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An effective model of multiple multi-objective evolutionary algorithms with the assistance of regional multi-objective evolutionary algorithms: VIPMOEAs

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APPLIED SOFT COMPUTING 2013年第5期13卷 2863-2895页

作者： Cheshmehgaz, Hossein Rajabalipour Desa, Mohamad Ishak Wibowo, Antoni UTM Fac Comp Sci & Informat Syst Skudai 81310 JB Malaysia

Division of the evolutionary search among multiple multi-objective evolutionary algorithms (MOEAs) is a recent advantage in MOEAs design, particularly in effective parallel and distributed MOEAs. However, most these algorithms rely on such a central (re) division that affects the algorithms' efficiency. This paper first proposes a local MOEA that searches on a particular region of objective space with its novel evolutionary selections. It effectively searches for Pareto Fronts (PFs) inside the given polar-based region, while nearby the region is also explored, intelligently. The algorithm is deliberately designed to adjust its search direction to outside the region - but nearby - in the case of a region with no Pareto Front. With this contribution, a novel island model is proposed to run multiple forms of the local MOEA to improve a conventional MOEA (e. g. NSGA-II or MOEA/D) running along - in another island. To dividing the search, a new division technique is designed to give particular regions of objective space to the local MOEAs, frequently and effectively. Meanwhile, the islands benefit from a sophisticated immigration strategy without any central (re) collection, (re) division and (re) distribution acts. Results of three experiments have confirmed that the proposed island model mostly outperforms to the clustering MOEAs with similar division technique and similar island models on DTLZs. The model is also used and evaluated on a real-world combinational problem, flexible logistic network design problem. The model definitely outperforms to a similar island model with conventional MOEA (NSGA-II) used in each island. (C) 2012 Elsevier B. V. All rights reserved.

关键词： multi-objective optimization problems multi-objective evolutionary algorithms multiple MOEAs Island model Pareto Fronts

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Adaptive multi-objective evolutionary algorithms for Overtime Planning in Software Projects

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2017年第10期43卷 898-917页

作者： Sarro, Federica Ferrucci, Filomena Harman, Mark Manna, Alessandra Ren, Jian UCL CREST Ctr London WC1E 6BT England Univ Salerno I-84084 Fisciano SA Italy Beihang Univ Beijing 100191 Peoples R China

Software engineering and development is well-known to suffer from unplanned overtime, which causes stress and illness in engineers and can lead to poor quality software with higher defects. Recently, we introduced a multi-objective decision support approach to help balance project risks and duration against overtime, so that software engineers can better plan overtime. This approach was empirically evaluated on six real world software projects and compared against state-of-the-art evolutionary approaches and currently used overtime strategies. The results showed that our proposal comfortably outperformed all the benchmarks considered. This paper extends our previous work by investigating adaptive multi-objective approaches to meta-heuristic operator selection, thereby extending and (as the results show) improving algorithmic performance. We also extended our empirical study to include two new real world software projects, thereby enhancing the scientific evidence for the technical performance claims made in the paper. Our new results, over all eight projects studied, showed that our adaptive algorithm outperforms the considered state of the art multi-objective approaches in 93 percent of the experiments (with large effect size). The results also confirm that our approach significantly outperforms current overtime planning practices in 100 percent of the experiments (with large effect size).

关键词： Software engineering management planning search-based software engineering project scheduling overtime hyperheuristic multi-objective evolutionary algorithms NSGAII

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Design and techno-economical optimization for stand-alone hybrid power systems with multi-objective evolutionary algorithms

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2007年第3期31卷 315-328页

作者： Shi, Jun-Hai Zhu, Xin-Jian Cao, Guang-Yi Shanghai Jiao Tong Univ Fuel Cell Inst Shanghai 200030 Peoples R China

The optimal design of the hybrid energy system can significantly improve the economical and technical performance of power supply. However, the problem is formidable because of the uncertain renewable energy supplies, the uncertain load demand, the nonlinear characteristics of some components, and the conflicting techno-economical objectives. In this work. the optimal design of the hybrid energy system has been formulated as a multi-objective optimization problem. We optimize the techno-economical performance of the hybrid energy system and analyse the trade-offs between the multi-objectives using multi-objective genetic algorithms. The proposed method is tested on the widely researched hybrid PV-wind power system design problem. The optimization seeks the compromise system configurations with reference to three incommensurable techno-economical criteria, and uses an hourly time-step simulation procedure to determine the design criteria with the weather resources and the load demand for one reference year. The well-known efficient multi-objective genetic algorithm. called NGAS-II (the fast elitist non-dominated sorting genetic algorithm), is applied on this problem. A hybrid PV-wind power system has been designed with this method and several Methods in the literature. The numerical results demonstrate that the proposed method is superior to the other methods. It can handle the optimal design of the hybrid energy system effectively and facilitate the designer with a range of the design solutions and the trade-off information. For this particular application, the hybrid PV-wind power system using more solar panels achieves better technical performance while the one using more wind power is more economical. Copyright (c) 2006 John Wiley & Sons, Ltd.

关键词： hybrid energy systems optimal design multi-objective evolutionary algorithms multiobjective optimization technical and economical optimization hybrid PV-wind systems

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Single and multi-objective evolutionary algorithms for the coordination of serial manufacturing operations

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JOURNAL OF INTELLIGENT MANUFACTURING 2006年第2期17卷 251-270页

作者： Naso, D Turchiano, B Meloni, C Politecn Bari Dipartimento Elettrotecn & Elettron Bari Italy

This paper focuses on a typical problem arising in serial production, where two consecutive departments must sequence their internal work, each taking into account the requirements of the other one. Even if the considered problem is inherently multi-objective, to date the only heuristic approaches dealing with this problem use single-objective formulations, and also require specific assumptions on the objective function, leaving the most general case of the problem open for innovative approaches. In this paper, we develop and compare three evolutionary algorithms for dealing with such a type of combinatorial problems. Two algorithms are designed to perform directed search by aggregating the objectives of each department in a single fitness, while a third one is designed to search for the Pareto front of non-dominated solutions. We apply the three algorithms to considerably complex case studies derived from industrial production of furniture. Firstly, we validate the effectiveness of the proposed genetic algorithms considering a simple case study for which information about the optimal solution is available. Then, we focus on more complex case studies, for which no a priori indication on the optimal solutions is available, and perform an extensive comparison of the various approaches. All the considered algorithms are able to find satisfactory solutions on large production sequences with nearly 300 jobs in acceptable computation times, but they also exhibit some complementary characteristics that suggest hybrid combinations of the various methods.

关键词： multi-objective evolutionary algorithms scheduling sequencing manufacturing systems

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Comparison of multi-objective evolutionary algorithms in hybrid Kansei engineering system for product form design

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ADVANCED ENGINEERING INFORMATICS 2018年 36卷 31-42页

作者： Shieh, Meng-Dar Li, Yongfeng Yang, Chih-Chieh Natl Cheng Kung Univ Dept Ind Design 1 Univ Rd Tainan 70101 Taiwan Southern Taiwan Univ Sci & Technol Dept Multimedia & Entertainment Sci 1 Nantai St Tainan 71005 Taiwan

Understanding the affective needs of customers is crucial to the success of product design. Hybrid Kansei engineering system (HKES) is an expert system capable of generating products in accordance with the affective responses. HKES consists of two subsystems: forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). In previous studies, HKES was based primarily on single-objective optimization, such that only one optimal design was obtained in a given simulation run. The use of multi-objective evolutionary algorithm (MOEA) in HKES was only attempted using the non-dominated sorting genetic algorithm-II (NSGA-II), such that very little work has been conducted to compare different MOEAs. In this paper, we propose an approach to HKES combining the methodologies of support vector regression (SVR) and MOEAs. In BKES, we constructed predictive models using SVR. In FKES, optimal design alternatives were generated using MOEAs. Representative designs were obtained using fuzzy c-means algorithm for clustering the Pareto front into groups. To enable comparison, we employed three typical MOEAs: NSGA-II, the Pareto envelope-based selection algorithm-II (PESA-II), and the strength Pareto evolutionary algorithm-2 (SPEA2). A case study of vase form design was provided to demonstrate the proposed approach. Our results suggest that NSGA-II has good convergence performance and hybrid performance;in contrast, SPEA2 provides the strong diversity required by designers. The proposed HKES is applicable to a wide variety of product design problems, while providing creative design ideas through the exploration of numerous Pareto optimal solutions.

关键词： Product form design Kansei engineering multi-objective evolutionary algorithms Support vector regression

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Applying multi-objective evolutionary algorithms to the automatic learning of extended Boolean queries in fuzzy ordinal linguistic information retrieval systems

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FUZZY SETS AND SYSTEMS 2009年第15期160卷 2192-2205页

作者： Lopez-Herrera, A. G. Herrera-Viedma, E. Herrera, F. Univ Granada Dept Comp Sci & AI E-18071 Granada Spain

The performance of information retrieval systems (IRSs) is Usually measured using two different criteria, precision and recall. Precision is the ratio of the relevant documents retrieved by the IRS in response to a user's query to the total number of documents retrieved, whilst recall is the ratio of the number of relevant documents retrieved to the total number of relevant documents for the user's query that exist in the documentary database. In fuzzy ordinal linguistic IRSs (FOLIRSs), where extended Boolean queries are used, defining the user's queries in a manual way is usually a complex task. In this contribution. our interest is focused on the automatic learning of extended Boolean queries in FOLIRSs by means of multi-objective evolutionary algorithms considering both mentioned performance criteria. We present an analysis of two well-known general-purpose multi-objective evolutionary algorithms to learn extended Boolean queries in FOLIRSs. These evolutionary algorithms are the non-dominated sorting genetic a algorithm (NSGA-II) and the Strength Pareto evolutionary algorithm (SPEA2). (C) 2009 Elsevier B.V. All rights reserved.

关键词： Information retrieval systems Genetic programming Inductive query by example multi-objective evolutionary algorithms Query learning

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Efficiency determination of induction motors using multi-objective evolutionary algorithms

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ADVANCES IN ENGINEERING SOFTWARE 2010年第2期41卷 255-261页

作者： Cunkas, Mehmet Sag, Tahir Selcuk Univ Dept Elect & Comp Educ Fac Tech Educ TR-42003 Konya Turkey

This paper introduces a method based on multi-objective evolutionary algorithms for the determination of in-service induction motor efficiency. In general, the efficiency is determined by accumulating multiple objectives into one objective by a linear combination and optimizing the resulting single-objective problem. The approach has some drawbacks such that exact information about solution alternatives will not be readily visible. In this paper the multi-objective evolutionary optimization algorithms, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Strength Pareto evolutionary Algorithm-2 (SPEA2), are successfully applied to the efficiency determination problem in induction motor. The performances of algorithms are compared on the basis of the obtained results. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithms Induction motor In-situ efficiency determination

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Case-base maintenance with multi-objective evolutionary algorithms

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2016年第2期46卷 259-284页

作者： Lupiani, Eduardo Massie, Stewart Craw, Susan Juarez, Jose M. Palma, Jose Univ Murcia Murcia Spain Robert Gordon Univ Aberdeen AB9 1FR Scotland

Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solving ability of the Case-Base Reasoning system. However, when the case-base has many cases, then performance problems arise due to the time needed to find those similar cases to the input problem. At this point, Case-Base Maintenance algorithms can be used to reduce the number of cases and maintain the accuracy of the Case-Base Reasoning system at the same time. Whereas Case-Base Maintenance algorithms typically use a particular heuristic to remove (or select) cases from the case-base, the resulting maintained case-base relies on the proportion of redundant and noisy cases that are present in the case-base, among other factors. That is, a particular Case-Base Maintenance algorithm is suitable for certain types of case-bases that share some indicators, such as redundancy and noise levels. In the present work, we consider Case-Base Maintenance as a multi-objective optimization problem, which is solved with a multi-objective evolutionary Algorithm. To this end, a fitness function is introduced to measure three different objectives based on the Complexity Profile model. Our hypothesis is that the multi-objective evolutionary Algorithm performing Case-Base Maintenance may be used in a wider set of case-bases, achieving a good balance between the reduction of cases and the problem-solving ability of the Case-Based Reasoning system. Finally, from a set of the experiments, our proposed multi-objective evolutionary Algorithm performing Case-Base Maintenance shows regularly good results with different sets of case-bases with different proportion of redundant and noisy cases.

关键词： Case-based reasoning Case-base maintenance multi-objective evolutionary algorithms

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The review of multiple evolutionary searches and multi-objective evolutionary algorithms

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ARTIFICIAL INTELLIGENCE REVIEW 2015年第3期43卷 311-343页

作者： Cheshmehgaz, Hossein Rajabalipour Haron, Habibollah Sharifi, Abdollah Univ Teknol Malaysia Fac Comp Sci & Informat Syst Skudai 81310 Johor Bahru Malaysia Univ Sistan & Baluchestan Dept Ind Engn Zahedan Iran

Over the past decade, subdividing evolutionary search into multiple local evolutionary searches has been identified as an effective method to search for optimal solutions of multi-objective optimization problems (MOPs). The existing multi-objective evolutionary algorithms that benefit from the multiple local searches (multiple-MOEAs, or MMOEAs) use different dividing methods and/or collaborations (information sharing) strategies between the created divisions. Their local evolutionary searches are implicitly or explicitly guided toward a part of global optimal solutions instead of converging to local ones in some divisions. In this reviewed paper, the dividing methods and the collaborations strategies are reviewed, while their advantage and disadvantage are mentioned.

关键词： multi-objective evolutionary algorithms Dividing methods Island model Migration strategy evolutionary search

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Optimal crop plans for a multi-reservoir system having intra-basin water transfer using multi-objective evolutionary algorithms coupled with chaos

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 140卷 34-47页

作者： Arunkumar, R. Jothiprakash, V. Indian Inst Technol Dept Civil Engn Bombay 400076 Maharashtra India Univ Western Ontario Dept Civil & Environm Engn London ON N6G 5B9 Canada

Optimizing a multi-reservoir system is complicated, since the operation of one reservoir depends on other reservoir and may also have conflicting multiple objectives. The conflicting purposes of a multi-reservoir system requires a systematic multi-objective study. Recently, multi-objective evolutionary algorithms (MOEAs) have been widely used for the multi-objective analysis of the reservoir systems. However, the simple MOEAs result in premature convergence and local optimal solution for complex non-linear multi-objective optimization problems. To improve the performance and maintain the diversity in the population, chaos is being combined with the evolutionary algorithms for optimizing complex problems. In the present study, the chaos algorithm is coupled with MOEAs such as non-dominated genetic algorithm-II (CNSGA-II) and multi-objective differential evolution algorithm (CMODE) to derive an optimal crop planning for a multi-reservoir system having intra-basin water transfer. The model is developed with the objective of maximizing the net benefits and maximizing the crop production, subject to various physical, land and water availability constraints. The resulted optimal policy is further assessed using a simulation model and its performance is evaluated using various statistical indices. It is found that CMODE has resulted in slightly higher net benefits of Rs. 1921.77 Million and 1201.55 thousand tons of crop production with an irrigation intensity of 106.29% compared to other techniques used in this study. It has also resulted in an optimal spatial and temporal intra-basin water transfer from the upstream reservoirs to the downstream reservoir. The simulation of optimal results showed that the optimal policies obtained from CMODE performed well for longer period with less irrigation deficits. All the reservoirs in the system achieved more than 95% reliability in meeting the irrigation demands and intra-basin water transfer. (C) 2017 Elsevier B.V. All rights res

关键词： Chaos Crop planning multi-objective evolutionary algorithms multi-reservoir system Optimization Simulation

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