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An adaptive disturbance multi-objective evolutionary algorithm based on decomposition

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2022年第4期41卷 306-315页

作者： Shi, Yanfang Shi, Jianguo Hebei Software Inst Baoding 071000 Hebei Peoples R China

In solving multi-objective optimisation problems, the uniformly distributed weight vector of decomposition based multi-objective evolutionary algorithm (MOEA/D) is not completely suitable for the non-uniformly distributed Pareto front (PF). In order to solve the situation above, this paper proposes an adaptive disturbance multi-objective evolutionary algorithm based on decomposition (AD-MOEA/D), which introduces the disturbance individuals and disturbance weight vectors during the evolution. The disturbance individuals maintain the population diversity and improve convergence accuracy. The disturbance weight vectors assist the weight vectors to adjust adaptively and improve the distribution of PF. Besides, both disturbance individuals and disturbance weight vectors are produced according to the actual evolution, which will not participate in evolution when it is not necessary. The experimental results on multi-objective test functions show that the PF optimised by AD-MOEA/D has better convergence and distribution.

关键词： multi-objective evolutionary algorithm disturbance individuals disturbance weight vectors decomposition

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Local Search Enhanced multi-objective evolutionary algorithm for Fuzzy Flexible Job Shop Scheduling

Local Search Enhanced Multi-Objective Evolutionary Algorithm...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Zhang, Xuwei Zhao, Ziyan Liu, Shixin College of Information Science and Engineering Northeastern University The State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

ISBN: (纸本)9781665430654

The uncertainty in actual manufacturing systems often manifests as uncertain processing times, especially in flexible manufacturing systems. This work proposes a Decomposition-based evolutionary algorithm with Local Search (DLSEA) to solve flexible scheduling with fuzzy processing times by minimizing makespan and total machine workload. Considering the different scales of objectives, two normalization methods are employed on subpopulations, respectively, aiming to mitigate the potential detrimental effects of a single normalization method. This work also introduces a local search method to enhance the performance of DLSEA. The proposed DLSEA is compared with four state-of-the-art algorithms on two series of cases. The experimental results show that DLSEA exhibits superior search capabilities. © 2023 IEEE.

关键词： Fuzzy flexible job shop scheduling Local search multi-objective evolutionary algorithm Normalization

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A dual dynamic constraint boundary based constrained multi-objective evolutionary algorithm for small feasible regions

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 275卷

作者： Zhu, Cong Yang, Yongkuan Kong, Xiangsong Yang, Yanxiang Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Xiamen Key Lab Frontier Elect Power Equipment & In Xiamen 361024 Peoples R China

Addressing constrained multi-objective optimization problems (CMOPs) with small feasible regions presents a significant challenge, as existing algorithms often struggle to balance feasibility, diversity, and convergence within the population. To overcome this challenge, we propose a dual dynamic constraint boundary-based constrained multi-objective evolutionary algorithm, referred to as TPDCB. In TPDCB, the original CMOP is transformed into two dynamic CMOPs using a dual dynamic constraint boundary strategy to better identify feasible solutions. Specifically, for the two dynamic CMOPs within the constraint relaxation boundary, the first dynamic CMOP primarily focuses on multi-objective optimization, while the second dynamic CMOP equally emphasizes both multi-objective optimization and constraint satisfaction to enhance individual diversity. Furthermore, an auxiliary problem without constraints is introduced by treating constraint violations as an additional optimization objective, which improves the algorithm's global convergence. Finally, a tri-population co-evolution framework is proposed to simultaneously tackle all three constructed problems. The algorithm's performance is evaluated on 22 benchmark problems and three real-world applications, and compared to seven state-of-the-art algorithms. Experimental results demonstrate that TPDCB is competitive in solving CMOPs with small feasible regions.

关键词： Constrained multi-objective optimization multi-objective evolutionary algorithm Small feasible region Dynamic constraint boundary Constraint handling Co-evolution

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A multi-objective evolutionary algorithm for robust positive-unlabeled learning

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INFORMATION SCIENCES 2024年 678卷

作者： Qiu, Jianfeng Tang, Qi Tan, Ming Li, Kaixuan Xie, Juan Cai, Xiaoqiang Cheng, Fan Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Anhui Jianzhu Univ Sch Math & Phys Hefei 230601 Peoples R China Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230601 Peoples R China

Positive and unlabeled (PU) learning is to learn a binary classifier with good generalization ability from PU data. A variety of PU learning algorithms with promising performance have been proposed. However, most of them assume that PU samples are "clean", which is not true in real applications due to the existing noisy or redundant samples. Thus, how to obtain a robust PU classifier with better performance is a challenging problem. To this end, we propose a novel multi -objective evolutionary algorithm to tackle it, named BPUSS-MOEA. Specifically, we firstly transform the robust PU learning into a bi-objective PU sample selection (BPUSS) problem, in which two objectives are designed. One is the number of selected "clean" PU samples and the other is the PU accuracy. Then, a dual -coding scheme is designed to represent the selected "clean" PU samples and the labels of U samples. With the dual -coding scheme, a novel offspring generation strategy is developed to achieve the offsprings with high quality. To further improve the performance of BPUSS-MOEA, an effective population initialization strategy is designed. Experiments on 10 datasets with different noise levels show that compared with the state -ofthe -arts, the proposed algorithm demonstrates its robustness in terms of the PU accuracy.

关键词： Robust PU learning multi-objective evolutionary algorithm PU sample selection Dual-coding scheme

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An Effective multi-objective evolutionary algorithm Based on Decomposition for A Robot Rescue Path Planning Problem 41

An Effective Multi-objective Evolutionary Algorithm Based on...

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第41届中国控制会议

作者： Loulei Dai Hongguo Wang Quan-ke Pan School of Mechanical and Electrical Engineering and Automation Shanghai University Liaocheng Vocational & Technical College

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The paper addresses a post-disaster robot rescue path planning problem where a number of people are trapped and a robot is used to rescue *** objective functions are to minimize the number of deaths and the number of serious *** first,a path matrix between two trapped people is obtained by using an improved A *** then,an improved multiobjective evolutionary algorithm is proposed to find rescue paths with Pareto optimal or near-optimal *** last,the proposed algorithm is compared with other multi-objective *** experimental results demonstrate the proposed algorithm can effectively plan a shorter path to reduce the number of deaths and serious injuries after disasters.

关键词： Robot Rescue Path planning A~* algorithm multi-objective evolutionary algorithm

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An effective multi-objective evolutionary algorithm for multiple spraying robots task assignment problem

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SWARM AND evolutionary COMPUTATION 2024年 87卷

作者： Dong, Jin-Shuai Pan, Quan-Ke Miao, Zhong-Hua Sang, Hong-Yan Gao, Liang Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

This paper addresses a multiple agricultural spraying robots task assignment problem in the greenhouse environment. The objective of the problem is to obtain a set of Pareto solutions that simultaneously optimize the total travel distance and maximum completion time of all robots. To solve this problem, an effective multi-objective evolutionary algorithm is proposed. In the proposed algorithm, an initial population with high quality and diversity is generated by a heuristic allocation strategy based on robot capacity constraints. During the evolutionary phase, a crossover strategy based on information in the non-dominated solution set is designed for exploration in the global scope. A multi-objective local search with an iterated greedy idea is introduced to improve the exploration ability of the algorithm. Meanwhile, a restart operator based on the ideal point is presented to jump out of the local optimum. Finally, extensive experiments based on different scales are conducted. The results show that the proposed algorithm significantly outperforms several state-of-the-art multiobjective algorithms in the literature.

关键词： Agricultural spraying robots Task assignment multi-objective evolutionary algorithm Heuristic

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A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

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Environment Systems and Decisions 2022年第2期42卷 177-188页

作者： Antonello, Federico Baraldi, Piero Zio, Enrico Serio, Luigi Energy Department Politecnico di Milano Via Lambruschini 4 Milan 20156 Italy MINES ParisTech PSL Research University CRC Sophia Antipolis 75006 France Technology Department CERN Geneva 23 1211 Switzerland

In this work, a multi-objective evolutionary algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms;this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies. © 2022, The Author(s).

关键词： Abnormal behaviours Alarm data Complex technical infrastructures Functional dependencies Genetic algorithm multi-objective evolutionary algorithm Novelty search Particle accelerator

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A multi-objective evolutionary algorithm Based on Cone Heart Point Adjustment Vectors to Solve Concave-convex Problems 34

A Multi-objective Evolutionary Algorithm Based on Cone Heart...

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34th Chinese Control and Decision Conference (CCDC)

作者： Li, Xin Li, Xiaoli Wang, Kang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Minist Educ Beijing Key Lab Computat Intelligence & Intellige Engn Res Ctr Digital Commun Beijing 100124 Peoples R China

ISBN: (纸本)9781665478960

Decomposition-based multi-objective optimization algorithms have been shown to be successful in solving multi-objective optimization problems (MOPs). Since the shape is unknown before the search, the classical decomposition method relies on a set of uniformly distributed reference vectors to divide the objective space into multiple subregions. However, the the distribution of reference vectors produced by the Das and Dennis systematic approach is difficult to maintain the diversity of (extremely) concave and (extremely) convex problems. To remedy this issue, a multi-objective evolutionary algorithm based on cone heart point adjustment vectors is proposed to solve concave and convex problems (CHP-MOEA). In this paper, the concavity or convexity is first according to the intermediate objective vector. Then, the distribution of reference vectors are adjusted according to the cone heart point on the basis of the traditional vectors. Finally, experimental results on 31 benchmark problems show that the proposed algorithm exhibits better performance than other state-of-the-art algorithms.

关键词： Cone heart point Concavity or convexity multi-objective evolutionary algorithm

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Optimal placement of fixed hub height wind turbines in a wind farm using twin archive guided decomposition based multi-objective evolutionary algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 130卷

作者： Raju, Sri Srinivasa M. Mohapatra, Prabhujit Dutta, Saykat Mallipeddi, Rammohan Das, Kedar Nath Natl Inst Technol Dept Math Silchar India Vellore Inst Technol Sch Adv Sci Dept Math Vellore Tamil Nadu India Acad Sinica Inst Stat Sci Taipei Taiwan Kyungpook Natl Univ Sch Elect Engn Dept Artificial Intelligence Daegu 41566 South Korea

Harnessing maximum wind energy's power output and efficiency is vital to combat environmental challenges tied to conventional fossil fuels. Wind power's cost-effectiveness and emission reduction potential underscore its significance. Efficient wind farm layout plays a pivotal role, both technically and commercially. evolutionary algorithms show their potential while solving multi-objective wind farm layout optimization problems. However, due to the large-scale nature of the problems, existing algorithms are getting trapped into local optima and fail to explore the search space. To address this, the TAG-DMOEA algorithm is upgraded with an adaptive offspring strategy (AOG) for better exploration. The proposed algorithm is employed on a wind farm layout problem with real-time data of wind speed and direction from two different locations. Unlike mixed hub heights, fixed hub heights such as 60, 67, and 78 m are adopted to conduct the case studies at two potential locations with real-time statistical data for the investigation of improved results. The results obtained by TAG-DMOEA-AOG on six cases are compared with 10 state-of-the-art algorithms. Statistical tests such as Friedman test and Wilcoxon signed rank test along with post hoc analysis (Nemenyi test) confirmed the superiority of the TAG-DMOEA-AOG on all cases of the considered multi-objective wind farm layout optimization problem.

关键词： Wind turbine multi-objective evolutionary algorithm Optimization Decomposition Weight vector

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Optimization of distributed energy resources planning and battery energy storage management via large-scale multi-objective evolutionary algorithm

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ENERGY 2024年 311卷

作者： Ali, Aamir Bughio, Ateeq-u-Rehman Abbas, Ghulam Keerio, M. U. Mugheri, N. H. Memon, Shaina Saand, A. S. Quaid e Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah 67450 Sindh Pakistan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

This paper investigates the synergistic integration of renewable energy sources and battery energy storage systems to enhance the sustainability, reliability, and flexibility of modern power systems. Addressing a critical gap in distribution networks, particularly regarding the variability of renewable energy, the study aims to minimize energy costs, emission rates, and reliability indices by optimizing the placement and sizing of wind and solar photovoltaic generators alongside battery energy storage systems. An improved large-scale multi-objective evolutionary algorithm with a bi-directional sampling strategy is *** scenarios are considered. In the first scenario, six study cases are analyzed to determine the optimal number, location, and size of distributed generators at peak load demand. The proposed algorithm outperforms existing state-of-the-art methods for smallscale distributed resource allocation. In the second scenario, a multi-period load demand across various seasons is evaluated, introducing new opportunities for battery energy storage systems. The problem is modeled with intertemporal constraints, creating a large-scale optimization challenge. Results demonstrate significant improvements: cost savings of up to 51.67 %, power reductions of up to 76.78 %, and a 99.9 % improvement in voltage deviation. Emission rates decreased by 99.92 % in case 1, 95.19 % in case 2, and 97.38 % in case 3, compared to the base case. The proposed algorithm shows superior convergence and performance in solving both small- and large-scale optimization problems, outperforming recent multi-objective evolutionary *** study provides a robust framework for optimizing renewable energy integration and battery energy storage, offering a scalable solution to modern power system challenges.

关键词： Distribution network Distributed generation Battery energy storage system multi-objective evolutionary algorithm

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