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An investigation into many-objective optimization on combinatorial problems: Analyzing the pickup and delivery problem

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SWARM AND evolutionary COMPUTATION 2018年 38卷 218-230页

作者： Garcia-Najera, Abel Lopez-Jaimes, Antonio Univ Autanoma Metropolitana Dept Matemat Aplicadas & Sistemas Unidad Cuajimalpa Av Vasco de Quiroga 4871Col Santa Fe Cuajimalpa Mexico City 05348 DF Mexico

Many-objective optimization focuses on solving optimization problems with four or more objectives. Effort has been made mainly on studying continuous problems, with interesting results and for which several proposals have appeared. An important result states that the problem does not necessarily becomes more difficult while more objectives are considered. Nevertheless, combinatorial problems have not received an appropriate attention, making this an open research area. This investigation takes this subject on by studying a many objective combinatorial problem, particularly, the pickup and delivery problem (PDP), which is an important combinatorial optimization problem in the transportation industry and consists of finding a collection of routes with minimum cost. Traditionally, cost has been associated with the number of routes and the total travel distance, however, in many applications, some other objectives emerge, for example, travel time, workload imbalance, and uncollected profit. If we consider all these objectives equally important, PDP can be tackled as a many-objective problem. This study is concerned with the study of: (i) the performance of four representative multi-objective evolutionary algorithms on PDP varying the number of objectives, (ii) the properties of the many-objective PDP regarding scalability, i.e. the conflict between each pair of objectives and the proportion of non-dominated solutions as the number of objectives is varied, and finally (iii) the change of PDP's difficulty when the number of objectives is increased. Results show that the regarded objectives are actually in conflict and that the problem is more difficult to solve while more objectives are considered.

关键词： Many-objective optimization Pickup and delivery problem multi-objective evolutionary algorithms Combinatorial optimization

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Two-archive method for aggregation-based many-objective optimization

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INFORMATION SCIENCES 2018年 422卷 305-317页

作者： Cai, Lei Qu, Shiru Cheng, Guojian Xian Univ Technol Fac Comp Sci & Engn Xian 710048 Shaanxi Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Shaanxi Peoples R China Xian Shiyou Univ Sch Comp Sci Xian 710065 Shaanxi Peoples R China

In this paper, a novel two-archive method is proposed for solving many-objective optimization problems. Our aim is to exploit the advantages of using two separate archives to balance the convergence and diversity. To this end, two updating strategies based on the aggregation-based framework are presented and incorporated into the two-archive method. In addition, we further extend this method by eliminating the restricted neighbourhood models. The proposed algorithms have been tested extensively on a number of well-known benchmark problems with 3-20 objectives. Experimental results reveal that the proposed algorithms work well on the many-objective optimization problems with different characteristics. (C) 2017 Elsevier Inc. All rights reserved.

关键词： evolutionary computation multi-objective evolutionary algorithms Many-objective optimization Two archives Aggregation-based method

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A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout

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COMPUTERS IN BIOLOGY AND MEDICINE 2019年 113卷 103390-000页

作者： Daud, Kauthar Mohd Mohamad, Mohd Saberi Zakaria, Zalmiyah Hassan, Rohayanti Shah, Zuraini Ali Deris, Safaai Ibrahim, Zuwairie Napis, Suhaimi Sinnott, Richard O. Univ Teknol Malaysia Fac Engn Sch Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Univ Malaysia Kelantan Inst Artificial Intelligence & Big Data City Campus Kota Baharu 16100 Kelantan Malaysia Univ Malaysia Kelantan Fac Bioengn & Technol Jeli CampusLock Bag 100 Jeli 17600 Kelantan Malaysia Univ Teknol Malaysia Fac Engn Sch Comp Software Engn Res Grp Johor Baharu 81310 Malaysia Univ Malaysia Pahang Fac Elect & Elect Engn Pekan Pahang Malaysia Univ Putra Malaysia Fac Biotechnol & Biomol Sci Dept Cell & Mol Biol Serdang 43400 Selangor Malaysia Univ Melbourne Sch Comp & Informat Syst Melbourne Sch Engn Melbourne Vic 3010 Australia

Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate.

关键词： multi-objective evolutionary algorithms Metabolic engineering Flux balance analysis Reaction knockout Pareto dominance Artificial intelligence Bioinformatics

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Solving a stochastic multi-objective and multi-period hub location problem considering economic aspects by meta-heuristics: application in public transportation

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2019年第3期60卷 183-202页

作者： Hamid, Mahdi Bastan, Mahdi Hamid, Mojtaba Sheikhahmadi, Farrokh Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Univ Eyvanekey Dept Ind Engn Garmsar Iran Iran Univ Sci & Technol Dept Ind Engn Tehran Iran Virginia Tech Pamplin Coll Business Dept Business Informat Technol Blacksburg VA 24061 USA

In this paper, a multi-objective scenario based mathematical model is presented for the capacitated hub location problem in public transportation considering economic and investment aspects. Three objective functions are regarded in the presented mathematical model. The first one aims to minimise the total costs considering the possibility of investing the unused budget at each period. The second one aims to minimise the total processing time in the hub network at each period. The last one aims to minimise the maximum distance between each pair of origin-destination nodes in the network. To solve the model, three multi-objective meta-heuristic algorithms are developed, namely S metric selection evolutionary multi-objective optimisation algorithm (SMS-EMOA), multi-objective imperialist competitive algorithm (MOICA) and non-dominated sorting genetic algorithm (NSGA-II). Finally, developed algorithms are compared to each other based on several comparison measures using a relatively novel statistical approach.

关键词： hub location problem multi-period hub location problem uncertainty economic aspects multi-objective evolutionary algorithms

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To Preserve or Not to Preserve Invalid Solutions in Search-Based Software Engineering: A Case Study in Software Product Lines 18

To Preserve or Not to Preserve Invalid Solutions in Search-B...

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40th ACM/IEEE International Conference on Software Engineering (ICSE)

作者： Guo, Jianmei Shi, Kai Alibaba Grp Hangzhou Zhejiang Peoples R China East China Univ Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781450356381

multi-objective evolutionary algorithms (MOEAs) have been successfully applied for software product lines (SPLs) to search for optimal or near-optimal solutions that balance multiple objectives. However, MOEAs usually produce invalid solutions that violate the constraints predefined. As invalid solutions are unbuildable in practice, we debate the preservation of invalid solutions during the search. We conduct experiments on seven real-world SPLs, including five largest SPLs hitherto reported and two SPLs with realistic values and constraints of quality attributes. We identify three potential limitations of preserving invalid solutions. Furthermore, based on the state-of-the-art, we design five algorithm variants that adopt different evolutionary operators. By performance evaluation, we provide empirical guidance on how to preserve valid solutions. Our empirical study demonstrates that whether or not to preserve invalid solutions deserves more attention in the community, and in some cases, we have to preserve valid solutions all along the way.

关键词： Search-based software engineering software product lines multi-objective evolutionary algorithms constraint solving validity

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Visualizing the Optimization Process for multi-objective Optimization Problems 17th

Visualizing the Optimization Process for Multi-objective Opt...

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

作者： Chakuma, Bayanda Helbig, Marde Univ Pretoria Dept Comp Sci Pretoria South Africa

ISBN: (纸本)9783319912530;9783319912523

Visualization techniques used to visualize the optimization process of multi-objective evolutionary algorithms (MOEAs) have been discussed in the literature, predominantly in the context of aiding domain experts in decision making and in improving the effectiveness of the design optimization process. These techniques provide the decision maker with the ability to directly observe the performance of individual solutions, as well as their distribution in the approximated Pareto-optimal front. In this paper a visualization technique to study the mechanics of a MOEA, as it is solving multi-objective optimization problems (MOOPs), is discussed. The visualization technique uses a scatterplot animation to visualize the evolutionary process of the algorithms search, focusing on the changes in the population of non-dominated solutions obtained for each generation. The ability to visualize the optimization process of the algorithm provides the means to evaluate the performance of the algorithm, as well as visually observing the trade-offs between objectives.

关键词： Visualization Scatterplot multi-objective optimization multi-objective evolutionary algorithms

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Collaborative multi-objective Optimization for Distributed Design of Complex Products 18

Collaborative Multi-Objective Optimization for Distributed D...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Duro, Joao A. Yan, Yiming Purshouse, Robin C. Fleming, Peter J. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

ISBN: (纸本)9781450356183

multidisciplinary design optimization problems with competing objectives that involve several interacting components can be called complex systems. Nowadays, it is common to partition the optimization problem of a complex system into smaller subsystems, each with a subproblem, in part because it is too difficult to deal with the problem all-at-once. Such an approach is suitable for large organisations where each subsystem can have its own (specialised) design team. However, this requires a design process that facilitates collaboration, and decision making, in an environment where teams may exchange limited information about their own designs, and also where the design teams work at different rates, have different time schedules, and are normally not co-located. A multi-objective optimization methodology to address these features is described. Subsystems exchange information about their own optimal solutions on a peer-to-peer basis, and the methodology enables convergence to a set of optimal solutions that satisfy the overall system. This is demonstrated on an example problem where the methodology is shown to perform as well as the ideal, but "unrealistic" approach, that treats the optimization problem all-at-once.

关键词： Collaborative multidisciplinary optimization Complex systems multi-objective evolutionary algorithms multiple-criteria decision-making

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Aesthetic Design Using multi-objective evolutionary algorithms

Aesthetic Design Using Multi-Objective Evolutionary Algorith...

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6th International Conference on evolutionary multi-Criterion Optimization

作者： Gaspar-Cunha, Antonio Loyens, Dirk van Hattum, Ferrie Univ Minho Inst Polymer & Composites I3N Campus Azurem P-4800058 Guimaraes Portugal Univ Minho Sch Architecture P-4800058 Guimaraes Portugal ESAD Escola Superior Artes Design Avenida Calouste Gulbenkian P-4460268 Senhora Hora Matosinhos Portugal

ISBN: (纸本)9783642198922

The use of computational methodologies for the optimization of aesthetic parameters is not frequent mainly due to the fact that these parameters are not quantifiable and are subjective. In this work an interactive methodology based on the use of multi-objective optimization algorithms is proposed. This strategy associates the results of different optimization runs considering the existent quantifiable objectives and different sets of boundary conditions concerning the decision variables, as defined by an expert decision maker. The associated results will serve as initial population of solutions for a final optimization run. The idea is that a more global picture of potential "good" solutions can be found. At the end this will facilitate the work of the expert decision maker since more solutions are available. The method was applied to a case study and the preliminary results obtained showed the potentially of the strategy adopted.

关键词： aesthetic design multi-objective evolutionary algorithms

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SUSTAINABLE CLOSED-LOOP SUPPLY CHAIN NETWORK DESIGN

SUSTAINABLE CLOSED-LOOP SUPPLY CHAIN NETWORK DESIGN

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作者： Ehsan Pourjavad University of Regina

学位级别：博士

Sustainable Closed Loop Supply Chain (CLSC) management is increasingly adopted by companies, due to increasing concerns for the environmental, legislative compliance, decreasing availability of raw materials, and customer demands for environmentally friendly products. Sustainable CLSC network design provides a platform which ensures an effective and efficient supply chain management. In this thesis, the sustainable CLSC network design problem was formulated deterministically via Mixed Integer Linear Programming (MILP), and non- deterministically via Fuzzy multi-objective Mixed Integer Linear Programming (FMOMILP) model, by considering sustainability and uncertainty. Fuzzy programming approaches were utilized to solve the problem. Two multi-objective evolutionary algorithms were employed to find the optimal solutions for large cases. Computational experiments were conducted, as well as studying actual industrial cases, to illustrate the applicability and significance of the proposed approaches and solution methods. Results showed that the Fuzzy Programming approach presents a systematic framework that enables management to obtain a satisfactory solution by adjusting the search direction. The results also demonstrated that the adopted Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is a satisfactory technique to solve large scale sustainable CLSC network design problems.

关键词： Closed Loop Supply Chain Sustainability Uncertainty Fuzzy multi-objective evolutionary algorithms

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multi-objective evolutionary feature selection for online sales forecasting

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NEUROCOMPUTING 2017年 234卷 75-92页

作者： Jimenez, F. Sanchez, G. Garcia, J. M. Sciavicco, G. Miralles, L. Univ Murcia Fac Comp Sci Madrid Spain Univ Ferrara Dept Math & Comp Sci Ferrara Italy Univ Panamericana Fac Ingn Campus Mexico Augusto Rodin 498 Mexico City 03920 Mexico

Sales forecasting uses historical sales figures, in association with products characteristics and peculiarities, to predict short-term or long-term future performance in a business, and it can be used to derive sound financial and business plans. By using publicly available data, we build an accurate regression model for online sales forecasting obtained via a novel feature selection methodology composed by the application of the multi objective evolutionary algorithm ENORA (evolutionary NOn-dominated Radial slots based Algorithm) as search strategy in a wrapper method driven by the well-known regression model learner Random Forest. Our proposal integrates feature selection for regression, model evaluation, and decision making, in order to choose the most satisfactory model according to an a posteriori process in a multi-objective context. We test and compare the performances of ENORA as multi-objective evolutionary search strategy against a standard multi objective evolutionary search strategy such as NSGA-11 (Non-dominated Sorted Genetic Algorithm), against a classical backward search strategy such as RFE (Recursive Feature Elimination), and against the original data set.

关键词： multi-objective evolutionary algorithms Feature selection Random forest Regression model Online sales forecasting

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