In this paper, we design a set of multi-objective constrained optimization problems (MCOPs) and propose a new repair operator to address them. The proposed repair operator is used to fix the solutions that violate the...
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ISBN:
(纸本)9781467376792
In this paper, we design a set of multi-objective constrained optimization problems (MCOPs) and propose a new repair operator to address them. The proposed repair operator is used to fix the solutions that violate the box constraints. More specifically, it employs a reversed correction strategy that can effectively avoid the population falling into local optimum. In addition, we integrate the proposed repair operator into two classical multi-objective evolutionary algorithms MOEA/D and NSGA-II. The proposed repair operator is compared with other two kinds of commonly used repair operators on benchmark problems CTPs and MCOPs. The experiment results demonstrate that our proposed approach is very effective in terms of convergence and diversity.
The purpose of this paper is to investigate a multi-objective evolutionary algorithm (MOEA) for optimizing neural ensemble classifiers. This paper provides an automatic procedure based on MOEA to identify the best acc...
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ISBN:
(纸本)9781479952557
The purpose of this paper is to investigate a multi-objective evolutionary algorithm (MOEA) for optimizing neural ensemble classifiers. This paper provides an automatic procedure based on MOEA to identify the best accuracy and diversity. A MOEA is used to search for the combination of layers and clusters in ensemble classifiers to obtain the non-dominated set of accuracy and diversity. The experiments were conducted on UCI machine learning benchmark datasets using the MOEA and also single objectiveevolutionaryalgorithms. The detailed results and analysis show that MOEA has improved the performance of ensemble classifier and obtained better accuracy compared to recently published approaches.
This study has demonstrated a design tool for oil spill detection in COSMO-SkyMed satellite data using multi-objective evolutionary algorithm which based on Pareto optimal solutions. The COSMO-SkyMed along the Gulf of...
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ISBN:
(纸本)9783319091532
This study has demonstrated a design tool for oil spill detection in COSMO-SkyMed satellite data using multi-objective evolutionary algorithm which based on Pareto optimal solutions. The COSMO-SkyMed along the Gulf of Thailand is involved in this study. The study also shows that multi-objective evolutionary algorithm provides an accurate pattern of oil slick in COSMO-SkyMed data. This shown by 96% for oil spill, 1% look-alike and 3% for sea roughness using the receiver -operational characteristics (ROC) curve. The MOGA also shows excellent performance in COSMO-SkyMed data. In conclusion, multi-objective evolutionary algorithm can be used as an automatic detection tool for oil spill in COSMO-SkyMed satellite data.
multi-objective Fuzzy Flexible Jobshop Scheduling Problems (MFFJSPs) have garnered widespread attention since they are able to handle the uncertainty of processing time in actual production. Nevertheless, making a goo...
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multi-objective Fuzzy Flexible Jobshop Scheduling Problems (MFFJSPs) have garnered widespread attention since they are able to handle the uncertainty of processing time in actual production. Nevertheless, making a good balance between the diversity and convergence of non-dominated solutions is a challenging issue that cannot be overlooked when MFFJSP is solved. To deal with these issues, this work proposes a Dynamic Quadratic Decomposition-based multi-objective evolutionary algorithm (DQD-MOEA) to solve MFFJSP by minimizing makespan and total machine workload. To solve a problem that the distribution and diversity of searched non-dominant solutions are poor due to the discrete decision space and objective space of MFFJSP, it proposes a dynamic quadratic decomposition method. Its core idea is to eliminate all the failed reference vectors because they have no intersection with areal Pareto front, and ensure that solutions evolve along effective reference vectors. This work also introduces a problem-specific local search method to accelerate the solution convergence for MFFJSP. It proposes a hybrid initialization method to improve the quality of initial solutions. Finally, a series of experiments are performed and the results demonstrate that DQD-MOEA is significantly better than state-of-the-art algorithms in terms of convergence and solution diversity when solving widely-tested benchmark cases.
Association rule mining (ARM) is a widely used technique in data mining for pattern discovery. However, association rule mining in numerical data poses a considerable challenge. In recent years, researchers have turne...
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Association rule mining (ARM) is a widely used technique in data mining for pattern discovery. However, association rule mining in numerical data poses a considerable challenge. In recent years, researchers have turned to optimization-based approaches as a potential solution. One particular area of interest in numerical association rules mining (NARM) is controlling the length of itemset intervals. In this paper, we propose a novel evolutionaryalgorithm based on the multi-objective firefly algorithm for efficiently mining numerical association rules (MOFNAR). MOFNAR utilizes Balance, square of cosine (SOC) and comprehensibility as objectives of evolutionaryalgorithm to assess rules and achieve a rule set that is both simple and accurate. We introduce the Balance measure to effectively control the intervals of numerical itemsets and eliminate misleading rules. Furthermore, we suggest a penalty approach, and the crowding-distance method is employed to maintain high diversity. Experimental results on five well-known datasets show the effectiveness of our method in discovering a simple rule set with high confidence that covers a significant percentage of the data.
The vast search space in large-scale multi-objective optimization represents a significant challenge for evolutionaryalgorithms to converge towards the Pareto Front. As an effective search strategy, direction-guided ...
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The vast search space in large-scale multi-objective optimization represents a significant challenge for evolutionaryalgorithms to converge towards the Pareto Front. As an effective search strategy, direction-guided sampling technique could improve the search efficiency by exploring along the approximated directions to approach the Pareto set. However, the approximated directions may fail to interact with the true Pareto set and result in inefficient search. To address this issue, a dual-sampling method is proposed in this paper. In addition to the samples along the directions approximated by direction-guided sampling, fuzzy Gaussian sampling is applied to adjust the search direction and generate more accurate and evenly distributed solutions. Moreover, a convergence-and-diversity-based mating selection is introduced to balance the exploration and exploitation. The experiments on 72 test benchmarks with bi- and tri-objectives and 500-5000 decision variables show the superiority of the proposed algorithm compare with the state-of-the-art algorithms.
In this paper, we propose a new framework hybridizing a Support Vector Machine (SVM), a multi-objective Genetic algorithm (MOGA) and a Locality Sensitive Hashing (LSH). The goal is to tackle fine-grained classificatio...
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ISBN:
(纸本)9781479975617
In this paper, we propose a new framework hybridizing a Support Vector Machine (SVM), a multi-objective Genetic algorithm (MOGA) and a Locality Sensitive Hashing (LSH). The goal is to tackle fine-grained classification challenges which means classifying many classes with high similarities between classes and poor similarities inside one class. SVM is used for its ability to learn multi-class problems from very few training data. MOGA is used to optimize training samples used by the SVM so as to improve its learning rate. As data define a discrete set of instances and not a continuous solution space, LSH is used to map "optimal solutions" obtained by MOGA onto the closest real instances contained in the dataset. We evaluate our method for content-based image classification on the standard image database Caltech256 (i.e. 30000 images distributed in 256 classes). Experiments show that our method outperforms state-of-the-art approaches.
Some researchers have framed the extraction of association rules as a multi-objective problem, jointly optimizing several measures to obtain a set with more interesting and accurate rules. In this paper, we propose a ...
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Some researchers have framed the extraction of association rules as a multi-objective problem, jointly optimizing several measures to obtain a set with more interesting and accurate rules. In this paper, we propose a new multi-objectiveevolutionary model which maximizes the comprehensibility, interestingness and performance of the objectives in order to mine a set of quantitative association rules with a good trade-off between interpretability and accuracy. To accomplish this, the model extends the well-known multi-objective evolutionary algorithm Non-dominated Sorting Genetic algorithm II to perform an evolutionary learning of the intervals of the attributes and a condition selection for each rule. Moreover, this proposal introduces an external population and a restarting process to the evolutionary model in order to store all the nondominated rules found and improve the diversity of the rule set obtained. The results obtained over real-world datasets demonstrate the effectiveness of the proposed approach. (C) 2013 Elsevier Inc. All rights reserved.
Previous theoretical analyses of evolutionarymulti-objective optimization (EMO) mostly focus on obtaining epsilon-approximations of Pareto fronts. However, in practical applications, an appropriate value of epsilon i...
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Previous theoretical analyses of evolutionarymulti-objective optimization (EMO) mostly focus on obtaining epsilon-approximations of Pareto fronts. However, in practical applications, an appropriate value of epsilon is critical but sometimes, for a multi-objective optimization problem (MOP) with unknown attributes, difficult to determine. In this paper, we propose a new definition for the finite representation of the Pareto front the adaptive Pareto front, which can automatically accommodate the Pareto front. Accordingly, it is more practical to take the adaptive Pareto front, or its epsilon-approximation (termed the epsilon-adaptive Pareto front) as the goal of an EMO algorithm. We then perform a runtime analysis of a (mu + 1) multiobjectiveevolutionaryalgorithm ((mu + 1) MOEA) for three MOPs, including a discrete MOP with a polynomial Pareto front (denoted as a polynomial DMOP), a discrete MOP with an exponential Pareto front (denoted as an exponential DMOP) and a simple continuous two-objective optimization problem (SCTOP). By employing an estimator-based update strategy in the (mu + 1) MOEA, we show that (1) for the polynomial DMOP, the whole Pareto front can be obtained in the expected polynomial runtime by setting the population size mu equal to the number of Pareto vectors;(2) for the exponential DMOP, the expected polynomial runtime can be obtained by keeping mu increasing in the same order as that of the problem size n;and (3) the diversity mechanism guarantees that in the expected polynomial runtime the MOEA can obtain an epsilon-adaptive Pareto front of SCTOP for any given precision epsilon. Theoretical studies and numerical comparisons with NSGA-II demonstrate the efficiency of the proposed MOEA and should be viewed as an important step toward understanding the mechanisms of MOEAs. (C) 2013 Elsevier Inc. All rights reserved.
Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied the ext...
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Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied the extended Markowitz's mean-variance portfolio optimization model. We considered the cardinality, quantity, pre-assignment and round lot constraints in the extended model. These four real-world constraints limit the number of assets in a portfolio, restrict the minimum and maximum proportions of assets held in the portfolio, require some specific assets to be included in the portfolio and require to invest the assets in units of a certain size respectively. An efficient learning-guided hybrid multi-objective evolutionary algorithm is proposed to solve the constrained portfolio optimization problem in the extended mean-variance framework. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote the efficient convergence by guiding the evolutionary search towards the promising regions of the search space. The proposed algorithm is compared against four existing state-of- the-art multi-objective evolutionary algorithms, namely Non-dominated Sorting Genetic algorithm ( NSGA-II), Strength Pareto evolutionaryalgorithm (SPEA-2), Pareto Envelope-based Selection algorithm (PESA-II) and Pareto Archived Evolution Strategy (PAES). Computational results are reported for publicly available OR-library datasets from seven market indices involving up to 1318 assets. Experimental results on the constrained portfolio optimization problem demonstrate that the proposed algorithm significantly outperforms the four well-known multi-objective evolutionary algorithms with respect to the quality of obtained efficient frontier in the conducted experiments. (C) 2014 The Authors. Published by Elsevier B.V.
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