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multi-objective evolutionary emergency response optimization for major accidents

JOURNAL OF HAZARDOUS MATERIALS 2010年第1-3期178卷 792-803页

作者： Georgiadou, Paraskevi S. Papazoglou, Ioannis A. Kiranoudis, Chris T. Markatos, Nikolaos C. Natl Tech Univ Athens Sch Chem Engn Athens 15780 Greece Natl Ctr Sci Res Demokritos Syst Reliabil & Ind Safety Lab Athens 15310 Greece

Emergency response planning in case of a major accident (hazardous material event, nuclear accident) is very important for the protection of the public and workers' safety and health. In this context, several protective actions can be performed, such as, evacuation of an area: protection of the population in buildings: and use of personal protective equipment. The best solution is not unique when multiple criteria are taken into consideration (e.g. health consequences, social disruption, economic cost). This paper presents a methodology for multi-objective optimization of emergency response planning in case of a major accident. The emergency policy with regards to protective actions to be implemented is optimized. An evolutionary algorithm has been used as the optimization tool. Case studies demonstrating the methodology and its application in emergency response decision-making in case of accidents related to hazardous materials installations are presented. However, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency response procedures in other cases (nuclear accidents, transportation of hazardous materials) or for land-use planning issues. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Emergency planning Major accident Evacuation multi-objective evolutionary algorithm Shelter-in-place

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Selection methods and diversity preservation in many-objective evolutionary algorithms

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DATA TECHNOLOGIES AND APPLICATIONS 2018年第4期52卷 502-519页

作者： Marti, Luis Segredo, Eduardo Sanchez-Pi, Nayat Hart, Emma Univ Fed Fluminense Inst Comp Niteroi RJ Brazil Edinburgh Napier Univ Sch Comp Edinburgh Midlothian Scotland Univ La Laguna Dept Ingn Informat & Sistemas San Cristobal De La Lagu Spain Univ Estado Rio de Janeiro Rio De Janeiro Brazil

Purpose - One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. The paper aims to discuss these issues. Design/methodology/approach - Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings - The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic algorithm III and a modified version of the Non-dominated Sorting Genetic algorithm II, where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value - The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of the knowledge, it has not been investigated yet.

关键词： Convergence Diversity preservation Many-objective optimisation multi-objective evolutionary algorithm Selection mechanism Walking Fish Group (WFG) test suite

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Convex hull-based multi-objective evolutionary computation for maximizing receiver operating characteristics performance

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MEMETIC COMPUTING 2016年第1期8卷 35-44页

作者： Hong, Wenjing Tang, Ke Univ Sci & Technol China Sch Comp Sci & Technol USTC Birmingham Joint Res Inst Intelligent Comput Hefei 230026 Peoples R China

The receiver operating characteristics (ROC) analysis has gained increasing popularity for analyzing the performance of classifiers. In particular, maximizing the convex hull of a set of classifiers in the ROC space, namely ROCCH maximization, is becoming an increasingly important problem. In this work, a new convex hull-based evolutionary multi-objective algorithm named ETriCM is proposed for evolving neural networks with respect to ROCCH maximization. Specially, convex hull-based sorting with convex hull of individual minima (CH-CHIM-sorting) and extreme area extraction selection (EAE-selection) are proposed as a novel selection operator. Empirical studies on 7 high-dimensional and imbalanced datasets show that ETriCM outperforms various state-of-the-art algorithms including convex hull-based evolutionary multi-objective algorithm (CH-EMOA) and non-dominated sorting genetic algorithm II (NSGA-II).

关键词： ROC convex hull multi-objective evolutionary algorithm evolutionary neural networks Selection operator

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Novel multi objective evolutionary framework for solving next release problem

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期44卷 3315-3339页

作者： Ghasemi, Mohsen Bagherifard, Karamollah Parvin, Hamid Nejatian, Samad Islamic Azad Univ Dept Comp Engn Yasooj Branch Yasuj Iran Islamic Azad Univ Dept Comp Engn Nourabad Mamasani Branch Nourabad Mamasani Iran Islamic Azad Univ Dept Elect Engn Yasooj Branch Yasuj Iran

Software developers want to meet the requirements of customers in next versions. Choosing which set of requirements can be done according to cost and time is an NP-hard problem known as Next Release Problem (NRP). In this article, a multi objective evolutionary algorithm (MOEA) framework is proposed to solve NRP. The framework applies the nonrepetitive population, integrates solutions and external repository. Furthermore, a novel approach is implemented to satisfy the constraints of the problem. In this framework, six evolutionary algorithms are implemented and using seven quality indicators, the achieved results of that algorithms are compared with the original versions of same algorithms. Through using HV (the ratio of the region covered by Pareto Front) and NDS (the number of solutions in the Pareto Front) metrics, the effects of the proposed algorithms are compared with other works' results. The efficacy of the proposed MOEA framework is measured using three real world datasets. The gained results represent that the implemented algorithms perform better than other related algorithms previously published.

关键词： Next release problem multi-objective evolutionary algorithm search-based software engineering teaching learning based optimization non-repetitive population

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Using an evolutionary algorithm to optimize the broadcasting methods in mobile ad hoc networks

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2011年第6期34卷 1794-1804页

作者： Abdou, Wahabou Henriet, Adrien Bloch, Christelle Dhoutaut, Dominique Charlet, Damien Spies, Francois Univ Franche Comte Lab Informat F-25201 Montbeliard France

A mobile ad hoc network (MANET) is a collection of mobile nodes communicating through wireless connections without any prior network infrastructure. In such a network the broadcasting methods are widely used for sending safety messages and routing information. To transmit a broadcast message effectively in a wide and high mobility MANET (for instance in vehicular ad hoc network) is a hard task to achieve. An efficient communication algorithm must take into account several aspects like the neighborhood density, the size and shape of the network, the use of the channel. Probabilistic strategies are often used because they do not involve additional latency. Some solutions have been proposed to make their parameters vary dynamically. For instance, the retransmission probability increases when the number of neighbors decreases. But, the authors do not optimize parameters for various environments. This article aims at determining the best communication strategies for each node according to its neighborhood density. It describes a tool combining a network simulator (ns-2) and an evolutionary algorithm (EA). Five types of context are considered. For each of them, we tackle the best behavior for each node to determine the right input parameters. The proposed EA is first compared to three EAs found in the literature: two well-known EAs (NSGA-II and SPEA2) and a more recent one (DECMOSA-SQP). Then, it is applied to the MANET broadcasting problem. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithm MANET VANET Broadcast Flooding Simulation Channel saturation

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A multi-objective evolutionary optimization approach for an integrated location-inventory distribution network problem under vendor-managed inventory systems

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ANNALS OF OPERATIONS RESEARCH 2011年第1期186卷 213-229页

作者： Liao, Shu-Hsien Hsieh, Chia-Lin Lin, Yu-Siang Tamkang Univ Dept Management Sci & Decis Making Taipei 251 Taiwan Natl Taiwan Univ Sci & Technol Taiwan Tech Dept Ind Management Taipei 106 Taiwan

In this paper, we propose an integrated model to incorporate inventory control decisions-such as economic order quantity, safety stock and inventory replenishment decisions-into typical facility location models, which are used to solve the distribution network design problem. A simultaneous model is developed considering a stochastic demand, modeling also the risk poling phenomenon. multi-objective decision analysis is adopted to allow use of a performance measurement system that includes cost, customer service levels (fill rates), and flexibility (responsive level). This measurement system provides more comprehensive measurement of supply chain system performance than do traditional, single measure approaches. A multi-objective location-inventory model which permits a comprehensive trade-off evaluation for multi-objective optimization is initially presented. More specifically, a multiobjective evolutionary algorithm is developed to determine the optimal facility location portfolio and inventory control parameters in order to reach best compromise of these conflicting criteria. An experimental study using practical data was then illustrated for the possibility of the proposed approach. Computational results have presented promising solutions in solving a practical-size problem with 50 buyers and 15 potential DCs and proved to be an innovative and efficient approach for so called difficult-to-solve problems.

关键词： Supply chain integration Vendor-managed inventory multi-objective supply chain model multi-objective evolutionary algorithm

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multi-objective evolutionary design of central pattern generator network for biomimetic robotic fish

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COMPLEX & INTELLIGENT SYSTEMS 2023年第2期9卷 1707-1727页

作者： Li, Wei Kun Chen, Hao Cui, Wei Cheng Song, Chang Hui Chen, Lin Ke Westlake Inst Adv Study Inst Adv Technol 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China Zhejiang Univ Zhejiang Univ Westlake Univ Joint Training Hangzhou 310024 Peoples R China Westlake Univ Sch Engn 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China Key Lab Coastal Environm & Resources Zhejiang Pro 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China

Fish-inspired motion is an important research area with many applications in real-world tasks such as underwater vehicles or robotic fish control design. Owing to robust, smooth, and coordinated oscillatory signals generated by Central Pattern Generators (CPGs) for locomotion control of robots with multiple degrees of freedom, CPGs are the most versatile solution for robotic control systems, especially in robotic fish. However, tuning central pattern generator parameters is difficult for complex mechanical system designs. Besides, most current CPG-based methods only consider one aspect (e.g., speed), which widens the gap between theory and practice in robotic fish design. Also, it may affect the practical applicability of the designed motion model to a certain extent. This paper addresses this problem by constructing a multi-objective evolutionary design of a central pattern generator network to control the proposed biomimetic robotic fish. A new CPG model is proposed to help biomimetic robotic fish swim efficiently. In addition, an efficient multi-objective evolutionary algorithm proposed in our previous work is also applied to assist the biomimetic robotic fish in obtaining faster-swimming speed, good stability of the head, and higher propulsive efficiency simultaneously. Considering that the result of multi-objective optimization is a set of non-dominated solutions rather than a solution, a screening method based on fuzzy theory is adopted to assist decision-makers in selecting the most appropriate solution. Based on this, the control model of biomimetic robotic fish is constructed. The proposed control model is simulated and compared with seven well-known algorithms and a series of robotic fish designs. After that, the proposed control model is validated with extensive experiments on the actual biomimetic robotic fish. Simulations and experiments demonstrate the proposed control model's effectiveness and good performance, especially when the control model has be

关键词： Central pattern generators Biomimetic robotic fish multi-objective evolutionary algorithm Robotic fish control model

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evolutionary algorithm based Ensemble Extractive Summarization for Developing Smart Medical System

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2021年第2期13卷 229-259页

作者： Mallick, Chirantana Das, Asit Kumar Nayak, Janmenjoy Pelusi, Danilo Vimal, S. Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur 711103 Howrah India Aditya Inst Technol & Management AITAM Dept Comp Sci & Engn Tekkali 532201 Andhra Pradesh India Univ Teramo Dept Commun Sci Teramo Italy Natl Engn Coll Dept Informat Technol Kovilpatti 628503 Tamil Nadu India

The amount of information in the scientific literature of the bio-medical domain is growing exponentially, which makes it difficult in developing a smart medical system. Summarization techniques help for efficient searching and understanding of relevant information from the medical documents. In the paper, an evolutionary algorithm based ensemble extractive summarization technique is devised as a smart medical application with the idea of hybrid artificial intelligence on natural language processing. We have considered the abstracts of the target article and its cited articles as the base summaries and a multi-objective evolutionary algorithm is applied for generating the ensemble summary of the target article. Each sentence of the base summaries is represented by a concept vector of the medical terms contained in it with the help of the Unified Modelling Language System (UMLS) tool which is widely used in various smart medical applications. These terms carry the key information of the sentence which is very useful to find out the semantic similarity among the sentences. Fitness functions of the evolutionary algorithm are mainly defined using clustering coefficient and sparsity index, the concepts of graph theory. After the convergence of the algorithm, the best solution of the final population gives the ensemble summary. Next, the semantic similarity of each sentence in the target article with the ensemble summary is calculated and the sentences which are most similar to the ensemble summary are considered as the summary of the target article. The method is applied to the articles available in the PubMed MEDLINE database system and experimental results are compared with some state of the art methods applied in the Bio-medical domain. Experimental results and comparative study based on the performance evaluation show that the method competes with some recently proposed summarization methods and outperforms others, which express the effectiveness of the proposed meth

关键词： Bio-Medical informatics Ensemble summary Supervised extractive summarization multi-objective evolutionary algorithm Clustering coefficient Sparsity index

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Software review: DEAP (Distributed evolutionary algorithm in Python) library

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2019年第1期20卷 139-142页

作者： Kim, Jinhan Yoo, Shin Korea Adv Inst Sci & Technol Sch Comp 291 Daehak Ro Daejeon 34141 South Korea

We give a critical assessment of the DEAP (Distributed evolutionary algorithm in Python) open-source library and highly recommend it to both beginners and experts alike. DEAP supports a range of evolutionary algorithms including both strongly and loosely typed Genetic Programming, Genetic algorithm, and multi-objective evolutionary algorithms such as NSGA-II and SPEA2. It contains most of the basic functions required by evolutionary computation, so that its users can easily construct various flavours of both single and multi-objective evolutionary algorithms and execute them using multiple processors. It is ideal for fast prototyping and can be used with an abundance of other Python libraries for data processing as well as other machine learning techniques.

关键词： Python multi-objective evolutionary algorithm Pythonidae Libraries evolutionary algorithm source program library Genetic algorithm (GA)

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A fuzzy multidimensional multiple-choice knapsack model for project portfolio selection using an evolutionary algorithm

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ANNALS OF OPERATIONS RESEARCH 2013年第1期206卷 449-483页

作者： Tavana, Madjid Khalili-Damghani, Kaveh Abtahi, Amir-Reza La Salle Univ Lindback Distinguished Chair Informat Syst & Deci Philadelphia PA 19141 USA Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran Iran Univ Econ Sci Fac Econ Inst Management Dept Knowledge Engn & Decis Sci Tehran Iran

Project portfolio selection problems are inherently complex problems with multiple and often conflicting objectives. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems with precise data. However, the project data in real-world problems are often imprecise or ambiguous. We propose a fuzzy multidimensional multiple-choice Knapsack Problem (MMKP) formulation for project portfolio selection. The proposed model is composed of an Efficient Epsilon-Constraint (EEC) method and a customized multi-objective evolutionary algorithm. A Data Envelopment Analysis (DEA) model is used to prune the generated solutions into a limited and manageable set of implementable alternatives. Statistical analysis is performed to investigate the effectiveness of the proposed approach in comparison with the competing methods in the literature. A case study is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.

关键词： multidimensional multiple-choice knapsack multi-objective evolutionary algorithm Efficient epsilon-constraint Data envelopment analysis

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