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Hybrid evolutionary approaches for the single machine order acceptance and scheduling problem

APPLIED SOFT COMPUTING 2017年 52卷 725-747页

作者： Chaurasia, Sachchida Nand Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

This paper presents two hybrid metaheuristic approaches, viz. a hybrid steady-state genetic algorithm (SSGA) and a hybrid evolutionary algorithm with guided mutation (EA/G) for order acceptance and scheduling (OAS) problem in a single machine environment where orders are supposed to have release dates and sequence dependent setup times are incurred in switching from one order to next in the schedule. OAS problem is an NP-hard problem. We have compared our approaches with the state-of-the-art approaches reported in the literature. Computational results show the effectiveness of our approaches. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Steady-state genetic algorithm estimation of distribution algorithm Evolutionary algorithm Guided mutation Order acceptance and scheduling Single machine scheduling Sequence dependent setup time

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An evolutionary algorithm based hyper-heuristic framework for the set packing problem

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INFORMATION SCIENCES 2019年 505卷 1-31页

作者： Chaurasia, Sachchida Nand Kim, Joong Hoon Korea Univ Res Ctr Disaster Prevent Sci & Technol Seoul 02841 South Korea Queen Mary Univ London Sch Elect Engn & Comp Sci Operat Res Lab London EL 4FZ England Korea Univ Sch Civil Environm & Architectural Engn Seoul 02841 South Korea

In recent years, hyper-heuristics have received massive attention from the research community as an alternative of meta-heuristics. In a hyper-heuristic, generation or selection of an effective heuristic among a pool of heuristics is an important and challenging task in the search process. At each iteration, a suitable heuristic can take the search process toward the global optimal solution. Moreover, some additional factors such as quality and the number of heuristics also affect the performance. In this paper, we propose an evolutionary algorithm based hyper-heuristic framework that incorporates dynamic selection of parameters. To test its generality, effectiveness and robustness, we apply this approach on two different NP-hard problems - set packing problem (SPP) and minimum weight dominating set (MWDS) problem. The proposed approach for the SPP and the MWDS problem has been evaluated respectively on their respective set of benchmark instances. Computational results show that the proposed approach for the SPP and MWDS problem perform much better than their respective state-of-the-art approaches in terms of the solution quality and computational time. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Set packing problem estimation of distribution algorithm Guided-mutation Heuristic Hyper-heuristic Minimum weight dominating set problem

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Biomedical Classification Problems Automatically Solved by Computational Intelligence Methods

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IEEE ACCESS 2020年 8卷 101104-101117页

作者： Carlos Padierna, Luis Villasenor-Mora, Carlos Lopez Juarez, Silvia Alejandra Univ Guanajuato Div Ciencias & Ingn Campus Leon Leon 37150 Mexico

Biomedical classification problems are of great interest to both medical practitioners and computer scientists. Due to the harmful consequences of a wrong decision in this ambit, computational methods must be carefully designed to provide a reliable tool for helping physicians to obtain accurate predictions on unseen cases. Computational Intelligence (CI) provides robust models to perform optimization, classification and regression tasks. These models have been previously designed, mainly based on the expertise of computer scientists, to solve a vast number of biomedical problems. As the number of both CI algorithms and biomedical problems continues to grow, selecting the right method to solve a given problem becomes more challenging. To deal with this complexity, a systematic methodology for selecting a suitable model for a given classification problem is required. In this work, we review the more promising classification and optimization algorithms and reformulate them into a synergic framework to automatically design and optimize pattern classifiers. Our proposal, including state-of-the-art evolutionary algorithms and support vector machines, is tested on a variety of biomedical problems. Experimental results on benchmark datasets allow us to conclude that the automatically designed classifiers reach higher or equal performance than those designed by computer specialists.

关键词： Kernel Support vector machines Computational modeling Classification algorithms estimation Tuning Task analysis Biomedical classification problems estimation of distribution algorithm evolutionary algorithms genetic programming orthogonal polynomial kernels support vector machines

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The use of explicit building blocks in evolutionary computation

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2016年第3期47卷 691-706页

作者： Sangkavichitr, Chalermsub Chongstitvatana, Prabhas Chulalongkorn Univ Dept Comp Engn Bangkok Thailand

This paper proposes a new algorithm to identify and compose building blocks. Building blocks are interpreted as common subsequences between good individuals. The proposed algorithm can extract building blocks from a population explicitly. Explicit building blocks are identified from shared alleles among multiple chromosomes. These building blocks are stored in an archive. They are recombined to generate offspring. The additively decomposable problems and hierarchical decomposable problems are used to validate the algorithm. The results are compared with the Bayesian optimisation algorithm, the hierarchical Bayesian optimisation algorithm, and the chi-square matrix. This proposed algorithm is simple, effective, and fast. The experimental results confirm that building block identification is an important process that guides the recombination procedure to improve the solutions. In addition, the method efficiently solves hard problems.

关键词： linkage building block genetic algorithm multi-parent recombination estimation of distribution algorithm

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Data imputation for gas flow data in steel industry based on non-equal-length granules correlation coefficient

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INFORMATION SCIENCES 2016年 367卷 311-323页

作者： Lv, Zheng Zhao, Jun Liu, Ying Wang, Wei Dalian Univ Technol Sch Control Sci & Engn Dalian Liaoning Peoples R China

In the field of data-driven based modeling and optimization, the completeness and the accuracy of data samples are the foundations for further research tasks. Since the byproduct gas system of steel industry is rather complicated and its data-acquisition process might be frequently affected by the unexpected operational factors, the data-missing phenomenon usually occurs, which might lead to the failure of model establishment or inaccurate information discovery. In this study, a data imputation method based on the manufacturing characteristics is proposed for resolving the data-missing problem in steel industry. A novel correlation analysis, named by non-equal-length granules correlation coefficient (NGCC), is reported, and the corresponding model based on estimation of distribution algorithm (EDA) is established to study the correlation of the similar procedures. To verify the performance of the proposed method, this study considers three typical features of the gas flow data with different missing ratios. The experiment results indicate that it is greatly effective for the missing data imputation of byproduct gas, and exhibits better performance on the accuracy compared to the other methods. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Byproduct gas of steel industry Data imputation Non-equal-length granules correlation coefficient estimation of distribution algorithm

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Improved Mutual Information Maximizing Input Clustering algorithm for Permutation Flow Shop Scheduling Problem

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SENSOR LETTERS 2012年第8期10卷 1862-1866页

作者： Gao, Huimin Wang, Xiaoping Jiaxing Univ Mech & Elect Engn Coll Jiaxing 314001 Peoples R China Jiaxing Univ Sch Business Jiaxing 314001 Peoples R China

Improved Mutual Information Maximizing Input Clustering algorithm is a kind of discrete estimation of distribution algorithm, which is convenient to solve permutation flow shop scheduling problem. In this paper, the encoding mode and probability model are improved, new individual strategy is proposed, greedy algorithm is introduced at the initial phase of the probability matrix, and crossover operator, mutation operator, insert operator and swap operator are adopted during the process of evolution, dynamic adjusted method is employed to determine the population size. These improvements gurantee the population diversity even in small population. Experiment results show that the improved Mutual Information Maximizing Input Clustering algorithm is effective and stable.

关键词： Mutual Information Maximizing Input Clustering algorithm Permutation Flow Shop Scheduling Problem estimation of distribution algorithm Probability Matrix

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Approach to Hybrid Flow-Shop Scheduling Problem Based on Self-Guided Genetic algorithm

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2015年第3期19卷 365-371页

作者： Dai, Wen-Zhan Xia, Kai Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Zhejiang Peoples R China

The effective self-guided genetic algorithm (SGGA) which we proposed is based on the characteristics of a hybrid flow shop scheduling problem. A univariate probability model based on workpiece permutation is introduced together with a bivariate probability model based on a similar workpiece blocks. An approach to updating a probability model parameters is given based on superior individuals. A novel probability calculation function is proposed taking advantages of statistical learning information provided by univariate and bivariate probabilistic model to calculate the probability of workpieces located in different positions. A method for evaluating the quality of individual candidates generated by GA crossover and mutation operators is suggested for selecting promising and excellent individual candidates as offspring. Simulation results show that the SGGA has excellent performance and robustness.

关键词： hybrid flow shop scheduling problem genetic algorithm estimation of distribution algorithm bivariate probability model self-guided genetic algorithm

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Marginal distribution algorithm for Feature Model Test Configuration Generation

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第9期14卷 914-924页

作者： Sahid, Mohd Zanes Saringat, Mohd Zainuri Hamzah, Mohd Hamdi Irwan Zainal, Nurezayana Univ Tun Hussein Onn Malaysia UTHM Fac Comp Sci & Informat Technol Parit Raja Johor Malaysia

Generating test configuration for Software Product Line (SPL) is difficult, due to the exponential effect of feature combination. Pairwise testing can generate test input for a single software product that deviates from exhaustive testing, nevertheless proven to be effective. In the context of SPL testing, to generate minimal test configuration that maximizes pairwise coverage is not trivial, especially when dealing with a huge number of features and when constraints must be satisfied, which is the case in most SPL systems. In this paper, we propose an estimation of distribution algorithm, based on pairwise testing, to alleviate this problem. Comparisons are made against a greedy based and a constraint handling based approach. The experiments demonstrate the feasibility of the proposed algorithm, such that it achieves better test configurations dissimilarity and at the same time maintain the test configuration size and pairwise coverage. This is supported by analysis using descriptive statistics.

关键词： estimation of distribution algorithm marginal distribution algorithm test configuration generation pairwise testing software product line

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Smart grid planning method based on multi-objective particle swarm optimisation algorithm

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2021年第1期13卷 22-31页

作者： Zhang, Jianguang State Grid Shaoxing Power Supply Co Shaoxing 312000 Zhejiang Peoples R China

Smart grid refers to a modern electric energy supply system to tackle a lot of problems in grid management, such as, resource shortage, environment pollution and so on. In this paper, we propose a novel smart grid planning method using multi-objective particle swarm optimisation algorithm. The goal of smart grid plan is to calculate the minimum investment and annual operating costs, when we obtain the planning level of load distribution, substation capacity and power supply area to satisfy the load requirement and optimised substation location. Afterwards, we propose a multi-objective particle swarm optimisation algorithm which integrates the estimation of distribution algorithm. Furthermore, the propose approach divides the particle population into a lot of sub-populations and then build probability models for each population. Finally, experimental results demonstrate that the proposed method can effectively arrange new substation, which is able to make up for deficiencies of current existing substations.

关键词： smart grid planning multi-objective optimisation particle swarm optimisation estimation of distribution algorithm

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Fast Solutions Enhancing using a Copula-based EDA and SVM for many-objective problems

Fast Solutions Enhancing using a Copula-based EDA and SVM fo...

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8th IFAC Conference on Manufacturing Modelling, Management and Control (MIM)

作者： Cheriet, Abdelhakim Cherif, Foudil Taleb-Ahmed, Abdelmalik Univ Biskra LESIA Lab Biskra Algeria LAMIH Dept Elect Engn Valencienneses France

In this paper we propose a new Copula-based estimation of distribution algorithm, to solve Many-objective optimization problem and to get new optimal solutions in very court time. Our algorithm uses the proprieties of Copula and exploits their statistical properties to make new solutions using the founded optimal solutions through the estimation of their distribution. The first step of the proposed Copula-based estimation of distribution algorithm (CEDA-SVM) is taking initial solutions offered by any MOEA (Multi Objective Evolutionary algorithm), and then creates Copulas to estimate their distribution, and we use Support Vector Machine (SVM) to learn the Pareto solutions model;those Copulas will be used to generate new solutions and SVM to avoid the expensive function evaluations. The idea of using the estimated distribution of the optimal solutions helps CEDA-SVM to avoid running the optimizer (MOEA) every time we need new alternatives solutions when the founded ones are not satisfactory. We tested CEDA-SVM on a set of many-objective benchmark problems traditionally used by the community, namely DTLZ (1, 2, 3, and 4) with different dimensions (3, 5, 8, 10, and 15). We used CEDA along with MOEA/D-Schy and MOEA/D-BI as two examples of MOEA thus resulting in two variants CEDA-MOAE/D-Sey and CEDA-MOEA/D-BI and compare them with MOEA/D-Schy and MOEA/D-BI. The results of our experiments show that, with both variants of CEDA-SVM, new solutions can be obtained in a very small time compared to the other algorithms. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Multiobjective optimization many-objective optimization estimation of distribution algorithm SVM Copula

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