true Pareto fronts (PFs) while ensuring diversity. However, some constrained multi-objective evolutionary algorithms (CMOEAs) employ a single search strategy, making it difficult for their populations to maintain convergence, diversity, and feasibility simultaneously. To address this issue, we propose a novel multi-stage global and local collaborative CMOEA named MSGLC, which includes two different search stages. In the first stage, constraints are ignored, and a CMOP is decomposed into multiple single-objective sub-problems without considering any constraints, allowing the population to quickly search the boundaries of unconstrained PFs, thus improving convergence. In the second stage, through weak co-evolution of the main and auxiliary populations, the main population considers the original CMOP, while the auxiliary population considers the e-relaxed CMOP. They respectively adopt local and global differential evolution operators for offspring generation. This can help the populations effectively get across multiple large infeasible areas and quickly search for the constrained PF. Furthermore, we introduce a computational resource allocation strategy based on sub-regions, concentrating computational resources in potentially search areas, thereby enhancing the search efficiency of the proposed algorithm. Comparative experimental results demonstrate that the proposed MSGLC significantly outperforms five other state-of-the-art CMOEAs on the LIRCMOP benchmark problems.

关键词： constrained multi-objective optimization multi-stage search Global and local cooperation

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