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A bi-subpopulation coevolutionary immune algorithm for multi-objective combinatorial optimization in multi-UAV task allocation

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COMPLEX & INTELLIGENT SYSTEMS 2025年第2期11卷 1-19页

作者： Chen, Xi Wan, Yu Qi, Jingtao Zhao, Zipeng Ruan, Yirun Tang, Jun Natl Univ Def Technol Lab big data & decis Changsha 410073 Peoples R China

With the development of Unmanned Aerial Vehicle (UAV) technology towards multi-UAV and UAV swarm, multi-UAV cooperative task allocation has more and more influence on the success or failure of UAV missions. From the operational research point of view, such problems belong to high-dimensional combinatorial optimization problems, which makes the solving process face many challenges. One is that the discrete and high-dimensional decision variables make the quality of the solution obtained with acceptable time not guaranteed. Second, the desired solution of real missions often needs to satisfy multiple objective functions, or a set of solutions for decision-making. Therefore, this paper constructs a multi-objective combinatorial optimization in multi-UAV Task Allocation Problem (MCOTAP) model, and proposes a Bi-subpopulation Coevolutionary Immune Algorithm (BCIA). The two coevolutionary mechanisms improve the lower limit of population diversity, and the evolutionary strategy pool integrating multiple strategies and the adaptive strategy selection mechanism enhance the local search ability in the late evolution. In the experiments, BCIA competes fairly with the mainstream multi-objective evolutionary algorithms (MOEAs), multi-objective immune algorithms (MOIAs) and the recently proposed multi-UAV mission planning algorithms. The experimental results on different test problems (including several multi-objective combinatorial optimization benchmark problems and the proposed MCOTAP model) show that BCIA has superior performance in solving multi-objective combinatorial optimization problems (MCOPs). At the same time, the effectiveness of each design component of BCIA has been comprehensively verified in the ablation study.

关键词： Coevolutionary algorithm multi-objective combinatorial optimization multi-objective immune algorithm multi-UAV Task allocation problem

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multi-objective combinatorial optimization analysis of the recycling of retired new energy electric vehicle power batteries in a sustainable dynamic reverse logistics network

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第16期30卷 47580-47601页

作者： Mu, Nengye Wang, Yuanshun Chen, Zhen-Song Xin, Peiyuan Deveci, Muhammet Pedrycz, Witold Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Sichuan Peoples R China Xinjiang Univ Coll Transportat Engn Urumqi 830046 Xinjiang Peoples R China Natl & Local Joint Engn Lab Intelligent Integrated Chengdu 611756 Sichuan Peoples R China Natl Engn Lab Integrated Transportat Big Data Appl Chengdu 611756 Sichuan Peoples R China Wuhan Univ Sch Civil Engn Wuhan 430072 Hubei Peoples R China UCL Bartlett Sch Sustainable Construct London WC1E 6BT England Natl Def Univ Turkish Naval Acad Dept Ind Engn TR-34940 Istanbul Turkiye Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Istinye Univ Fac Engn & Nat Sci Dept Comp Engn Istanbul Turkiye

The recycling of retired new energy vehicle power batteries produces economic benefits and promotes the sustainable development of environment and society. However, few attentions have been paid to the design and optimization of sustainable reverse logistics network for the recycling of retired power batteries. To this end, we develop a six-level sustainable dynamic reverse logistics network model from the perspectives of economy, environment, and society. We solve the multi-objective combinatorial optimization model to explore the layout of the sustainable reverse logistics network for retired new energy vehicle power batteries recycling. A case study is implemented to verify the effectiveness of the proposed model. The results show that (a) the facility nodes near the front of the network fluctuate more by opening and closing;(b) the dynamic reverse logistics network is superior to its static counterpart;and (c) cooperation cost changes affect the transaction volume between third-party and cooperative enterprises and total network cost.

关键词： Retired power battery New energy vehicle Reverse logistics multi-objective combinatorial optimization Dynamic reverse logistics network

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Approximating the Nondominated Frontiers of multi-objective combinatorial optimization Problems

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NAVAL RESEARCH LOGISTICS 2009年第2期56卷 191-198页

作者： Koeksalan, Murat Lokman, Banu Middle E Tech Univ Dept Ind Engn TR-06531 Ankara Turkey

Finding, all nondominated vectors for multi-objective combinatorial optimization (MOCO) problems is computationally very hard in general. We approximate the nondominated Frontiers of MOCO problems by fitting smooth hypersurfaces. For a given problem, we lit the hypersurface using a single nondominated reference vector. We experiment with different types of MOCO problems and demonstrate that in all cases the fitted hypersurfaces approximate all nondominated vectors well. We discuss that such an approximation is useful to find the neighborhood of preferred regions of the nondominated vectors with very little computational effort. Further computational effort can then be spent in the identified region to find the actual nondominated vectors the decision maker will prefer. (C) 2009 Wiley Periodical, Inc. Naval Research Logistics 56: 191-198, 2009

关键词： multi-objective combinatorial optimization nondominated vectors surface fitting

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Core-guided method for constraint-based multi-objective combinatorial optimization

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APPLIED INTELLIGENCE 2021年第6期51卷 3865-3879页

作者： Tian, Naiyu Ouyang, Dantong Wang, Yiyuan Hou, Yimou Zhang, Liming Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China

multi-objective combinatorial optimization(MOCO), which consists of several conflicting objectives to be optimized, finds an ever-increasing number of uses in many real-world applications. In past years, the research of MOCO mainly focuses on evolutionary algorithms. Recently, constraint-based methods come into the view and have been proved to be effective on MOCO problems. This paper builds on the previous works of constraint-based algorithm MCSEnumPD(AAAI-18) using path diversification method. Due to the inadequacy that the original method fails to prune the redundant search space effectively, this paper proposes the definition of infeasible path and develops a novel algorithm that exploits the properties of unsatisfiable cores, referred as CgPDMCS. The approach extends MCSEnumPD algorithm with a core-guided path diversification method, which avoids solving infeasible paths representing the supersets of the unsatisfiable cores. Experimental results show that the novel approach provides further performance gains over the previous constraint-based algorithms, especially for the instances tightly constrained.

关键词： Constraint SAT Minimal correction set multi-objective combinatorial optimization Pareto front

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Genetic local search for multi-objective combinatorial optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2002年第1期137卷 50-71页

作者： Jaszkiewicz, A Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland

The paper presents a new genetic local search (GLS) algorithm for mufti-objective combinatorial optimization (MOCO). The goal of the algorithm is to generate in a short time a set of approximately efficient solutions that will allow the decision maker to choose a good compromise solution. In each iteration, the algorithm draws at random a utility function and constructs a temporary population composed of a number of best solutions among the prior generated solutions. Then, a pair of solutions selected at random from the temporary population is recombined. Local search procedure is applied to each offspring. Results of the presented experiment indicate that the algorithm outperforms other mufti-objective methods based on GLS and a Pareto ranking-based mufti-objective genetic algorithm (GA) on travelling salesperson problem (TSP). (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： multi-objective combinatorial optimization metaheuristics genetic local search

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An adaptive memetic framework for multi-objective combinatorial optimization problems: studies on software next release and travelling salesman problems

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SOFT COMPUTING 2017年第9期21卷 2215-2236页

作者： Cai, Xinye Cheng, Xin Fan, Zhun Goodman, Erik Wang, Lisong China Elect Technol Grp Corp Res Inst 28 Nanjing Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Shantou Univ Sch Engn Dept Elect Engn Guangdong Prov Key Lab Digital Signal & Image Pro Shantou Guangdong Peoples R China Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI 48824 USA

In this paper, we propose two multi-objective memetic algorithms (MOMAs) using two different adaptive mechanisms to address combinatorial optimization problems (COPs). One mechanism adaptively selects solutions for local search based on the solutions' convergence toward the Pareto front. The second adaptive mechanism uses the convergence and diversity information of an external set (dominance archive), to guide the selection of promising solutions for local search. In addition, simulated annealing is integrated in this framework as the local refinement process. The multi-objective memetic algorithms with the two adaptive schemes (called uMOMA-SA and aMOMA-SA) are tested on two COPs and compared with some well-known multi-objective evolutionary algorithms. Experimental results suggest that uMOMA-SA and aMOMA-SA outperform the other algorithms with which they are compared. The effects of the two adaptive mechanisms are also investigated in the paper. In addition, uMOMA-SA and aMOMA-SA are compared with three single-objective and three multi-objective optimization approaches on software next release problems using real instances mined from bug repositories (Xuan et al. IEEE Trans Softw Eng 38(5):1195-1212, 2012). The results show that these multi-objective optimization approaches perform better than these single-objective ones, in general, and that aMOMA-SA has the best performance among all the approaches compared.

关键词： multi-objective combinatorial optimization Memetic algorithms Decomposition-based method Local search Adaptation

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An Interactive Polyhedral Approach for multi-objective combinatorial optimization with Incomplete Preference Information 1

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13th International Conference on Scalable Uncertainty Management (SUM)

作者： Benabbou, Nawal Lust, Thibaut Sorbonne Univ Lab Informat Paris 6 LIP6 CNRS F-75005 Paris France

ISBN: (数字)9783030355142

ISBN: (纸本)9783030355142;9783030355135

In this paper, we develop a general interactive polyhedral approach to solve multi-objective combinatorial optimization problems with incomplete preference information. Assuming that preferences can be represented by a parameterized scalarizing function, we iteratively ask preferences queries to the decision maker in order to reduce the imprecision over the preference parameters until being able to determine her preferred solution. To produce informative preference queries at each step, we generate promising solutions using the extreme points of the polyhedron representing the admissible preference parameters and then we ask the decision maker to compare two of these solutions (we propose different selection strategies). These extreme points are also used to provide a stopping criterion guaranteeing that the returned solution is optimal (or near-optimal) according to the decision maker's preferences. We provide numerical results for the multi-objective spanning tree and traveling salesman problems with preferences represented by a weighted sum to demonstrate the practical efficiency of our approach. We compare our results to a recent approach based on minimax regret, where preference queries are generated during the construction of an optimal solution. We show that better results are achieved by our method both in terms of running time and number of questions.

关键词： multi-objective combinatorial optimization Minimum spanning tree problem Traveling salesman problem Incremental preference elicitation Minimax regret

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On the Design and Anytime Performance of Indicator-based Branch and Bound for multi-objective combinatorial optimization

On the Design and Anytime Performance of Indicator-based Bra...

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2nd Genetic and Evolutionary Computation Conference (GECCO)

作者： Jesus, Alexandre D. Paquete, Luis Derbel, Bilel Liefooghe, Arnaud Univ Coimbra CISUC DEI Coimbra Portugal Univ Lille CNRS Cent Lille InriaUMR 9188CRIStAL F-59000 Lille France

ISBN: (纸本)9781450383509

In this article, we propose an indicator-based branch and bound (I-BB) approach for multi-objective combinatorial optimization that uses a best-first search strategy. In particular, assuming maximizing objectives, the next node to be processed is chosen with respect to the quality of its upper bound. This quality is given by a binary quality indicator, such as the binary hypervolume or the e-indicator, with respect to the archive of solutions maintained by the branch and bound algorithm. Although the I-BB will eventually identify the efficient set, we are particularly interested in analyzing its anytime behavior as a heuristic. Our experimental results, conducted on a multi-objective knapsack problem with 2, 3, 5, and 7 objectives, indicate that the I-BB can often outperform the naive depth-first and breadth-first search strategies, both in terms of runtime and anytime performance. The improvement is especially significant when the branching order for the decision variables is random, which suggests that the I-BB is particularly relevant when more favorable (problem-dependent) branching orders are not available.

关键词： multi-objective combinatorial optimization Search methodologies Branch and bound Indicator-based approaches

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Combining Local Search and Elicitation for multi-objective combinatorial optimization 1

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6th International Conference on Algorithmic Decision Theory (ADT)

作者： Benabbou, Nawal Leroy, Cassandre Lust, Thibaut Perny, Patrice Sorbonne Univ CNRS LIP6 F-75005 Paris France

ISBN: (数字)9783030314897

ISBN: (纸本)9783030314897;9783030314880

In this paper, we propose a general approach based on local search and incremental preference elicitation for solving multi-objective combinatorial optimization problems with imprecise preferences. We assume that the decision maker's preferences over solutions can be represented by a parameterized scalarizing function but the parameters are initially not known. In our approach, the parameter imprecision is progressively reduced by iteratively asking preference queries to the decision maker (1) before the local search in order to identify a promising starting solution and (2) during the local search but only when preference information are needed to discriminate between the solutions within a neighborhood. This new approach is general in the sense that it can be applied to any multi-objective combinatorial optimization problem provided that the scalarizing function is linear in its parameters (e.g., a weighted sum, an OWA aggregator, a Choquet integral) and that a (near-)optimal solution can be efficiently determined when preferences are precisely known. For the multi-objective traveling salesman problem, we provide numerical results obtained with different query generation strategies to show the practical efficiency of our approach in terms of number of queries, computation time and gap to optimality.

关键词： multi-objective combinatorial optimization Local search Preference elicitation Minimax regret Traveling salesman problem

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Local Search, data structures and Monte Carlo Search for multi-objective combinatorial optimization Problems

Local Search, data structures and Monte Carlo Search for Mul...

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作者： Marek Cornu PSL Research University

学位级别：博士

Many combinatorial optimization problems consider several, often conflicting, objectives. This thesis deals with Local Search, data structures and Monte Carlo Search methods for finding the set of efficient solutions of such problems, which is the set of all best possible trade-offs given all the objectives. We propose a new approximation method called 2-Phase Iterated Pareto Local Search based on Decomposition (2PIPLS/D) combin- ing the notions of Pareto Local Search (PLS) and Decomposition. PLS is a local search descent adapted to multi-objective spaces, and Decomposition consists in the subdivi- sion of the multi-objective problem into a number of Single-objective problems. Two Single-objective methods are considered: It- erated Local Search and Nested Monte Carlo Search. Two main components are embed- ded within the 2PIPLS/D framework. The first one generalizes and improves an exist- ing method generating an initial set of solu- tions. The second one reduces efficiently the search space and accelerates PLS without notable impact on the quality of the gener- ated approximation. We also introduce two new data structures for dynamically manag- ing a set of incomparable solutions. The first one is specialized for the bi-objective case, while the second one is general. 2PIPLS/D is applied to the bi-objective and tri-objective Traveling Salesman Problem and outperforms its competitors on tested in- stances. Then, 2PIPLS/D is instantiated on a new five-objective problem related to the re- cent territorial reform of French regions which resulted in the reassignment of departments to new larger regions.

关键词： multi-objective combinatorial optimization Meta-heuristic Local search Data structures Decomposition Monte Carlo search Traveling Salesman Problem

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