检索结果-内蒙古大学图书馆

A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models

引用

NEUROCOMPUTING 2014年 125卷 217-228页

作者： Tan, Choo Jun Lim, Chee Peng Cheah, Yu-N Univ Sains Malaysia Sch Comp Sci George Town Malaysia Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3217 Australia

In this paper, we propose a new multi-objective evolutionary algorithm-based ensemble optimizer coupled with neural network models for undertaking feature selection and classification problems. Specifically, the Modified micro Genetic algorithm (MmGA) is used to form the ensemble optimizer. The aim of the MmGA-based ensemble optimizer is two-fold, i.e. to select a small number of input features for classification and to improve the classification performances of neural network models. To evaluate the effectiveness of the proposed system, a number of benchmark problems are first used, and the results are compared with those from other methods. The applicability of the proposed system to a human motion detection and classification task is then evaluated. The outcome positively demonstrates that the proposed MmGA-based ensemble optimizer is able to improve the classification performances of neural network models with a smaller number of input features. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization evolutionary algorithm Feature selection Neural network classifiers Ensemble models

来源：评论

学校读者我要写书评

暂无评论

Optimal topology design of industrial structures using an evolutionary algorithm

引用

OPTIMIZATION AND ENGINEERING 2011年第4期12卷 681-717页

作者： Das, Raj Jones, Rhys Xie, Yi-Min Univ Auckland Ctr Adv Composite Mat Dept Mech Engn Auckland 1010 New Zealand Monash Univ Dept Mech & Aerosp Engn Melbourne Vic 3168 Australia RMIT Univ Sch Civil & Chem Engn Melbourne Vic 3001 Australia

The paper demonstrates the application of a modified evolutionary Structural Optimisation (ESO) algorithm for optimal design of topologies for complex structures. A new approach for adaptively controlling the material elimination and a 'gauss point average stress' is used as the ESO criterion in order to reduce the generation of checkerboard patterns in the resultant optimal topologies. Also, a convergence criterion is used to examine the uniformity of strength throughout a structure. The ESO algorithm is validated by comparing the ESO based solution with the result obtained using another numerical optimisation method (SIMP). The capabilities of ESO for producing an optimal design against a specified strength constraint are illustrated using two industrial design problems, viz: a bulkhead used in an aircraft structure and a sideframe used in a railway freight wagon. It has been shown that topology optimisation using ESO can result in a considerable reduction in the weight of a structure and an optimum material utilisation by generating a uniformly stressed structure. The ESO algorithm was also applied to the shape optimisation of a local geometry of the sideframe to (locally) reduce stress levels. The paper evaluates and establishes the ESO method as a practical tool for optimum topology and subsequent shape design problems for complex industrial structures.

关键词： Structural optimisation Topology optimisation evolutionary algorithm Finite element analysis Structural analysis

来源：评论

学校读者我要写书评

暂无评论

A hybrid self-adaptive evolutionary algorithm for marker optimization in the clothing industry

引用

APPLIED SOFT COMPUTING 2010年第2期10卷 409-422页

作者： Fister, Iztok Mernik, Marjan Filipic, Bogdan Jozef Stefan Inst Dept Intelligent Syst SI-1000 Ljubljana Slovenia Univ Maribor Fac Elect Engn & Comp Sci SI-2000 Maribor Slovenia Mura European Fash Design SI-9000 Murska Sobota Slovenia

The task of marker optimization in clothing production is to eliminate pieces from a work order using an optimal sequence of markers and plies, where the work order is given as a matrix of colors by sizes, markers are vectors of sizes to be laid-out and cut together, and the number of plies determines how many pieces are eliminated from the work order each time. Although the optimality of a marker sequence can be determined in several ways, we consider minimum preparation cost as a key objective in clothing production. The traditional algorithms and the simple evolutionary algorithms used in marker optimization today have relied on minimizing the number of markers, which only indirectly reduces production costs. In this paper we propose a hybrid self-adaptive evolutionary algorithm (HSA-EA) for marker optimization that improves the results of the previous algorithms and successfully deals with the objective of minimum preparation cost. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Clothing production Marker optimization 0/1 knapsack problem NP-hard problem evolutionary algorithm Self-adaptation Heuristics Hybridization

来源：评论

学校读者我要写书评

暂无评论

Improving decomposition-based multiobjective evolutionary algorithm with local reference point aided search

引用

INFORMATION SCIENCES 2021年 576卷 557-576页

作者： Jiang, Jing Han, Fei Wang, Jie Ling, Qinghua Han, Henry Fan, Zizhu Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Anqing Normal Univ Sch Comp & Informat Anqing 246133 Anhui Peoples R China Jiangsu Key Lab Secur Technol Ind Cyberspace Zhenjiang 212013 Jiangsu Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Jiangsu Univ Sci & Technol Sch Comp Sci Zhenjiang 212003 Jiangsu Peoples R China Fordham Univ Dept Comp & Informat Sci New York NY USA East China Jiaotong Univ Sch Basic Sci Nanchang 330013 Jiangxi Peoples R China

ABSTR A C T Due to the fixed and monotonous search direction, the performance of decomposition-based multiobjective evolutionary algorithms (MOEAs) highly depends on the Pareto front (PF) shape. Recent studies have highlighted the complementary effect of the ideal and nadir points. They roughly employed both as the reference points to diversify the search direction. However, few works investigate whether two points are equally important. This paper thereby proposes a novel decomposition-based MOEA, where the ideal point is consistently considered as the global reference point while the nadir point is condition-ally employed as the local one. We show that the nadir point may aid the ideal point in some cases and be recognized as a redundant one in others. More specifically, an assign-ment strategy is suggested to determine the necessity of using a local reference point for each subproblem, by considering whether the solution found by the nadir point and corre-sponding weight vector can improve the quality of the population. Experimental results finally verify the effectiveness of the proposed algorithm on 57 benchmark test problems with various PF shapes. In comparison with the state-of-the-art decomposition-based MOEAs, the proposed algorithm is promising to bring a more refined search and prevent redundant search behaviors. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Multiobjective optimization evolutionary algorithm Decomposition Ideal and nadir points Local reference point

来源：评论

学校读者我要写书评

暂无评论

An evolutionary algorithm for multi-criteria inverse optimal value problems using a bilevel optimization model

引用

APPLIED SOFT COMPUTING 2014年 23卷 308-318页

作者： Li, Hecheng Qinghai Normal Univ Key Lab Tibetan Informat Proc Minist Educ Dept Math Xining 810008 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

Given a linear program, a desired optimal objective value, and a set of feasible cost vectors, one needs to determine a cost vector of the linear program such that the corresponding optimal objective value is closest to the desired value. The problem is always known as a standard inverse optimal value problem. When multiple criteria are adopted to determine cost vectors, a multi-criteria inverse optimal value problem arises, which is more general than the standard case. This paper focuses on the algorithmic approach for this class of problems, and develops an evolutionary algorithm based on a dynamic weighted aggregation method. First, the original problem is converted into a bilevel program with multiple upper level objectives, in which the lower level problem is a linear program for each fixed cost vector. In addition, the potential bases of the lower level program are encoded as chromosomes, and the weighted sum of the upper level objectives is taken as a new optimization function, by which some potential nondominated solutions can be generated. In the design of the evolutionary algorithm some specified characteristics of the problem are well utilized, such as the optimality conditions. Some preliminary computational experiments are reported, which demonstrates that the proposed algorithm is efficient and robust. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Inverse optimal value problem Bilevel program evolutionary algorithm Solutions

来源：评论

学校读者我要写书评

暂无评论

A Novel evolutionary algorithm Inspired by Beans Dispersal

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2013年第1期6卷 79-86页

作者： Zhang, Xiaoming Sun, Bingyu Mei, Tao Wang, Rujing Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Hefei 230031 Anhui Peoples R China

Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization algorithm (BOA) is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wets' research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO) algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications.

关键词： evolutionary algorithm swarm intelligence particle swarm optimization bean optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

LOGIC GATE-BASED evolutionary algorithm FOR THE MULTIDIMENSIONAL KNAPSACK PROBLEM-WIRELESS SENSOR NETWORK APPLICATION

引用

RAIRO-OPERATIONS RESEARCH 2016年第4-5期50卷 825-843页

作者： Ferjani, Ayet Allah Liouane, Noureddine Borne, Pierre Natl Engn Sch Monastir Dept Elect Engn Monastir Tunisia Ecole Cent Lille BP 48 F-59651 Villeneuve Dascq France

evolutionary algorithms (EAs) are predominantly employed to find solutions for continuous optimization problems. As EAs are initially presented for continuous spaces, research on extending EAs to find solutions for binary spaces is in growing concern. In this paper, a logic gate-based evolutionary algorithm (LGEA) for solving some combinatorial optimization problems (COPs) is introduced. The proposed LGEA has the following features. First, it employs the logic operation to generate the trial population. Thereby, LGEA replaces common space transformation rules and classic recombination and mutation methods. Second, it is based on exploiting a variety of logic gates to search for the best solution. The variety among these logic tools will naturally lead to promote diversity in the population and improve global search abilities. The LGEA presents thus a new technique to combine the logic gates into the procedure of generating offspring in an evolutionary context. To judge the performance of the algorithm, we have solved the NP-hard multidimensional knapsack problem as well as a well-known engineering optimization problem, task allocation for wireless sensor network. Experimental results show that the proposed LGEA is promising.

关键词： evolutionary algorithm logic gate multidimensional knapsack problem task allocation wireless sensor network

来源：评论

学校读者我要写书评

暂无评论

Decomposition-based multi-objective evolutionary algorithm for vehicle routing problem with stochastic demands

引用

SOFT COMPUTING 2016年第9期20卷 3443-3453页

作者： Gee, Sen Bong Arokiasami, Willson Amalraj Jiang, Jing Tan, Kay Chen Natl Univ Singapore 4 Engn Dr 3 Singapore 117576 Singapore

Vehicle routing problem with stochastic demands (VRPSD) is a famous and challenging optimization problem which is similar to many real world problems. To resemble the real world scenario, total traveling distance, total driver remuneration, the number of vehicles used and the difference between driver remuneration are considered and formulated in the multi-objective optimization perspective. This paper aims to solve multi-objective VRPSD under the constraints of available time window and vehicle capacity using decomposition-based multi-objective evolutionary algorithm (MOEA/D) with diversity-loss-based selection method incorporates with local search and multi-mode mutation heuristics. We have also compared the optimization performance of the decomposition-based approach with the domination-based approach to study the difference between these two well-known evolutionary multi-objective algorithm frameworks. The simulation results have showed that the decomposition-based approach with diversity-loss-based selection method is able to maintain diverse output solutions.

关键词： Vehicle routing problem Multi-objective optimization evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

A Two-Stage evolutionary algorithm With Balanced Convergence and Diversity for Many-Objective Optimization

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第10期52卷 6222-6234页

作者： Ming, Fei Gong, Wenyin Wang, Ling China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Multiobjective optimization evolutionary algorithms (MOEAs) have received significant achievements in recent years. However, they encounter many difficulties in dealing with many-objective optimization problems (MaOPs) due to the weak selection pressure. One possible way to improve the ability of MOEAs for these MaOPs is to balance the convergence and diversity in the high-dimensional objective space. Based on this consideration, this article proposes a novel generic two-stage (TS) framework for MaOPs. The entire evolutionary search process is divided into two stages: in the first stage, a new subregion dominance and a modified subregion density-based mating selection mainly purse the convergence and in the second stage, a novel level-based Pareto dominance cooperates with the traditional Pareto dominance that mainly promotes diversity. Integrated into NSGA-II, the TS NSGA-II, referred to as TS-NSGA-II, is proposed. To extensively evaluate the performance of our approach, 29 benchmark problems were used as the test suite. The experimental results demonstrate our approach obtained superior or competitive performance compared with eight state-of-the-art many-objective optimization evolutionary algorithms. To study its generality, the proposed TS strategy was also combined with four other advanced methods for MaOPs. The results show that it can also improve the performance of these four methods in terms of convergence and diversity.

关键词： Convergence Optimization evolutionary computation Machine-to-machine communications Linear programming Indexes Visualization Convergence diversity evolutionary algorithm many-objective optimization two-stage (TS) strategy

来源：评论

学校读者我要写书评

暂无评论

DNA sequences design under many objective evolutionary algorithm

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 14167-14183页

作者： Guo, Huaiyu Zhu, Donglin Zhou, Changjun Zou, Chengye Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China

DNA computing is conducted through reactions between DNA molecules, the quality of DNA sequences is directly influence on reactions. Following previous works, there are five metrics to estimate quality of DNA sequences and one constraint to follow. evolutionary algorithms are widely applied in this field, conventional frames are often using multi-objective strategies to solve this problem. However, multi-objective strategies loss its efficiency in solving high dimensional problems especially Pareto Front is irregular. In this article, a many-objective evolutionary algorithm, R2HCAEMOA, is introduced to tackle with increased objective dimension. To increase diversity from beginning, chaotic mapping is applied to initialize decision variables of population. Since purpose of many-objective optimization algorithms is to find evenly distributed solution set on Pareto Front, decision makers are faced difficulty in solution selection. A method for choosing the most interesting solution from solution set is determined. Besides, an incremental scheme to generate a DNA sequence set is applied to enforce stability of evolutionary environment. The average values on each metrics are {0, 0, 56.00, 42.85, 0.15}, which the metrics are {continuity, hairpin, H-measure, similarity, variance of melting temperature}. Running time of our frame is significantly reduced compared with previous works. The results have shown our work is competitive among previous works and incline balanced value on each objective dimension.

关键词： DNA sequence design DNA computing evolutionary algorithm Many-objective optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：