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An Immune-Inspired Approach with Interval Allocation in Solving Multimodal Multi-Objective Optimization Problems with Local Pareto Sets
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作者 Weiwei Zhang Jiaqiang Li +2 位作者 Chao Wang Meng Li Zhi Rao 《Computers, Materials & Continua》 SCIE EI 2024年第6期4237-4257,共21页
In practical engineering,multi-objective optimization often encounters situations where multiple Pareto sets(PS)in the decision space correspond to the same Pareto front(PF)in the objective space,known as Multi-Modal ... In practical engineering,multi-objective optimization often encounters situations where multiple Pareto sets(PS)in the decision space correspond to the same Pareto front(PF)in the objective space,known as Multi-Modal Multi-Objective Optimization Problems(MMOP).Locating multiple equivalent global PSs poses a significant challenge in real-world applications,especially considering the existence of local PSs.Effectively identifying and locating both global and local PSs is a major challenge.To tackle this issue,we introduce an immune-inspired reproduction strategy designed to produce more offspring in less crowded,promising regions and regulate the number of offspring in areas that have been thoroughly explored.This approach achieves a balanced trade-off between exploration and exploitation.Furthermore,we present an interval allocation strategy that adaptively assigns fitness levels to each antibody.This strategy ensures a broader survival margin for solutions in their initial stages and progressively amplifies the differences in individual fitness values as the population matures,thus fostering better population convergence.Additionally,we incorporate a multi-population mechanism that precisely manages each subpopulation through the interval allocation strategy,ensuring the preservation of both global and local PSs.Experimental results on 21 test problems,encompassing both global and local PSs,are compared with eight state-of-the-art multimodal multi-objective optimization algorithms.The results demonstrate the effectiveness of our proposed algorithm in simultaneously identifying global Pareto sets and locally high-quality PSs. 展开更多
关键词 multimodal multi-objective optimization problem local PSs immune-inspired reproduction
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Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems
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作者 Sheng Qi Rui Wang +3 位作者 Tao Zhang Weixiong Huang Fan Yu Ling Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第8期1786-1801,共16页
Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to tr... Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges. 展开更多
关键词 Evolutionary algorithms pattern mining sparse large-scale multi-objective problems(SLMOPs) sparse large-scale optimization.
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Coevolutionary Framework for Generalized Multimodal Multi-Objective Optimization 被引量:3
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作者 Wenhua Li Xingyi Yao +3 位作者 Kaiwen Li Rui Wang Tao Zhang Ling Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第7期1544-1556,共13页
Most multimodal multi-objective evolutionary algorithms(MMEAs)aim to find all global Pareto optimal sets(PSs)for a multimodal multi-objective optimization problem(MMOP).However,in real-world problems,decision makers(D... Most multimodal multi-objective evolutionary algorithms(MMEAs)aim to find all global Pareto optimal sets(PSs)for a multimodal multi-objective optimization problem(MMOP).However,in real-world problems,decision makers(DMs)may be also interested in local PSs.Also,searching for both global and local PSs is more general in view of dealing with MMOPs,which can be seen as generalized MMOPs.Moreover,most state-of-theart MMEAs exhibit poor convergence on high-dimension MMOPs and are unable to deal with constrained MMOPs.To address the above issues,we present a novel multimodal multiobjective coevolutionary algorithm(Co MMEA)to better produce both global and local PSs,and simultaneously,to improve the convergence performance in dealing with high-dimension MMOPs.Specifically,the Co MMEA introduces two archives to the search process,and coevolves them simultaneously through effective knowledge transfer.The convergence archive assists the Co MMEA to quickly approach the Pareto optimal front.The knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the-dominance-based method to obtain global and local PSs effectively.Experimental results show that Co MMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs. 展开更多
关键词 Coevolution ∈-dominance generalized multimodal multi-objective optimization(MMO) local convergence two archives
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Even Search in a Promising Region for Constrained Multi-Objective Optimization
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作者 Fei Ming Wenyin Gong Yaochu Jin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第2期474-486,共13页
In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However,... In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties.First, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs. 展开更多
关键词 Constrained multi-objective optimization even search evolutionary algorithms promising region real-world problems
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Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer
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作者 Hongliang Zhang Yi Chen +1 位作者 Yuteng Zhang Gongjie Xu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第8期1459-1483,共25页
The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worke... The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality. 展开更多
关键词 Distributed flexible job shop scheduling problem dual resource constraints energy-saving scheduling multi-objective grey wolf optimizer Q-LEARNING
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Immune Optimization Approach for Dynamic Constrained Multi-Objective Multimodal Optimization Problems 被引量:1
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作者 Zhuhong Zhang Min Liao Lei Wang 《American Journal of Operations Research》 2012年第2期193-202,共10页
This work investigates one immune optimization approach for dynamic constrained multi-objective multimodal optimization in terms of biological immune inspirations and the concept of constraint dominance. Such approach... This work investigates one immune optimization approach for dynamic constrained multi-objective multimodal optimization in terms of biological immune inspirations and the concept of constraint dominance. Such approach includes mainly three functional modules, environmental detection, population initialization and immune evolution. The first, inspired by the function of immune surveillance, is designed to detect the change of such kind of problem and to decide the type of a new environment;the second generates an initial population for the current environment, relying upon the result of detection;the last evolves two sub-populations along multiple directions and searches those excellent and diverse candidates. Experimental results show that the proposed approach can adaptively track the environmental change and effectively find the global Pareto-optimal front in each environment. 展开更多
关键词 DYNAMIC CONSTRAINED multi-objective optimization multimodalITY Artificial IMMUNE Systems IMMUNE optimization Environmental Detection
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Zoning Search With Adaptive Resource Allocating Method for Balanced and Imbalanced Multimodal Multi-Objective Optimization 被引量:5
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作者 Qinqin Fan Okan K.Ersoy 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第6期1163-1176,共14页
Maintaining population diversity is an important task in the multimodal multi-objective optimization.Although the zoning search(ZS)can improve the diversity in the decision space,assigning the same computational costs... Maintaining population diversity is an important task in the multimodal multi-objective optimization.Although the zoning search(ZS)can improve the diversity in the decision space,assigning the same computational costs to each search subspace may be wasteful when computational resources are limited,especially on imbalanced problems.To alleviate the above-mentioned issue,a zoning search with adaptive resource allocating(ZS-ARA)method is proposed in the current study.In the proposed ZS-ARA,the entire search space is divided into many subspaces to preserve the diversity in the decision space and to reduce the problem complexity.Moreover,the computational resources can be automatically allocated among all the subspaces.The ZS-ARA is compared with seven algorithms on two different types of multimodal multi-objective problems(MMOPs),namely,balanced and imbalanced MMOPs.The results indicate that,similarly to the ZS,the ZS-ARA achieves high performance with the balanced MMOPs.Also,it can greatly assist a“regular”algorithm in improving its performance on the imbalanced MMOPs,and is capable of allocating the limited computational resources dynamically. 展开更多
关键词 Computational resource allocation decision space decomposition evolutionary computation multimodal multi-objective optimization
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A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts 被引量:24
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作者 Yicun Hua Qiqi Liu +1 位作者 Kuangrong Hao Yaochu Jin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第2期303-318,I0001-I0004,共20页
Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remed... Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remedy this issue,a large body of research has been performed in recent years and many new algorithms have been proposed.This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts.We start with a brief introduction to the basic concepts,followed by a summary of the benchmark test problems with irregular problems,an analysis of the causes of the irregularity,and real-world optimization problems with irregular Pareto fronts.Then,a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses.Finally,open challenges are pointed out and a few promising future directions are suggested. 展开更多
关键词 Evolutionary algorithm machine learning multi-objective optimization problems(MOPs) irregular Pareto fronts
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CHARACTERIZATION OF EFFICIENT SOLUTIONS FOR MULTI-OBJECTIVE OPTIMIZATION PROBLEMS INVOLVING SEMI-STRONG AND GENERALIZED SEMI-STRONG E-CONVEXITY 被引量:5
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作者 E.A.Youness Tarek Emam 《Acta Mathematica Scientia》 SCIE CSCD 2008年第1期7-16,共10页
The authors of this article are interested in characterization of efficient solutions for special classes of problems. These classes consider semi-strong E-convexity of involved functions. Sufficient and necessary con... The authors of this article are interested in characterization of efficient solutions for special classes of problems. These classes consider semi-strong E-convexity of involved functions. Sufficient and necessary conditions for a feasible solution to be an efficient or properly efficient solution are obtained. 展开更多
关键词 multi-objective optimization problems semi-strong E-convex efficient solutions properly efficient solutions
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A multi-objective optimization framework for ill-posed inverse problems
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作者 Maoguo Gong Hao Li Xiangming Jiang 《CAAI Transactions on Intelligence Technology》 2016年第3期225-240,共16页
Many image inverse problems are ill-posed for no unique solutions. Most of them have incommensurable or mixed-type objectives. In this study, a multi-objective optimization framework is introduced to model such ill-po... Many image inverse problems are ill-posed for no unique solutions. Most of them have incommensurable or mixed-type objectives. In this study, a multi-objective optimization framework is introduced to model such ill-posed inverse problems. The conflicting objectives are designed according to the properties of ill-posedness and certain techniques. Multi-objective evolutionary algorithms have capability to optimize multiple objectives simultaneously and obtain a set of trade-off solutions. For that reason, we use multi-objective evolutionary algorithms to keep the trade-off between these objectives for image ill-posed problems. Two case studies of sparse reconstruction and change detection are imple- mented. In the case study of sparse reconstruction, the measurement error term and the sparsity term are optimized by multi-objective evolutionary algorithms, which aims at balancing the trade-off between enforcing sparsity and reducing measurement error. In the case study of image change detection, two conflicting objectives are constructed to keep the trade-off between robustness to noise and preserving the image details. Experimental results of the two case studies confirm the multi-objective optimization framework for ill-posed inverse problems in image processing is effective. 展开更多
关键词 Ill-posed problem Image processing multi-objective optimization Evolutionary algorithm
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Exploring a Promising Region and Enhancing Decision Space Diversity for Multimodal Multi-Objective Optimization
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作者 Fei Ming Wenyin Gong 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第2期325-342,共18页
During the past decade,research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems(MMOPs)in the multi-objective optimization community.Recentl... During the past decade,research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems(MMOPs)in the multi-objective optimization community.Recently,researchers have begun to investigate enhancing the decision space diversity and preserving valuable dominated solutions to overcome the shortage caused by a preference for objective space convergence.However,many existing methods still have limitations,such as giving unduly high priorities to convergence and insufficient ability to enhance decision space diversity.To overcome these shortcomings,this article aims to explore a promising region(PR)and enhance the decision space diversity for handling MMOPs.Unlike traditional methods,we propose the use of non-dominated solutions to determine a limited region in the PR in the decision space,where the Pareto sets(PSs)are included,and explore this region to assist in solving MMOPs.Furthermore,we develop a novel neighbor distance measure that is more suitable for the complex geometry of PSs in the decision space than the crowding distance.Based on the above methods,we propose a novel dual-population-based coevolutionary algorithm.Experimental studies on three benchmark test suites demonstrates that our proposed methods can achieve promising performance and versatility on different MMOPs.The effectiveness of the proposed neighbor distance has also been justified through comparisons with crowding distance methods. 展开更多
关键词 multimodal multi-objective optimization evolutionary algorithms promising region neighbor distance decision space coevolution
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Solving material distribution routing problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm 被引量:7
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作者 高贵兵 张国军 +2 位作者 黄刚 朱海平 顾佩华 《Journal of Central South University》 SCIE EI CAS 2012年第2期433-442,共10页
The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency... The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II. 展开更多
关键词 material distribution routing problem multi-objective optimization evolutionary algorithm local search
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Multi-population and diffusion UMDA for dynamic multimodal problems 被引量:3
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作者 Yan Wu Yuping Wang +1 位作者 Xiaoxiong Liu Jimin Ye 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第5期777-783,共7页
In dynamic environments,it is important to track changing optimal solutions over time.Univariate marginal distribution algorithm(UMDA) which is a class algorithm of estimation of distribution algorithms attracts mor... In dynamic environments,it is important to track changing optimal solutions over time.Univariate marginal distribution algorithm(UMDA) which is a class algorithm of estimation of distribution algorithms attracts more and more attention in recent years.In this paper a new multi-population and diffusion UMDA(MDUMDA) is proposed for dynamic multimodal problems.The multi-population approach is used to locate multiple local optima which are useful to find the global optimal solution quickly to dynamic multimodal problems.The diffusion model is used to increase the diversity in a guided fashion,which makes the neighbor individuals of previous optimal solutions move gradually from the previous optimal solutions and enlarge the search space.This approach uses both the information of current population and the part history information of the optimal solutions.Finally experimental studies on the moving peaks benchmark are carried out to evaluate the proposed algorithm and compare the performance of MDUMDA and multi-population quantum swarm optimization(MQSO) from the literature.The experimental results show that the MDUMDA is effective for the function with moving optimum and can adapt to the dynamic environments rapidly. 展开更多
关键词 univariate marginal distribution algorithm(UMDA) dynamic multimodal problems dynamic optimization multipopulation scheme.
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A Multi-Objective Optimal Evolutionary Algorithm Based on Tree-Ranking 被引量:1
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作者 Shi Chuan, Kang Li-shan, Li Yan, Yan Zhen-yuState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei,China 《Wuhan University Journal of Natural Sciences》 CAS 2003年第S1期207-211,共5页
Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has so... Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcoming s, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare-to front, retain the diversity of the population, and use less time. 展开更多
关键词 multi-objective optimal problem multi-objective optimal evolutionary algorithm Pareto dominance tree structure dynamic space-compressed mutative operator
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Time variant multi-objective linear fractional interval-valued transportation problem 被引量:1
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作者 Dharmadas Mardanya Sankar Kumar Roy 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2022年第1期111-130,共20页
This paper studies a time-variant multi-objective linear fractional transportation problem. In reality, transported goods should reach in destinations within a specific time. Considering the importance of time, a time... This paper studies a time-variant multi-objective linear fractional transportation problem. In reality, transported goods should reach in destinations within a specific time. Considering the importance of time, a time-variant multi-objective linear fractional transportation problem is formulated here. We take into account the parameters as cost, supply and demand are interval valued that involved in the proposed model, so we treat the model as a multi-objective linear fractional interval transportation problem. To solve the formulated model, we first convert it into a deterministic form using a new transformation technique and then apply fuzzy programming to solve it. The applicability of our proposed method is shown by considering two numerical examples. At last, conclusions and future research directions regarding our study is included. 展开更多
关键词 fractional transportation problem multi-objective optimization interval number time variant parameter fuzzy programming Pareto optimal solution
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Novel electromagnetism-like mechanism method for multiobjective optimization problems 被引量:1
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作者 Lixia Han Shujuan Jiang Shaojiang Lan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第1期182-189,共8页
As a new-style stochastic algorithm, the electromagnetism-like mechanism(EM) method gains more and more attention from many researchers in recent years. A novel model based on EM(NMEM) for multiobjective optimizat... As a new-style stochastic algorithm, the electromagnetism-like mechanism(EM) method gains more and more attention from many researchers in recent years. A novel model based on EM(NMEM) for multiobjective optimization problems is proposed, which regards the charge of all particles as the constraints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the proposed NMEM algorithm is a very robust method for solving the multiobjective optimization problems. 展开更多
关键词 electromagnetism-like mechanism(EM) method multi-objective optimization problem PARTICLE Pareto optimal solutions
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A new evolutionary algorithm for constrained optimization problems
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作者 王东华 刘占生 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2011年第2期8-12,共5页
To solve single-objective constrained optimization problems,a new population-based evolutionary algorithm with elite strategy(PEAES) is proposed with the concept of single and multi-objective optimization.Constrained ... To solve single-objective constrained optimization problems,a new population-based evolutionary algorithm with elite strategy(PEAES) is proposed with the concept of single and multi-objective optimization.Constrained functions are combined to be an objective function.During the evolutionary process,the current optimal solution is found and treated as the reference point to divide the population into three sub-populations:one feasible and two infeasible ones.Different evolutionary operations of single or multi-objective optimization are respectively performed in each sub-population with elite strategy.Thirteen famous benchmark functions are selected to evaluate the performance of PEAES in comparison of other three optimization methods.The results show the proposed method is valid in efficiency,precision and probability for solving single-objective constrained optimization problems. 展开更多
关键词 constrained optimization problems evolutionary algorithm POPULATION-BASED elite strategy single and multi-objective optimization
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A Parallel Search System for Dynamic Multi-Objective Traveling Salesman Problem
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作者 Weiqi Li 《Journal of Mathematics and System Science》 2014年第5期295-314,共20页
This paper introduces a parallel search system for dynamic multi-objective traveling salesman problem. We design a multi-objective TSP in a stochastic dynamic environment. This dynamic setting of the problem is very u... This paper introduces a parallel search system for dynamic multi-objective traveling salesman problem. We design a multi-objective TSP in a stochastic dynamic environment. This dynamic setting of the problem is very useful for routing in ad-hoc networks. The proposed search system first uses parallel processors to identify the extreme solutions of the search space for each ofk objectives individually at the same time. These solutions are merged into the so-called hit-frequency matrix E. The solutions in E are then searched by parallel processors and evaluated for dominance relationship. The search system is implemented in two different ways master-worker architecture and pipeline architecture. 展开更多
关键词 dynamic multi-objective optimization traveling salesman problem parallel search algorithm solution attractor.
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A Multi-Objective Scheduling and Routing Problem for Home Health Care Services via Brain Storm Optimization 被引量:4
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作者 Xiaomeng Ma Yaping Fu +2 位作者 Kaizhou Gao Lihua Zhu Ali Sadollah 《Complex System Modeling and Simulation》 2023年第1期32-46,共15页
At present,home health care(HHC)has been accepted as an effective method for handling the healthcare problems of the elderly.The HHC scheduling and routing problem(HHCSRP)attracts wide concentration from academia and ... At present,home health care(HHC)has been accepted as an effective method for handling the healthcare problems of the elderly.The HHC scheduling and routing problem(HHCSRP)attracts wide concentration from academia and industrial communities.This work proposes an HHCSRP considering several care centers,where a group of customers(i.e.,patients and the elderly)require being assigned to care centers.Then,various kinds of services are provided by caregivers for customers in different regions.By considering the skill matching,customers’appointment time,and caregivers’workload balancing,this article formulates an optimization model with multiple objectives to achieve minimal service cost and minimal delay cost.To handle it,we then introduce a brain storm optimization method with particular multi-objective search mechanisms(MOBSO)via combining with the features of the investigated HHCSRP.Moreover,we perform experiments to test the effectiveness of the designed method.Via comparing the MOBSO with two excellent optimizers,the results confirm that the developed method has significant superiority in addressing the considered HHCSRP. 展开更多
关键词 home health care multi-center service multi-objective optimization scheduling and routing problems brain storm optimization
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Non-dominated Sorting Advanced Butterfly Optimization Algorithm for Multi-objective Problems
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作者 Sushmita Sharma Nima Khodadadi +2 位作者 Apu Kumar Saha Farhad Soleimanian Gharehchopogh Seyedali Mirjalili 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第2期819-843,共25页
This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems.There is also an improvement to the original version of B... This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems.There is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective version.Due to better coverage and a well-distributed Pareto front,non-dominant rankings are applied to the modified BOA using the crowding distance strategy.Seven benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA(MONSBOA),including unconstrained,constrained,and real-world design multiple-objective,highly nonlinear constraint problems.Various performance metrics,such as Generational Distance(GD),Inverted Generational Distance(IGD),Maximum Spread(MS),and Spacing(S),have been used for performance comparison.It is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80%occasions in solving problems with a variety of linear,nonlinear,continuous,and discrete characteristics based on the Pareto front when compared quantitatively.From all the analysis,it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence. 展开更多
关键词 multi-objective problems Butterfly optimization algorithm Non-dominated sorting Crowding distance
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