2013 IEEE Congress on Evolutionary Computation最新文献

Constrained evolutionary optimization of a distillation train in chemical engineering 化工蒸馏流程的约束演化优化

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-07-15 DOI: 10.1109/CEC.2013.6557839

R. Gutierrez-Guerra, R. Murrieta-Dueñas, J. Cortez-González, A. H. Aguirre, J. Segovia‐Hernández

{"title":"Constrained evolutionary optimization of a distillation train in chemical engineering","authors":"R. Gutierrez-Guerra, R. Murrieta-Dueñas, J. Cortez-González, A. H. Aguirre, J. Segovia‐Hernández","doi":"10.1109/CEC.2013.6557839","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557839","url":null,"abstract":"The optimal design and synthesis of distillation systems remains one of the most challenging problems in process engineering. The goal of this paper is to introduce an evolutionary approach for the optimization of the total energy consumption of distillation systems with constraints. Moreover, the contribution of this paper is a novel constraint handling technique that manages design goals as equality constraints, such as the purity and the recovery of the final components. In the literature of these problems prevail the use of inequality constraints; although easy to apply they may lead the search to suboptimal solutions. The case study is a distillation column sequence (DCS) for the separation of four components; this problem is easy to describe yet complex to solve so our approach can show its advantages. The evolutionary algorithm Boltzmann Univariate Marginal Distribution Algorithm, (BUMDA), performs the optimization. AspenONE©software is used for the rigorous evaluation of the fitness function of the population. The results show the efficacy performance of the proposed approach reaching near optimal designs in less than 3000 function evaluations.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128679455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An evolutionary approach to the multi-objective pickup and delivery problem with time windows 带时间窗的多目标取货问题的一种进化方法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-07-15 DOI: 10.1109/CEC.2013.6557676

Abel García-Nájera, M. Gutiérrez-Ándrade

{"title":"An evolutionary approach to the multi-objective pickup and delivery problem with time windows","authors":"Abel García-Nájera, M. Gutiérrez-Ándrade","doi":"10.1109/CEC.2013.6557676","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557676","url":null,"abstract":"The pickup and delivery problem (PDP) has many real-life applications. In this problem there is a customer set which is partitioned into two subsets: the customers requiring an amount of product (delivery) and the customers providing the product (pickup). There is also a set of transportation requests, which specify the quantity of product that has to be picked up from an origin customer and delivered to a destination customer. There exist a number of vehicles available to be used for completing these tasks. PDP consists of finding a collection of routes with minimum cost, such that all transportation request are serviced. Traditionally, the number of routes has been minimized first, and then the travel distance, however, if these objectives are considered to be equally important, the problem can be tackled as a bi-objective problem. Moreover, time is not always directly proportional to distance, thus travel time can also be considered an important criterion to be optimized and, consequently, PDP has to be regarded as a tri-objective problem. In this paper, we solve PDP as a problem with multiple objectives by means of an evolutionary algorithm and evaluate its performance with proper multi-objective performance tools.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116652259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

A new algorithm for reducing metaheuristic design effort 一种减少元启发式设计工作量的新算法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-07-15 DOI: 10.1109/CEC.2013.6557972

M. Riff, Elizabeth Montero

引用次数: 39

Evaluation of gossip Vs. broadcast as communication strategies for multiple swarms solving MaOPs 多群体解决MaOPs的传播策略评价:八卦与广播

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-07-15 DOI: 10.1109/CEC.2013.6557740

A. D. Campos, A. Pozo, E. P. Duarte

{"title":"Evaluation of gossip Vs. broadcast as communication strategies for multiple swarms solving MaOPs","authors":"A. D. Campos, A. Pozo, E. P. Duarte","doi":"10.1109/CEC.2013.6557740","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557740","url":null,"abstract":"In this work we evaluate the application of multiple independent swarms to solve Many-Objective Problems (MaOPs). Solving MaOPs is often a challenge, as these problems do not have a single best solution, but a set of solutions. Furthermore, the objectives to be optimized are usually conflicting among themselves. Employing multiple independent swarms that evolve independently from each other is an effective optimization strategy, that pushes convergence while preserving the diversity of the solutions. One of the key decisions for organizing a set of swarms is to define the communication strategy they use to share solutions. The strategy defines how particles migrate among the swarms, and how much interaction they feature among themselves. We evaluate two multi-swarm communication strategies, broadcast and the probabilistic gossip to 1-neighbor. Extensive simulation results are presented for two members of the DTLZ family with 2, 3, 4, 5, 10, 15, and 20 objectives. A set of quality indicators were evaluated for both communication strategies as well as for a baseline reference execution based on a single swarm. Results show that both distributed strategies outperform the centralized alternative. It is also possible to conclude that the higher level of interactivity of the broadcast alternative proved to be the best for several scenarios.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128423814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evolving feature selection for characterizing and solving the 1D and 2D bin packing problem 演化特征选择用于描述和求解一维和二维装箱问题

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-07-15 DOI: 10.1109/CEC.2013.6557816

Eunice López-Camacho, H. Terashima-Marín

引用次数: 3

A study on two-step search based on PSO to improve convergence and diversity for Many-Objective Optimization Problems 基于粒子群的两步搜索改进多目标优化问题的收敛性和多样性研究

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-07-15 DOI: 10.1109/CEC.2013.6557785

Hiroyuki Hirano, T. Yoshikawa

{"title":"A study on two-step search based on PSO to improve convergence and diversity for Many-Objective Optimization Problems","authors":"Hiroyuki Hirano, T. Yoshikawa","doi":"10.1109/CEC.2013.6557785","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557785","url":null,"abstract":"Particle Swarm Optimization (PSO) is one of the most effective search methods in optimization problems. Multiobjective Optimization Problems (MOPs) has been focused on and PSO researches applied to MOPs have been reported. On the other hand, the problem that the search performance using conventional methods for MOPs becomes low is reported in Many-objective Optimization Problems (MaOPs) which have four or more objective functions. The authors have proposed two-step search method based on PSO for MaOPs. In the first step, it divides the population into some groups, and each group performs the single objective search for each objective function and the center of them. In the second step, the search is performed to acquire the diversity of Pareto solutions by PSO search with the goal, global-best, based on the solutions acquired in the first step. This paper defines the real coded multi-objective knapsack problem and studies the performance of the proposed method applied to this problem. The experimental results shows that the search of the first step for high convergence and that of the second step for large diversity aimed in the proposed method works well. It also shows that the proposed method is superior to other conventional methods especially in terms of the convergence in MaOPs.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115480004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

A new performance metric for user-preference based multi-objective evolutionary algorithms 基于用户偏好的多目标进化算法的性能度量

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-07-15 DOI: 10.1109/CEC.2013.6557912

A. Mohammadi, M. Omidvar, Xiaodong Li

{"title":"A new performance metric for user-preference based multi-objective evolutionary algorithms","authors":"A. Mohammadi, M. Omidvar, Xiaodong Li","doi":"10.1109/CEC.2013.6557912","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557912","url":null,"abstract":"In this paper, we propose a metric for evaluating the performance of user-preference based evolutionary multiobjective algorithms by defining a preferred region based on the location of a user-supplied reference point. This metric uses a composite front which is a type of reference set and is used as a replacement for the Pareto-optimal front. This composite front is constructed by extracting the non-dominated solutions from the merged solution sets of all algorithms that are to be compared. A preferred region is then defined on the composite front based on the location of a reference point. Once the preferred region is defined, existing evolutionary multi-objective performance metrics can be applied with respect to the preferred region. In this paper the performance of a cardinality-based metric, a distance-based metric, and a volume-based metric are compared against a baseline which relies on knowledge of the Pareto-optimal front. The experimental results show that the distance-based and the volume-based metrics are consistent with the baseline, showing meaningful comparisons. However, the cardinality-based approach shows some inconsistencies and is not suitable for comparing the algorithms.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121379885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 60

Differential evolution on the CEC-2013 single-objective continuous optimization testbed CEC-2013单目标连续优化试验台的差分演化

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557689

A. K. Qin, Xiaodong Li

引用次数: 45

Efficient parent selection for Approximation-Guided Evolutionary multi-objective optimization 基于近似制导的进化多目标优化的高效亲代选择

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557784

Markus Wagner, T. Friedrich

引用次数: 9

Wind tunnel evaluation-based optimization for improvement of flow control by plasma actuator using kriging model-based genetic algorithm 基于kriging模型的遗传算法改进等离子体执行器流动控制的风洞优化

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557878

Masahiro Kanazaki, T. Matsuno, Kengo Maeda, H. Kawazoe

引用次数: 1