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Genetic and evolutionary Computation Conference (GECCO)

作者： da Silva, Diego Fernandes Okada, Luiz Fernando Colanzi, Thelma Elita Assuncao, Wesley K. G. Univ Estadual Maringa Maringa Parana Brazil Univ Tecnol Fed Parana Toledo Parana Brazil

ISBN: (纸本)9781450371285

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line. PLA designing has been formulated as a multi-objective optimization problem and successfully solved by a state-of-the-art search-based approach. However, the majority of empirical studies optimize PLA designs without applying one of the fundamental genetic operators: the crossover. An operator for PLA design, named Feature-driven Crossover, was proposed in a previous study. In spite of the promising results, this operator occasionally generated incomplete solutions. To overcome these limitations, this paper aims to enhance the search-based PLA design optimization by improving the Feature-driven Crossover and introducing a novel crossover operator specific for PLA design. The proposed operators were evaluated in two well-studied PLA designs, using three experimental configurations of NSGA-II in comparison with a baseline that uses only mutation operators. Empirical results show the usefulness and efficiency of the presented operators on reaching consistent solutions. We also observed that the two operators complement each other, leading to PLA design solutions with better feature modularization than the baseline experiment.

关键词： multi-objective evolutionary algorithm software product line software architecture recombination operators

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multi-objective Ant Colony algorithm Based on Pheromone Weight 16

Multi-objective Ant Colony Algorithm Based on Pheromone Weig...

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16th International Conference on Computational Intelligence and Security (CIS)

作者： Yang, Lei Jia, Xiaotian Liu, Ganming South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China

ISBN: (纸本)9781665404457

This paper proposed multi-objective ant colony algorithm based on pheromone weight, which is used to solve multi-objective optimization problems. The algorithm introduces the weight of distance-related in the initialization of pheromones, which is beneficial to the ant speed up the path selection, improving the efficiency of ant search. At the same time, the adaptive variation operator that dynamically adjusts the number of ant neighbors with the number of iterations and the weight Tchebycheff aggregation method are also introduced, which are beneficial to improve the convergence speed and the quality of the algorithm. The algorithm has been compared with other related algorithms using Hypervolume and other indicators in the standard dual Traveling Salesman Problem (TSP), and has been proven that the improved algorithm has better results.

关键词： multi-objective evolutionary algorithm Ant colony optimization Hypervolume index

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multi-objective Magnitude-Based Pruning for Latency-Aware Deep Neural Network Compression 16th

Multi-objective Magnitude-Based Pruning for Latency-Aware De...

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16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Hong, Wenjing Yang, Peng Wang, Yiwen Tang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Univ Sci & Technol China Dept Management Sci Hefei 230027 Peoples R China Guangdong Hong Kong Macao Greater Bay Area Ctr Br Guangzhou 510515 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Dept Chem & Biol Engn Hong Kong Peoples R China

ISBN: (纸本)9783030581114;9783030581121

Layer-wise magnitude-based pruning is a popular method for Deep Neural Network (DNN) compression. It has the potential to reduce the latency for an inference made by a DNN by pruning connects in the network, which prompts the application of DNNs to tasks with real-time operation requirements, such as self-driving vehicles, video detection and tracking. However, previous methods mainly use the compression rate as a proxy for the latency, without explicitly accounting for latency in the training of the compressed network. This paper presents a new layer-wise magnitude-based pruning method, namely multi-objective Magnitude-based Latency-Aware Pruning (MMLAP). MMLAP captures latency directly and incorporates a novel multi-objective evolutionary algorithm to optimize both accuracy of a DNN and its latency efficiency when designing compressed networks, i.e., when tuning hyper-parameters of LMP. Empirical studies show the competitiveness of MMLAP compared to well-established LMP methods and show the value of multi-objective optimization in yielding Pareto-optimal compressed networks in terms of accuracy and latency.

关键词： Compression Magnitude-based pruning Latency-aware multi-objective optimization multi-objective evolutionary algorithm

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Proposal of a Realistic Many-objective Test Suite 16th

Proposal of a Realistic Many-Objective Test Suite

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16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Chen, Weiyu Ishibuchi, Hisao Shang, Ke Southern Univ Sci & Technol SUSTech Dept Comp Sci & Engn Univ Key Lab Evolving Intelligent Syst Guangdong Shenzhen Key Lab Computat Intelligence Shenzhen Guangdong Peoples R China

ISBN: (纸本)9783030581114;9783030581121

Real-world many-objective optimization problems are not always available as easy-to-use test problems. For example, their true Pareto fronts are usually unknown, and they are not scalable with respect to the number of objectives or decision variables. Thus, the performance of existing multi-objective evolutionary algorithms is always evaluated using artificial test problems. However, there exist some differences between frequently-used many-objective test problems and real-world problems. In this paper, first we clearly point out one difference with respect to the effect of changing the value of each decision variable on the location of the objective vector in the objective space. Next, to make artificial test problems more realistic, we introduce a coefficient matrix to their problem structure. Then, we demonstrate that a different coefficient matrix leads to a different difficulty of a test problem. Finally, based on these observations, we propose a realistic many-objective test suite using various coefficient matrices.

关键词： Many-objective optimization multi-objective evolutionary algorithm Many-objective test suite Coefficient matrix

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Use of Energy-Based Domain Knowledge as Feedback to evolutionary algorithms for the Optimization of Water Distribution Networks

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WATER 2020年第11期12卷 3101-3101页

作者： Paez, Diego Salcedo, Camilo Garzon, Alexander Gonzalez, Maria Alejandra Saldarriaga, Juan Queens Univ Dept Civil Engn Kingston ON K7L 3N6 Canada Univ Arizona Dept Civil & Architectural Engn & Mech Tucson AZ 85721 USA Univ Andes Water Supply & Sewer Syst Res Ctr CIACUA Bogota 111711 Colombia Univ Andes Dept Civil & Environm Engn Bogota 111711 Colombia

The optimization of water distribution networks (WDN) has evolved, requiring approaches that seek to reduce capital costs and maximize the reliability of the system simultaneously. Hence, several evolutionary algorithms, such as the non-dominated sorting-based multi-objective evolutionary algorithm (NSGA-II), have been widely used despite the high computational costs required to achieve an acceptable solution. Alternatively, energy-based methods have been used to reach near-optimal solutions with reduced computational requirements. This paper presents a method to combine the domain knowledge given by energy-based methods with an evolutionary algorithm, in a way that improves the convergence rate and reduces the overall computational requirements to find near-optimal Pareto fronts (PFs). This method is divided into three steps: parameters calibration, preprocessing of the optimal power use surface (OPUS) results, and periodic feedback using OPUS in NSGA II. The method was tested in four benchmark networks with different characteristics, seeking to minimize the costs of the WDN and maximizing its reliability. Then the results were compared with a generic implementation of NSGA-II, and the performance and quality of the solutions were evaluated using two metrics: hypervolume (HV) and modified inverted generational distance (IGD+). The results showed that the feedback procedure increases the efficiency of the algorithm, particularly the first time the algorithm is retrofitted.

关键词： multi-objective evolutionary algorithm NSGA-II optimal power use surface (OPUS) hypervolume IGD plus domain knowledge hot start

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Effectiveness of NSGA-II with Linearly Scheduled Pareto-Partial Dominance for Practical Many-Objecitve Nurse Scheduling 7

Effectiveness of NSGA-II with Linearly Scheduled Pareto-Part...

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7th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Ohki, Makoto Tottori Univ Fac Engn 1014 Koyama Minami Tottori 6800945 Japan

ISBN: (纸本)9781728159539

This paper describes an application of NSGA-II as one of multi-objective evolutionary algorithms (MOEAs) to a Many-objective Nurse Scheduling in an actual hospitals in Japan and its effectiveness. Although many techniques for the actual nurse scheduling have been poposed, they are based on the culture of work styles in Europe or in the US, and then they are not fitted for creating a nurse work schedule in Japan. The nurse scheduling problem has many objectives, twelve objectives specially in the problem shown in this paper. Such an optimization problem having many objectives is generally called a Many-objective Optimization Problem (MaOP), and it is considered that MOEAs such as NSGA-II are not effective. Although MOEA/D and NSGA-III, which are one of MaOEA, are known as effective algorithms for MaOPs, these algorithms, for example, require an so many number of scalarization vectors or appropriate reference set, they are not always easy to apply to real world problems. The MaOEAs are also very sensitive techniques to the vectors or reference set. On the other hand, although it has been pointed out that MOEAs are not suitable for MaOP in verification reports with several benchmarks, there is no fact that MOEAs have been applied to real-world MaOPs and their effectiveness has been denied. Therefore, this paper tries to apply NSGA-II, one of MOEAs, to the practical nurse scheduling problem without omitting or reducing all the objectives, and verify its effectiveness.

关键词： nurse scheduling multi-objective evolutionary algorithm many-objective optimization problem NSGA-II

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An evolutionary algorithm-Based Text Categorization Technique 4th

An Evolutionary Algorithm-Based Text Categorization Techniqu...

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4th International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Das, Ajit Kumar Das, Asit Kumar Sarkar, Apurba Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Howrah 711103 W Bengal India

ISBN: (纸本)9789811080555;9789811080548

In general, most of the organizations generate unstructured data from which extraction of meaningful information becomes a difficult task. Preprocessing of unstructured data before mining helps to improve the efficiency of the mining algorithms. In this paper, text data is initially preprocessed using tokenization, stop word removal, and stemming operations and a bag-of-words is identified to characterize the text dataset. Next, improved strength pareto evolutionary algorithm-based genetic algorithm is applied to determine the more compact set of informative words for clustering of text documents efficiently. It is a bi-objective genetic algorithm used to approximate the pareto-optimal front exploring the search space for optimal solution. The external clustering index and number of words described in the documents are considered as two objective functions of the algorithm, and based on these functions chromosomes in the population are evaluated and the best chromosome in non dominated pareto front of final population gives the optimal set of words sufficient for categorizartion of text dataset.

关键词： Text mining Feature selection Text clustering Cluster validation multi-objective evolutionary algorithm

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The stability optimization of the international lithium trade

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RESOURCES POLICY 2021年 74卷 102336-102336页

作者： Wu, Congcong Gao, Xiangyun Xi, Xian Zhao, Yiran Li, Yu China Univ Geosci Sch Econ & Management Beijing 100083 Peoples R China Minist Nat Resources Key Lab Carrying Capac Assessment Resource & Envi Beijing 100083 Peoples R China Hebei GEO Univ Coll Informat Engn Shijiazhuang 050031 Hebei Peoples R China

The international mineral resources trade is an important way to correct the unbalanced distribution of global mineral resources. This paper proposes a bi-objective model to improve the stability of the mineral resource trade by optimizing the trade structure. The improved nondominated sorting genetic algorithm (NSGA-II) was used to solve the bi-objective optimization model, and a more robust trade structure was obtained. We applied the model to the optimization of the global lithium carbonate trade. The optimized results enabled us to identify the key trade relationships that play the most critical role in improving the stability of lithium carbonate trade. The effectiveness of the optimization model was proved by analyzing the stability of major net importing countries and the influence of major net exporting countries. Furthermore, the strongest destructive model, which was opposite to the optimal model, was used to analyze the core trade relations and core countries in the actual lithium trade. These core trade relations provide a foundation for maintaining the stability of the international trade. Our work provides new ideas for improving the stability of the global mineral trade and provides a new perspective and special basis for trading countries to choose trade partners.

关键词： Trade stability Complex network Trade structural optimization multi-objective evolutionary algorithm Lithium

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evolutionary algorithms for multi-objective dual-resource constrained flexible job-shop scheduling problem

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OPSEARCH 2019年第3期56卷 983-1006页

作者： Yazdani, M. Zandieh, M. Tavakkoli-Moghaddam, R. Islamic Azad Univ Qazvin Branch Fac Ind & Mech Engn Dept Ind Engn Qazvin Iran Shahid Beheshti Univ GC Management & Accounting Fac Dept Ind Management Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

This paper presents a multi-objective dual-resource constrained flexible job-shop scheduling problem (MODRCFJSP) with the objectives of minimizing the makespan, critical machine workload and total workload of machines simultaneously. Two types of multi-objective evolutionary algorithms including fast elitist non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are proposed for solving MODRCFJSP. Some efficient mutation and crossover operators are adapted to the special chromosome structure of the problem for producing new solutions in the algorithm's generations. Besides, we provide controlled elitism based version of NSGA-II and NRGA, namely controlled elitist NSGA-II (CENSGA-II) and controlled elitist NRGA (CENRGA), to optimize MODRCFJSP. To show the performance of the four proposed algorithms, numerical experiments with randomly generated test problems are used. Moreover, different convergence and diversity performance metrics are employed to illustrate the relative performance of the presented algorithms.

关键词： Scheduling Flexible job-shop Dual-resource constrained multi-objective optimization multi-objective evolutionary algorithm Controlled elitism procedure

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Fuzzy multi-objective portfolio model based on semi-variance-semi-absolute deviation risk measures

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SOFT COMPUTING 2019年第17期23卷 8159-8179页

作者： Yue, Wei Wang, Yuping Xuan, Hejun Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Xian Polytech Univ Sch Sci Xian 710071 Shaanxi Peoples R China

The risk measure plays an important role for portfolio selection problem. The lower partial risk (downside risk) measures have been considered to be more in line with investor's attitude toward risk. The purpose of this paper is to construct a portfolio selection model in the lower partial risk framework. First, semi-variance and semi-absolute deviation risk measures are used as double-risk measures simultaneously, which can overcome the shortcomings of both semi-variance risk measure and semi-absolute deviation risk measure and can provide additional strengths and flexibility. Second, to address a real portfolio selection problem, by considering the transaction cost and liquidity of portfolio, and regarding the returns of risk assets as LR-type fuzzy variables, we present a fuzzy multi-objective portfolio selection model. Third, in order to limit the proportion of the portfolio invested in assets with common characteristics and to avoid very small holdings, the quantity and class constraints are introduced into the model. Finally, to solve the proposed model efficiently, a new multi-objective evolutionary algorithm is designed. Furthermore, some experiments are conducted by using the data of Shanghai Stock Exchange, and the results indicate the efficiency and effectiveness of the proposed model and algorithm.

关键词： Portfolio selection The lower partial risk measures LR-fuzzy variable Possibilistic moments multi-objective evolutionary algorithm

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