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A NOVEL multi-objective ESTIMATION OF DISTRIBUTION ALGORITHM BASED ON SENSITIVITY OF objective FUNCTION

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第6期9卷 2543-2566页

作者： Seo, Hyunshik Lee, Chaewoo Ajou Univ Dept Elect & Comp Engn San 5 Suwon 443749 South Korea

Most multi-objective optimization problems (MOPs) have a set of optimal trade-off solutions known as the Pareto-optimal solutions since the objectives in MOPs are usually in conflict with one another. Recently proposed estimation of distribution algorithms (EDAs) build a probability distribution model based on the probabilistic information about decision variables of solutions, and then produce new solutions from the model. In the algorithms, the modeling technique enables the initial large search space to be reduced to small promising solution space during the search. However, the existing EDAs might be inefficient at generating the promising solutions since they depend on the information extracted from the decision variables of current solutions expected to approach the optimal solutions. For effective modeling of the promising solutions, we firstly introduce new information about the relationship between decision variables and objective functions;this information is called sensitivity of objective function. Secondly, we propose a multi-objective estimation of distribution algorithm based on the sensitivity of objective function (MOEDA-S). In the MOEDA-S, the sensitivity-based distribution modeling adapts to the current search strategy such that the convergence-focused search at the beginning part of the search is changed to a diversity-focused search at the latter part of the search. MOEDA-S is compared with two other leading multi-objective evolutionary algorithms on a set of test instances. The simulation results show that MOEDA-S outperforms the two compared algorithms in terms of both convergence and diversity performances of the solutions.

关键词： multi-objective optimization problems multi-objective evolutionary algorithms Estimation of distribution algorithms Variables sensitivity-based modeling

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Learning individual preferences from aggregate data: A genetic algorithm for discovering baskets of television shows with affinities to political and social interests

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 168卷 114184-114184页

作者： Padmanabhan, Balaji Barfar, Arash Univ S Florida Muma Coll Business Sch Informat Syst & Management 4202 F Fowler Ave Tampa FL 33620 USA Univ Nevada Coll Business Dept Informat Syst 1664 N Virginia St Reno NV 89557 USA

This paper presents a flexible general-purpose framework using genetic and multi-objective evolutionary algorithms that can leverage "unlabeled" (and anonymized) panel data on television viewership along with aggregate-level vote or public opinions statistics to (i) identify sets of programs that have affinities with politics and social issues, and (ii) estimate individual preferences from unlabeled data. The applications of this framework are significant given the wide interest in using big data for political advertising and building election forecasting models with non-polling data. Analyzing viewership spanning over seven billion minutes from Nielsen's TV panel for an entire year (2016), we illustrate how this framework can learn interesting baskets of programs whose viewership can help estimate individual attitudes toward politics, global warming, same-sex marriage, and abortion.

关键词： Genetic algorithms multi-objective evolutionary algorithms Audience analytics Aggregate data TV panel data Micro-targeting

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A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout

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COMPUTERS IN BIOLOGY AND MEDICINE 2019年 113卷 103390-000页

作者： Daud, Kauthar Mohd Mohamad, Mohd Saberi Zakaria, Zalmiyah Hassan, Rohayanti Shah, Zuraini Ali Deris, Safaai Ibrahim, Zuwairie Napis, Suhaimi Sinnott, Richard O. Univ Teknol Malaysia Fac Engn Sch Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Univ Malaysia Kelantan Inst Artificial Intelligence & Big Data City Campus Kota Baharu 16100 Kelantan Malaysia Univ Malaysia Kelantan Fac Bioengn & Technol Jeli CampusLock Bag 100 Jeli 17600 Kelantan Malaysia Univ Teknol Malaysia Fac Engn Sch Comp Software Engn Res Grp Johor Baharu 81310 Malaysia Univ Malaysia Pahang Fac Elect & Elect Engn Pekan Pahang Malaysia Univ Putra Malaysia Fac Biotechnol & Biomol Sci Dept Cell & Mol Biol Serdang 43400 Selangor Malaysia Univ Melbourne Sch Comp & Informat Syst Melbourne Sch Engn Melbourne Vic 3010 Australia

Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate.

关键词： multi-objective evolutionary algorithms Metabolic engineering Flux balance analysis Reaction knockout Pareto dominance Artificial intelligence Bioinformatics

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multi-objective social spider optimisation algorithm

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JOURNAL OF ENGINEERING-JOE 2018年第16期2018卷 1521-1527页

作者： Wang, Yanjiao Sun, Xiaonan Wang, Chao Tao, Huanhuan Northeast Elect Power Univ Sch Elect Engn 169 Changchun Rd Jilin 132011 Jilin Peoples R China

Considering that the social spider algorithm is still unable to solve the multi-objective optimisation problem, this study presents a multi-objective social spider optimisation algorithm. Firstly, a new normalised fitness value formula is proposed based on the multi-objective optimisation purposes, which is able to trade off the non-dominated rankings and crowded distances and evaluate individual strengths and weaknesses effectively;secondly, the gravitational factor is used in order to balance the impact of individual fitness and distance to individual performance, which improves the vibration perception ability of the calculation method as well;once again, the renewal pattern of the female and male population is improved in order to balance the convergence rate and population diversity of the algorithm;lastly, the environmental selection strategy which is based on cosine distance is proposed for female and male population renewal. Testing on the ZDT test set, experimental results show that, compared with the six representative multi-objective evolutionary algorithms, the proposed algorithm in this study has better distribution and better convergence performance.

关键词： optimisation evolutionary computation fitness value formula non-dominated rankings gravitational factor ZDT test set multi-objective evolutionary algorithms multi-objective social spider optimisation algorithm

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Energy System Diagnosis by a Fuzzy Expert System with Genetically Evolved Rules

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INTERNATIONAL JOURNAL OF THERMODYNAMICS 2008年第3期11卷 115-121页

作者： Toffolo, Andrea Lazzaretto, Andrea Univ Padua Dept Mech Engn Via Venezia 1 I-35131 Padua Italy

The problem of malfunction diagnosis in energy systems can be approached using an expert system which compares the experimental data measured by the plant acquisition system and the calculated data evaluated by a plant simulator under the same operating conditions. In this paper the rules that form the "knowledge base" of the expert system are not assigned heuristically by trying to code the expertise of plant personnel, as it is usually done, but they are artificially and randomly generated by the recombination and selection operators of an evolutionary algorithm. A two-objective optimization problem is set up, in order to search for the optimal sets of rules having the minimum complexity but simultaneously maximizing the number of correct fault identifications for a given set of malfunctioning operating conditions. A global and a local approach are applied to a real test case, a two-shaft gas turbine used as the gas section of a combined-cycle cogeneration plant, in order to evaluate the potentialities and the limits of this methodology.

关键词： Diagnosis fuzzy expert system multi-objective evolutionary algorithms

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A multi-Criteria Approach to Rank the Municipalities of the States of Mexico by its Marginalization Level: The Case of Jalisco

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2017年第2期16卷 473-513页

作者： Leyva Lopez, Juan Carlos Solano Noriega, Jesus Jaime Gastelum Chavira, Diego Alonso Univ Occidente Econ & Management Sci Dept Blvd Lola Beltran & Blvd Rolando Arjona Culiacan Sinaloa Mexico

Marginalization studies of a population are tools that enable the Mexican government to understand and compare the socio-demographic situation of different regions of the country. The goal is to implement effectively various programs of social or economic development whose aims are to ght against the population's lag, which has affected the quality of life of Mexican citizens. In this paper, a multi-criteria approach for ranking the municipalities of the states of Mexico by their levels of marginalization is proposed, and the case of Jalisco, Mexico, is presented. The approach uses the ELECTRE III method to construct a mediumsized valued outranking relation and then employs a new multi-objective evolutionary algorithm (MOEA) based on the nondominated sorting genetic algorithm (NSGA) II to exploit the relation to obtain a recommendation. The results of this application can be useful for policymakers, planners, academics, investors, and business leaders. This study also contributes to an important, yet relatively new, body of application-based literature that investigates multi-criteria approaches to decision-making that use fuzzy theory and evolutionary multi-objective optimization methods. A comparison of the ranking obtained with the proposed methodology and the stratification created by the National Population Council of Mexico shows that the methodology presented is consistent and yields reliable results for this problem.

关键词： multi-criteria decision analysis municipal marginalization ranking problem ELECTRE III multi-objective evolutionary algorithms

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Solving a stochastic multi-objective and multi-period hub location problem considering economic aspects by meta-heuristics: application in public transportation

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2019年第3期60卷 183-202页

作者： Hamid, Mahdi Bastan, Mahdi Hamid, Mojtaba Sheikhahmadi, Farrokh Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Univ Eyvanekey Dept Ind Engn Garmsar Iran Iran Univ Sci & Technol Dept Ind Engn Tehran Iran Virginia Tech Pamplin Coll Business Dept Business Informat Technol Blacksburg VA 24061 USA

In this paper, a multi-objective scenario based mathematical model is presented for the capacitated hub location problem in public transportation considering economic and investment aspects. Three objective functions are regarded in the presented mathematical model. The first one aims to minimise the total costs considering the possibility of investing the unused budget at each period. The second one aims to minimise the total processing time in the hub network at each period. The last one aims to minimise the maximum distance between each pair of origin-destination nodes in the network. To solve the model, three multi-objective meta-heuristic algorithms are developed, namely S metric selection evolutionary multi-objective optimisation algorithm (SMS-EMOA), multi-objective imperialist competitive algorithm (MOICA) and non-dominated sorting genetic algorithm (NSGA-II). Finally, developed algorithms are compared to each other based on several comparison measures using a relatively novel statistical approach.

关键词： hub location problem multi-period hub location problem uncertainty economic aspects multi-objective evolutionary algorithms

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An Algorithm for Calculating the Hypervolume Contribution of a Set

An Algorithm for Calculating the Hypervolume Contribution of...

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World Automation Congress

作者： Xiuling Zhou Ping Guo C. L. Philip Chen Image Processing and Pattern Recognition Lab Beijing Normal University Beijing 100875 China The Faculty of Science and Technology University of Macau Macau SAR China

ISBN: (纸本)9781467344975

The reliability model can be optimized with a multi-objective optimization algorithm, while hypervolume-based multi-objective evolutionary algorithms (MOEAs) have been shown to produce better results for multi-objective problem in practice. When hypervolume is used in some MOEAs as archiving strategy, diversity mechanism or selection criterion to guide the search, it is necessary to determine which subset contributes the least hypervolume contribution. Few algorithms have been designed for this purpose. In this paper a new algorithm based on HSO (hypervolume by slicing objective) is proposed for calculating the exclusive hypervolume contributions of each subset to the whole nondominated set directly for small dimension. The new algorithm is composed of two parts: the algorithm SHSO (set hypervolume contribution by slicing objective) and the algorithm SHSO*. SHSO is used to calculate the exclusive hypervolume contribution of a subset to the whole nondominated set. SHSO* is applied to select the subset which contributes the least hypervolume contribution by repeated application of SHSO. Compared with HSO adopted for calculating the exclusive hypervolume contribution, SHSO* outperforms HSO for all of the test fronts with small dimension.

关键词： Keyword - Reliability model multi-objective evolutionary algorithms Hypervolume contribution

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Good things come in threes: improving search-based crash reproduction with helper objectives 20

Good things come in threes: improving search-based crash rep...

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Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

作者： Pouria Derakhshanfar Xavier Devroey Andy Zaidman Arie van Deursen Annibale Panichella Delft University of Technology Delft The Netherlands

ISBN: (纸本)9781450367684

Writing a test case reproducing a reported software crash is a common practice to identify the root cause of an anomaly in the software under test. However, this task is usually labor-intensive and time-taking. Hence, evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called Crash Distance to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called MO-HO, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-objectives for crash reproduction, namely test length (to minimize) and method sequence diversity (to maximize), in addition to Crash Distance. We assessed MO-HO using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 non-trivial crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for MO-HO. We evaluated this best-performing algorithm for MO-HO against the state-of-the-art: single-objective approach (Single-objective Search) and decomposition-based multi-objectivization approach (De-MO). Our results show that MO-HO reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, MO-HO improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-objective Search and De-MO, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time.

关键词： search-based software testing multi-objective evolutionary algorithms crash reproduction

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Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic wind and solar PV generation

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Heliyon 2024年第20期10卷 e39041页

作者： Ali, Aamir Aslam, Sumbal Keerio, M.U. Mirsaeidi, Sohrab Mugheri, Noor Hussain Ismail, Muhammad Abbas, Ghulam Othmen, Salwa Department of Electrical Engineering Quaid-e-Awam University of Engineering Science and Technology Sindh Nawabshah 67450 Pakistan School of Electrical Engineering Beijing Jiaotong University Beijing 100044 China School of Electrical Engineering Southeast University Nanjing 210096 China Department of Computers and Information Technologies College of Sciences and Arts Turaif Northern Border University Arar 91431 Saudi Arabia

The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. multi-objective evolutionary algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure

关键词： Economic dispatch Fixed reserve multi-objective evolutionary algorithms Renewable energy resources Voltage stability index

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