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Many objective Particle Swarm Optimization

INFORMATION SCIENCES 2016年第0期374卷 115-134页

作者： Figueiredo, E. M. N. Ludermir, T. B. Bastos-Filho, C. J. A. Univ Fed Pernambuco Ctr Informat Rua Estrela 52 BR-52060160 Recife PE Brazil Univ Pernambuco Escola Politecn Pernambuco Recife PE Brazil

Many-objective problems refer to the optimization problems containing more than three conflicting objectives. To obtain a representative set of well-distributed non-dominated solutions close to Pareto front in the objective space remains a challenging problem. Many papers have proposed different multi-objective evolutionary algorithms to solve the lack of the convergence and diversity in many-objective problems. One of the more promising approaches uses a set of reference points to discriminate the solutions and guide the search process. However, this approach was incorporated mainly in multi-objective evolutionary algorithms, and there are just some few promising adaptations of Particle Swarm Optimization approaches for effectively tackling many-objective problems regarding convergence and diversity. Thus, this paper proposes a practical and efficient Many-objective Particle Swarm Optimization algorithm for solving many-objective problems. Our proposal uses a set of reference points dynamically determined according to the search process, allowing the algorithm to converge to the Pareto front, but maintaining the diversity of the Pareto front. Our experimental results demonstrate superior or similar performance when compared to other state-of-art algorithms. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Many-objective problems multi-objective evolutionary algorithms Particle Swarm Optimization Swarm intelligence

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Optimisation of photovoltaic-diesel-battery stand-alone systems minimising system weight

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ENERGY CONVERSION AND MANAGEMENT 2016年第Jul.期119卷 279-288页

作者： Cristobal-Monreal, Ivan R. Dufo-Lopez, Rodolfo Ctr Univ Def Acad Gen Militar Ctra Huesca S-N Zaragoza 50090 Spain Univ Zaragoza Dept Elect Engn Calle Maria de Luna 3 Zaragoza 50018 Spain

This article shows a new method for the optimisation of stand-alone (off-grid) hybrid systems (photovoltaic-diesel-battery) to supply the electricity of mobile systems such as non-governmental organization hospitals, temporary camps or other mobile facilities to be placed temporally in remote or conflictive areas. If there is difficult or dangerous access, the most important objective to be minimised is the total weight of the system. Also, the cost is an important variable to minimise. Nowadays, the majority of these systems are diesel-only or diesel-battery systems. However, depending on the duration of the temporary system, a photovoltaic-diesel-battery system can have a lower weight and/or cost. Three types of optimisation are considered: (i) minimisation of the weight of the system;(ii) minimisation of the cost;and (iii) minimisation of both weight and cost. The two first are conducted by genetic algorithms, and the last one is performed using multi-objective evolutionary algorithms. An example of application of this method to a temporary hospital in Central African Republic is shown, concluding that in the cases of more than 90 days photovoltaic (flexible crystalline silicon panels) + diesel + battery is the solution which minimises weight. When minimising cost, all the cases include photovoltaic with high penetration. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Renewable stand-alone systems Weight minimisation Genetic algorithms 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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μG2-ELM: An upgraded implementation of μ G-ELM

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NEUROCOMPUTING 2016年 171卷 1302-1312页

作者： Lacruz, B. Lahoz, D. Mateo, P. M. Univ Zaragoza Dept Metodos Estadist E-50009 Zaragoza Spain

mu G-ELM is a multiobjective evolutionary algorithm which looks for the best (in terms of the MSE) and most compact artificial neural network using the ELM methodology. In this work we present the mu G2-ELM, an upgraded version of JIG-ELM, previously presented by the authors. The upgrading is based on three key elements: a specifically designed approach for the initialization of the weights of the initial artificial neural networks, the introduction of a re-sowing process when selecting the population to be evolved and a change of the process used to modify the weights of the artificial neural networks. To test our proposal we consider several state-of-the-art Extreme Learning Machine (ELM) algorithms and we confront them using a wide and well-known set of continuous, regression and classification problems. From the conducted experiments it is proved that the mu G2-ELM shows a better general performance than the previous version and also than other competitors. Therefore, we can guess that the combination of evolutionary algorithms with the ELM methodology is a promising subject of study since both together allow for the design of better training algorithms for artificial neural networks. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks Extreme Learning Machines multi-objective evolutionary algorithms Decision theory

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An improved multi-objective population-based extremal optimization algorithm with polynomial mutation

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INFORMATION SCIENCES 2016年 330卷 49-73页

作者： Zeng, Guo-Qiang Chen, Jie Li, Li-Min Chen, Min-Rong Wu, Lie Dai, Yu-Xing Zheng, Chong-Wei Wenzhou Univ Dept Elect & Elect Engn Wenzhou 325035 Peoples R China S China Normal Univ Sch Comp Guangzhou 510631 Guangdong Peoples R China

As a recently developed evolutionary algorithm inspired by far-from-equilibrium dynamics of self-organized criticality, extremal optimization (EO) has been successfully applied to a variety of benchmark and engineering optimization problems. However, there are only few reported research works concerning the applications of EO in the field of multi-objective optimization. This paper presents an improved multi-objective population-based EO algorithm with polynomial mutation called IMOPEO-PLM to solve multi-objective optimization problems (MOPs). Unlike the previous multi-objective versions based on EO, the proposed IMOPEO-PLM adopts population-based iterated optimization, a more effective mutation operation called polynomial mutation, and a novel and more effective mechanism of generating new population. From the design perspective of multi-objective evolutionary algorithms (MOEAs), IMOPEO-PLM is relatively simpler than other reported competitive MOEAs due to its fewer adjustable parameters and only mutation operation. Furthermore, the extensive experimental results on some benchmark MOPs show that IMOPEO-PLM performs better than or at least competitive with these reported popular MOEAs, such as MOPEO, MOEO, NSGA-II, A-MOCLPSO, PAES, SPEA, SPEA2, SMS-EMOA, SMPSO, and MOEA/D-DE, by using nonparametric statistical tests, e.g., Kruskal-Wallis test, Mann-Whitney U test, Friedman and Quade tests, in terms of some commonly-used quantitative performance metrics, e.g., convergence, diversity (spread), hypervolume, generational distance, inverted generational distance. (C) 2015 Elsevier Inc. All rights reserved.

关键词： multi-objective optimization Extremal optimization multi-objective evolutionary algorithms Mutation operators

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Selection mechanisms based on the maximin fitness function to solve multi-objective optimization problems

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INFORMATION SCIENCES 2016年 332卷 131-152页

作者： Menchaca-Mendez, Adriana Coello Coello, Carlos A. IPN CINVESTAV Dept Comp Mexico City 07300 DF Mexico

In this paper, we study three selection mechanisms based on the maximin fitness function and we propose another one. These selection mechanisms give rise to the following MOEAs: "MC-MOEA", "MD-MOEA", "MH-MOEA" and "MAH-MOEA". We validated them using standard test functions taken from the specialized literature, having from three up to ten objective functions. We compare these four MOEAs among them and also with respect to MOEA/D (which is based on decomposition), and to SMS-EMOA (which is based on the hypervolume indicator). Our preliminary results indicate that "MD-MOEA" and "MAH-MOEA" are promising alternatives for solving MOPs with either low or high dimensionality. (C) 2015 Elsevier Inc. All rights reserved.

关键词： multi-objective evolutionary algorithms Selection operators Maximin fitness function

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Applying multi-objective evolutionary algorithms to QoS-Aware Web Service Composition

Applying Multi-Objective Evolutionary Algorithms to QoS-Awar...

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6th International Conference on Advanced Data Mining and Applications (ADMA)

作者： Li, Li Cheng, Peng Ou, Ling Zhang, Zili Southwest Univ Chongqing Peoples R China

ISBN: (纸本)9783642173127

Finding optimal solutions for QoS-aware Web service composition with conflicting objectives and various restrictions on quality matrices is a NP-hard problem. This paper proposes the use of multi-objective evolutionary algorithms (MOEAs for short) for QoS-aware service composition optimisation. More specifically, SPEA2 is introduced to achieve the goal. The algorithm is good at dealing with multi-objective combinational optimisation problems. Experimental results reveal that SPEA2 is able to approach the Pareto-optimal front with well spread distribution. The Pareto front approximations provide different trade-offs, from which the end-users may select the better one based on their preference.

关键词： multi-objective evolutionary algorithms Service composition QoS Pareto front

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A multi-objective Genetic Local Search Algorithm for Optimal Feature Subset Selection

A Multi-objective Genetic Local Search Algorithm for Optimal...

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International Conference on Computational Science and Computational Intelligence (CSIC)

作者： Tian, David Leeds Beckett Univ Leeds Sustainabil Inst Leeds LS1 3HE W Yorkshire England

ISBN: (纸本)9781509055104

Feature selection algorithms select the most relevant features of a data set to improve the classification performance of the machine learning classifiers trained using the data set. This paper proposes a feature selection algorithm called multi-objective genetic local search (MOGLS) which integrates a 3-objective genetic algorithm with a local search heuristic to find feature subsets with the maximum prediction accuracy, the smallest sizes and the minimum redundancy. The performance of MOGLS is compared with 4 algorithms: a wrapper genetic algorithm, correlation-based feature selection, mutual information ranking and C4.5 on 8 datasets from the UCI machine learning repository. MOGLS performs better than or as good as the 4 algorithms on the 8 datasets.

关键词： Machine learning technologies multi-objective evolutionary algorithms Genetic algorithms Metaheuristic optimization algorithms Fuzzy and rough data analysis

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multiobjective Discrete Differential Evolution for Service Restoration in Energy Distribution Systems

Multiobjective Discrete Differential Evolution for Service R...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Sanches, Danilo S. London, Joao Bosco A., Jr. Delbem, Alexandre C. B. Univ Tecnol Fed Parana Cornelio Procopio PR Brazil Univ Sao Paulo Sao Carlos Sch Engn Sao Carlos SP Brazil Univ Sao Paulo Inst Math & Comp Sci Sao Carlos SP Brazil

ISBN: (纸本)9781450343237

This paper presents a new multiobjective discrete differential evolution for service restoration in distribution systems. The proposed approach was compared with other five multiobjective evolutionary algorithms (MOEAs), which use Node-Depth Encoding (NDE). The proposed approach have been evaluated taking into account the switching operations necessary to find adequate restoration plans considering multiple non-linear constraints and objective functions. The MOEAs used in this paper have been employed to solve four different datasets with 3,860, 7,720, 15,440 and 30,880 buses, respectively. Simulations results have shown that proposed approach reached good solutions with low switching operations and reduced running time when compared with others MOEAs.

关键词： Large-Scale Distribution Systems Service Restoration Node-depth Encoding multi-objective evolutionary algorithms Differential Evolution

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A multi-objective Approach to Predicting Motor and Cognitive Deficit in Parkinson's Disease Patients

A Multi-Objective Approach to Predicting Motor and Cognitive...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Vallejo, Marta Cosgrove, Jeremy Alty, Jane E. Jamieson, Stuart Smith, Stephen L. Corne, David W. Lones, Michael A. Heriot Watt Univ Dept Comp Sci Edinburgh Midlothian Scotland Leeds Gen Infirm Dept Neurol Leeds W Yorkshire England Univ York Dept Elect York N Yorkshire England

ISBN: (纸本)9781450343237

Parkinson's disease (PD) is a chronic neurodegenerative condition. Traditionally categorised as a movement disorder, nowadays it is recognised that PD can also lead to significant cognitive dysfunction including, in many cases, full-blown dementia. Due to the wide range of symptoms, including significant overlap with other neurodegenerative conditions, both diagnosis and prognosis remain challenging. In this paper, we describe our use of a multi-objective evolutionary algorithm to explore trade-offs between polynomial regression models that predict different clinical measures, with the aim of identifying features that are most indicative of motor and cognitive PD variants. Our initial results are promising, showing that polynomial regression models are able to predict clinical measures with good accuracy, and that suitable predictive features can be identified.

关键词： multi-objective evolutionary algorithms Predictive modelling Parkinson's disease Polynomial regression

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