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12th International Joint Conference on Software Technologies (ICSOFT)

作者： Ben Mansour, Imen Alaya, Ines Tagina, Moncef Univ Manouba Natl Sch Comp Sci Manouba 2010 Tunisia

ISBN: (纸本)9783319936413;9783319936406

multiobjective optimization has grown to become an active research area since almost all real-world problems have multiple, usually conflicting, objectives. In this paper, we focus on the multiobjective knapsack problem, we solve instances with two, three and four objectives from the literature. We define an iterated local search algorithm in which a Tchebycheff function is used as a selection process to generate a good approximation of the efficient set. The proposed algorithm, Min-Max TLS, designs an efficient neighborhood function based on a permutation process. Min-Max TLS is compared with state-of-the-art approaches such as 2PPLS and MOTGA. Results show that our algorithm can achieve a good balance between exploitation and exploration during the search process.

关键词： multiobjective knapsack problem Iterated local search Scalarization functions Tchebycheff functions

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Incorporating ε-dominance in AMOSA: Application to multiobjective 0/1 knapsack problem and clustering gene expression data

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APPLIED SOFT COMPUTING 2013年第5期13卷 2405-2411页

作者： Bandyopadhyay, Sanghamitra Maulik, Ujjwal Chakraborty, Rudrasis Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Jadavpur Univ Kolkata 700032 India

Recently, a new model of multiobjective simulated annealing, AMOSA, was developed which was found to provide improved performance for several multi objective optimization problems especially for problems with many objectives. In this article, we aim to further improve the performance of AMOSA by incorporating the concept of epsilon-dominance which is a more generalized form of conventional dominance. This strategy is referred to as epsilon-AMOSA. The result of epsilon-AMOSA is compared with those of AMOSA, NSGA-II and epsilon-MOEA and AMOSA for several test problems with number of objectives varying from two to fifteen and the number of variables varying from one to thirty. The performance of epsilon-AMOSA is also compared with other strategies for multiobjective 0/1 knapsack problem. A real life application of epsilon-AMOSA for clustering genes from gene expression data is also demonstrated. The results demonstrate the effectiveness of epsilon-AMOSA. (C) 2012 Elsevier B. V. All rights reserved.

关键词： Additive epsilon-AMOSA multiobjective simulated annealing multiobjective knapsack problem Multiplicative epsilon-AMOSA MOO strategy multiobjective optimization epsilon-Dominance

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Neuroevolution for solving multiobjective knapsack problems

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 116卷 65-77页

作者： Denysiuk, Roman Gaspar-Cunha, Antonio Delbem, Alexandre C. B. Univ Minho IPC P-4800058 Guimaraes Portugal Univ Sao Paulo Inst Math & Comp Sci BR-13566590 Sao Carlos SP Brazil

The multiobjective knapsack problem (MOKP) is an important combinatorial problem that arises in various applications, including resource allocation, computer science and finance. When tackling this problem by evolutionary multiobjective optimization algorithms (EMOAs), it has been demonstrated that traditional recombination operators acting on binary solution representations are susceptible to a loss of diversity and poor scalability. To address those issues, we propose to use artificial neural networks for generating solutions by performing a binary classification of items using the information about their profits and weights. As gradient-based learning cannot be used when target values are unknown, neuroevolution is adapted to adjust the neural network parameters. The main contribution of this study resides in developing a solution encoding and genotype-phenotype mapping for EMOAs to solve MOKPs. The proposal is implemented within a state-of-the-art EMOA and benchmarked against traditional variation operators based on binary crossovers. The obtained experimental results indicate a superior performance of the proposed approach. Furthermore, it is advantageous in terms of scalability and can be readily incorporated into different EMOAs. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Evolutionary computation multiobjective knapsack problem Neuroevolution

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Combining Artificial Neural Networks and Evolution to Solve multiobjective knapsack problems 19

Combining Artificial Neural Networks and Evolution to Solve ...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Denysiuk, Roman Gaspar-Cunha, Antonio Delbem, Alexandre C. B. CEA LIST Gif Sur Yvette France Univ Minho Guimaraes Portugal Univ Sao Paulo Sao Carlos Brazil

ISBN: (纸本)9781450367486

The multiobjective knapsack problem (MOKP) is a combinatorial problem that arises in various applications, including resource allocation, computer science and finance. Evolutionary multiobjective optimization algorithms (EMOAs) can be effective in solving MOKPs. Though, they often face difficulties due to the loss of solution diversity and poor scalability. To address those issues, our study [2] proposes to generate candidate solutions by artificial neural networks. This is intended to provide intelligence to the search. As gradient-based learning cannot be used when target values are unknown, neuroevolution is adapted to adjust the neural network parameters. The proposal is implemented within a state-of-the-art EMOA and benchmarked against traditional search operators base on a binary crossover. The obtained experimental results indicate a superior performance of the proposed approach. Furthermore, it is advantageous in terms of scalability and can be readily incorporated into different EMOAs.

关键词： Evolutionary computing artificial neural networks multiobjective knapsack problem

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Priority based ε dominance: A new measure in multiobjective optimization

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INFORMATION SCIENCES 2015年 305卷 97-109页

作者： Bandyopadhyay, Sanghamitra Chakraborty, Rudrasis Maulik, Ujjwal Indian Stat Inst MIU Kolkata 700108 India Indian Stat Inst ECSU Kolkata 700108 India Jadavpur Univ Kolkata 700032 India

Dominance is a fundamental concept in multiobjective Optimization (MOO) where a solution is said to,dominate the other if it is better in at least one objective, and same as or better in the other objectives. Given a pair of solutions, if neither dominates the other, then the solutions are said to be non-dominated. In real spaces, a large number of non-dominated solutions can exist in principle, making convergence of MOO techniques rather slow. Relaxed forms of dominance have been proposed in the literature for faster convergence. One such form is the epsilon-dominance, which in principle, partitions the space into epsilon-sized grids. The value of epsilon is often user defined and the performance of epsilon-dominance based multi-objective evolutionary algorithms (epsilon-MOEAs) critically depends on the value of epsilon. In this article we propose a novel way of determining the value of epsilon, which is different for each objective function, based on the correlation between them. We call this approach Priority Based epsilon (PBE) as the method ranks the objectives according to their priority before calculating the epsilon values. PBE is incorporated in an archived simulated annealing based MOO technique called AMOSA. AMOSA has been earlier shown to outperform several well-known MOO techniques especially for many objective optimization. PBE based AMOSA, referred to as PBE-AMOSA, is found to comprehensively outperform AMOSA, MOEA/D-DE, the conventional epsilon based version epsilon-AMOSA and epsilon-MOEA both in case of benchmark test problems and 0/1 knapsack problem. (C) 2015 Elsevier Inc. All rights reserved.

关键词： epsilon-dominance multiobjective simulated annealing multiobjective knapsack problem PBE-AMOSA

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A multiobjective multidimensional knapsack model for exhibition selection for science centers

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2024年第1期40卷 165-177页

作者： Hulagu, Kemal Taha Figlali, Alpaslan Kocaeli Metropolitan Municipal TR-41040 Izmit Kocaeli Turkiye Kocaeli Univ Fac Engn Ind Engn Dept TR-41001 Izmit Kocaeli Turkiye

ience Centers carry out their missions through the exhibits and exhibitions they have. The selection of theexhibits is made under the space limitations and budget constraints, taking into account the center's goalsand the visitor profile. However, there is no mathematical approach in the literature for the selection ofexhibits in order to create exhibitions for the determined objective. It is seen that intuitive methods areapplied and especially the experiences of the curators are used to create the exhibition for science centers. Creating a new exhibition for a science center can be taken as the process of bringing together a portfolio ofvarious exhibits to optimize certain objectives and can be modeled as a knapsack problem. In this study, amathematical programming-based multiobjective method is proposed for the selection of the most suitableexhibits in science centers. The problem is considered as a multiobjective, multidimensional knapsackproblem. It is modeled and solved as a 0-1 integer programming model. Suggested model selects the exhibits that supports the mission of the science center at a high level, has a high instructional level and attractivenessunder the constraints of the total area, purchasing budget and annual operating budget. In the model, there is an additional constraint to ensure that the exhibits that make up the portfolio are selected from different fieldsof science. Considering the time required to solve the various problems created and the number of dominantsolutions obtained, it can be said that the proposed model can be easily used in real life problems.

关键词： Science center multiobjective knapsack problem multidimensional knapsack problem exhibition selection

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Hybridization of Decomposition and Local Search for multiobjective Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第10期44卷 1808-1820页

作者： Ke, Liangjun Zhang, Qingfu Battiti, Roberto Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Univ Trento Dept Informat Engn & Comp Sci I-38122 Trento Italy

Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: memetic algorithm based on decomposition (MOMAD). It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MOMAD evolves three populations: 1) population P-L for recording the current solution to each subproblem;2) population P-P for storing starting solutions for Pareto local search;and 3) an external population P-E for maintaining all the nondominated solutions found so far during the search. A problem-specific single objective heuristic can be applied to these subproblems to initialize the three populations. At each generation, a Pareto local search method is first applied to search a neighborhood of each solution in P-P to update P-L and P-E. Then a single objective local search is applied to each perturbed solution in P-L for improving P-L and P-E, and reinitializing P-P. The procedure is repeated until a stopping condition is met. MOMAD provides a generic hybrid multiobjective algorithmic framework in which problem specific knowledge, well developed single objective local search and heuristics and Pareto local search methods can be hybridized. It is a population based iterative method and thus an anytime algorithm. Extensive experiments have been conducted in this paper to study MOMAD and compare it with some other state-of-the-art algorithms on the multiobjective traveling salesman problem and the multiobjective knapsack problem. The experimental results show that our proposed algorithm outperforms or performs similarly to the best so far heuristics on these two problems.

关键词： Decomposition local search multiobjective knapsack problem multiobjective optimization multiobjective traveling salesman problem

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A parallel MOEA with criterion-based selection applied to the knapsack problem

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APPLIED SOFT COMPUTING 2019年 80卷 358-373页

作者： Kantour, Nedjmeddine Bouroubi, Sadek Chaabane, Djamel USTHB LIFORCE Lab Fac Math Dept Operat Res PB 32 El Alia Algiers 16111 Algeria USTHB AMCD RO Lab Fac Math Dept Operat Res PB 32 El Alia Algiers 16111 Algeria

In this paper, we propose a parallel multiobjective evolutionary algorithm called Parallel Criterion-based Partitioning MOEA (PCPMOEA), with an application to the multiobjective knapsack problem (MOKP). The suggested search strategy is based on a periodic partitioning of potentially efficient solutions, which are distributed to multiple multiobjective evolutionary algorithms (MOEAs). Each MOEA is dedicated to a sole objective, in which it combines both criterion-based and dominance-based approaches. The suggested algorithm addresses two main sub-objectives: minimizing the distance between the current non-dominated solutions and the ideal point, and ensuring the spread of the potentially efficient solutions. Experimental results are included, where we assess the performance of the suggested algorithm against the above mentioned sub-objectives, compared with state-of-the-art results using well-known multi-objective metaheuristics. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Parallel evolutionary algorithms multiobjective discrete optimization multiobjective knapsack problem

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Half-open polyblock for the representation of the search region in multiobjective optimization problems: its application and computational aspects

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4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH 2021年第1期19卷 41-70页

作者： Hoai, Pham Thi Thi, Hoai An Le Nam, Nguyen Canh Hanoi Univ Sci & Technol Sch Appl Math & Informat Dept Appl Math 1 Dai Co Viet Rd Hanoi Vietnam Univ Lorraine LGIPM Dept Informat & Applicat F-57000 Metz France

The search region in multiobjective optimization problems is a part of the objective space where nondominated points could lie. It plays an important role in the generation of the nondominated set of multiobjective combinatorial optimization (MOCO) problems. In this paper, we establish the representation of the search region by half-open polyblocks (a variant concept of "polyblock" in monotonic optimization) and propose a new procedure for updating the search region. We also study the impact of stack policies to the new procedure and the existing methods that update the search region. Stack policies are then analyzed, pointing out their performance effectiveness by means of the results of rich computational experiments on finding the whole set of nondominated points of MOCO problems.

关键词： multiobjective programming multiobjective discrete programming Monotonic optimization Weighted sum scalarization e-constraint method Multi-service outsourcing problem multiobjective knapsack problem multiobjective assignment problem

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Comparing Solution Sets of Different Size in Evolutionary Many-Objective Optimization

Comparing Solution Sets of Different Size in Evolutionary Ma...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Ishibuchi, Hisao Masuda, Hiroyuki Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

ISBN: (纸本)9781479974924

When the performance of different evolutionary multiobjective optimization (EMO) algorithms is compared, the same population size is usually used for all EMO algorithms in computer simulations. This setting is to obtain a solution set of the same size from a different algorithm. However, in general, each algorithm may have its own best parameter specifications for each test problem. Thus, it may be difficult to appropriately specify the same population size for all algorithms for their fair comparison. A different algorithm may be evaluated as being the best for a different specification of the population size. An alternative setting is to allow each algorithm to use its own best population size. In this setting, a solution set of different size is obtained from each algorithm. It may be difficult to perform fair comparison using solution sets of different size. In this paper, we discuss the difficulty in comparing EMO algorithms under these two settings of the population size: the same specification for all algorithms and a different specification for each algorithm. First, we discuss the effect of the number of non-dominated solutions on some performance indicators. Next we show the difficulty in the first setting: Performance of each algorithm depends on the population size. Then we discuss the difficulty in the second setting: The size of a solution set obtained by each algorithm is not the same. In this setting, we examine the use of solution selection as a post-processing procedure to choose the same number of solutions from each solution set of different size. The selected solutions are used for performance comparison.

关键词： Evolutionary many-objective optimization solution selection multiobjective knapsack problem performance indicator

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