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A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

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SOFT COMPUTING 2017年第4期21卷 885-906页

作者： Azzouz, Radhia Bechikh, Slim Ben Said, Lamjed Univ Tunis SOIE Lab Tunis Tunisia

In addition to the need for simultaneously optimizing several competing objectives, many real-world problems are also dynamic in nature. These problems are called dynamic multi-objective optimization problems. Applying evolutionary algorithms to solve dynamic optimization problems has obtained great attention among many researchers. However, most of works are restricted to the single-objective case. In this work, we propose an adaptive hybrid population management strategy using memory, local search and random strategies, to effectively handle environment dynamicity for the multi-objective case where objective functions change over time. Moreover, the proposed strategy is based on a new technique that detects the change severity, according to which it adjusts the number of memory and random solutions to be used. This ensures, on the one hand, a high level of convergence and on the other hand, the required diversity. We propose a dynamic version of the Non dominated Sorting Genetic Algorithm II, within which we integrate the above-mentioned strategies. Empirical results show that our proposal based on the use of the adaptive strategy is able to handle dynamic environments and to track the Pareto front as it changes over time. Moreover, when confronted with several recently proposed dynamic algorithms, it has presented competitive and better results on most problems.

关键词： Change severity sensing dynamic multi-objective optimization Time-changing objective functions Adaptive populationmanagement Memory-based strategy Local search-based strategy Change severity-based strategy

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A Parallel multi-objective Cooperative Co-evolutionary Algorithm with Changing Variables 17

A Parallel Multi-objective Cooperative Co-evolutionary Algor...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Xu, Biao Zhang, Yong Gong, Dun-wei Wang, Ling China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781450349390

multi-objective optimization problems with changing variables are very common in real-world applications. This kind of problems often has a changing Pareto-optimal set and a complex relation among decision variables. In order to rapidly track the time-dependent Pareto-optimal front, we propose a framework of parallel cooperative co-evolution based on dynamically grouping decision variables. Decision variables are first divided into a number of groups using the Spearman rank correlation analysis, with different groups having a weak correlation. Then, a sub-population is employed to optimize decision variables in each group using a traditional multi-objective evolutionary algorithm. The evaluation of a complete solution is fulfilled through the cooperation among sub-populations. We compare the proposed algorithm with three state-of-the-art algorithms by applying them to two modified benchmark optimization problems. Empirical results show that the proposed algorithm is superior to the compared ones.

关键词： dynamic multi-objective optimization parallel changing variables co-evolutionary

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Design and Architecture of the jMetaISP Framework 17

Design and Architecture of the jMetaISP Framework

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Nebro, Antonio J. Barba-Gonzalez, Cristobal Nieto, Jose Garcia Cordero, Jose A. Montes, Jose F. Aldana Univ Malaga ETS Ingn Informat Campus Teatinos E-29071 Malaga Spain European Org Nucl Res CERN CERN 31-3-028 CH-1211 Geneva Switzerland

ISBN: (纸本)9781450349390

jMetalSP is a framework for dynamic multi-objective Big Data optimization. It combines the jMetal multi-objective framework with the Apache Spark cluster computing system to allow the solving of dynamic optimization problems from a number of external streaming data sources in Big Data contexts. In this paper, we describe the current status of the jMetalSP project, focusing mainly in its design and internal architecture, with the aim of offering a comprehensive view of its main features to interested researchers. Among the covered features, we describe the main components of a jMetalSP application, including dynamic problems, dynamic algorithms, streaming data sources, and data consumers. For practical purposes, we describe two test cases to illustrate how to address dynamic combinatorial and dynamic continuous optimization problems by using the proposed framework.

关键词： optimization Framework dynamic multi-objective optimization multi-objective Metaheurstics Open Source

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Energy efficiency in high speed railway traffic operation: a real-time ecodriving algorithm 15

Energy efficiency in high speed railway traffic operation: a...

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15th IEEE International Conference on Environment and Electrical Engineering (EEEIC)

作者： Fernandez-Rodriguez, Adrian Fernandez-Cardador, Antonio Cucala, Asuncion P. Comillas Pontifical Univ Inst Res Technol IIT Madrid Spain

ISBN: (纸本)9781479979929

The application of ecodriving strategies leads to important energy savings in high speed railway systems. Ecodriving techniques are usually offline implemented in the design of commercial services. However, efficient driving can also be online recalculated to regulate the deviations from the schedule of the train. In this paper, a dynamic multi-objective model is proposed to calculate a set of efficient drivings that are updated during the journey. Using this model it is possible to react when delays arise replacing the current driving by a faster one. The new speed profile will be energy efficient under the new conditions. The model is solved using the extension of NSGA-II for dynamic optimization problems (DNSGA-II). This algorithm is combined with a detailed simulator of the train motion. The accuracy of the simulator ensures the fulfillment of the quality restrictions in commercial services. The proposed method has been applied to a case study using real data from a Spanish high speed line. The performance of DNSGA-II solving the dynamic model has been compared with NSGA-II. Furthermore, energy savings provided by the regulation algorithm have been evaluated comparing it with the typical driving style when a delay is detected.

关键词： DNSGA-II dynamic multi-objective optimization ecodriving energy saving high speed railway real time traffic operation simulation

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dynamic Optimal Control of Sustained Overvoltage during Power System Restoration Process

Dynamic Optimal Control of Sustained Overvoltage during Powe...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Chen, Bin Wang, Hongtao Cao, Xi Shandong Univ Sch Elect Engn Jinan 250061 Shandong Peoples R China

ISBN: (纸本)9781467380409

Sustained power frequency overvoltage control strategy is an important part of power system restoration plan. The existing sustained over voltage control methods are based on static network, ignoring the dynamic restoration process. By choosing restoration targets as milestones, the dynamic process can be divided into several restoration sequences, each sequence consists of several restoration operations. In this paper, a dynamic multi-objective sustained overvoltage control model based on the restoration sequence is proposed, which takes the coupling of voltage control schemes at different stages into consideration. The objective functions adapted to the characteristics of restoration process are defined, including the operation risk of restoration sequence, adjusting time of voltage control scheme and voltage deviation of energized buses. The effectiveness of proposed model is verified by the simulation results of dynamic and static optimization under different restoration scenarios in Shandong power grid.

关键词： power system restoration sustained power frequency overvoltage control restoration sequence dynamic multi-objective optimization

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An Adaptive Diversity Introduction Method for dynamic Evolutionary multiobjective optimization

An Adaptive Diversity Introduction Method for Dynamic Evolut...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Liu, Min Zheng, Jinhua Wang, Junnian Liu, Yuzhen Jiang, Lei Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan Peoples R China Coll Hunan Prov Key Lab Knowledge Proc & Networked Mfg Xiangtan Peoples R China Minnan Normal Univ Sch Comp Zhangzhou Peoples R China Xiangtan Univ Inst Informat Engn Xiangtan Peoples R China

ISBN: (纸本)9781479914883

This paper investigates how to use diversity introduction methods to enhance the dynamic evolutionary multiobjective optimization algorithms in dealing with dynamic multiobjective optimization problems (DMOPs). Although diversity introduction method is easy used to response to the dynamic change, current diversity introduction methods still have a difficulty in identifying the correct proportion of diversity introduction. To overcome this difficulty, this paper proposes an adaptive diversity introduction (ADI) method. Specifically, the proportion of diversity introduction can be dynamically adjusted rather than being hand designed and fixed in advance. In addition, an adaptive relocation operator is designed to adapt the evolving individuals to the new environmental condition. The effectiveness of the ADI method is validated against various diversity introduction methods upon five DMOPs test problems. The simulation results show that the proposed ADI has better robustness and total performance than other diversity introduction methods.

关键词： dynamic multi-objective optimization evolutionary algorithm diversity introduction adaptive

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dynamic Optimal Control of Sustained Overvoltage during Power System Restoration Process

Dynamic Optimal Control of Sustained Overvoltage during Powe...

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IEEE Power and Energy Society General Meeting

作者： Bin Chen Hongtao Wang Xi Cao School of Electrical Engineering Shandong University

ISBN: (纸本)9781467380416

Sustained power frequency overvoltage control strategy is an important part of power system restoration plan. The existing sustained overvoltage control methods are based on static network, ignoring the dynamic restoration process. By choosing restoration targets as milestones, the dynamic process can be divided into several restoration sequences, each sequence consists of several restoration operations. In this paper, a dynamic multi-objective sustained overvoltage control model based on the restoration sequence is proposed, which takes the coupling of voltage control schemes at different stages into consideration. The objective functions adapted to the characteristics of restoration process are defined, including the operation risk of restoration sequence, adjusting time of voltage control scheme and voltage deviation of energized buses. The effectiveness of proposed model is verified by the simulation results of dynamic and static optimization under different restoration scenarios in Shandong power grid.

关键词： Power system restoration Sustained power frequency overvoltage control Restoration sequence dynamic multi-objective optimization Power system restoration restoration restoration goals static optimization voltage error ENERGIZING autocovariance air adjustment scheme

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Population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problems

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SWARM AND EVOLUTIONARY COMPUTATION 2014年第0期14卷 31-47页

作者： Helbig, Marde Engelbrecht, Andries R. Meraka Inst CSIR Scientia ZA-0184 Pretoria South Africa Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa

Most real-world optimisation problems are dynamic in nature with more than one objective, where at least two of these objectives are in conflict with one another. This kind of problems is referred to as dynamic multi-objective optimisation problems (DMOOPs). Most research in multi-objective optimisation (MOO) have focussed on static MOO (SMOO) and dynamic single-objective optimisation. However, in recent years, algorithms were proposed to solve dynamic MOO (DMOO). This paper provides an overview of the algorithms that were proposed in the literature to solve DMOOPs. In addition, challenges, practical aspects and possible future research directions of DMOO are discussed. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Population-based algorithms dynamic multi-objective optimization dynamic multi-objective optimization algorithms

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A Parallel Search System for dynamic multi-objective Traveling Salesman Problem

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Journal of Mathematics and System Science 2014年第5期4卷 295-314页

作者： Weiqi Li University of Michigan - Flint 303 E. Kearsley Street Flint M148502 U.S.A.

This paper introduces a parallel search system for dynamic multi-objective traveling salesman problem. We design a multi-objective TSP in a stochastic dynamic environment. This dynamic setting of the problem is very useful for routing in ad-hoc networks. The proposed search system first uses parallel processors to identify the extreme solutions of the search space for each ofk objectives individually at the same time. These solutions are merged into the so-called hit-frequency matrix E. The solutions in E are then searched by parallel processors and evaluated for dominance relationship. The search system is implemented in two different ways master-worker architecture and pipeline architecture.

关键词： dynamic multi-objective optimization traveling salesman problem parallel search algorithm solution attractor.

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A Novel Differential Evolution for dynamic multiobjective optimization with Adaptive Immigration Scheme 3

A Novel Differential Evolution for Dynamic Multiobjective Op...

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3rd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Wan, Shuzhen Wang, Diangang China Three Gorges Univ Sch Comp & Informat Technol Yichang Peoples R China DeZhou Highway Adm Bur Dept Engn Dezhou Peoples R China

ISBN: (纸本)9781479905614

dynamic multi-objective optimization(DMO) is a challenge research topic because the problems it involves are multi-objective and constantly change in time. Maintaining the diversity of the population in the evolutionary process is very important for DMO. When the diversity loss is too fast, tracing the Pareto Optimal Front(POF) will become very difficult. In this paper, an adaptive immigration scheme is proposed and integrated into a the DE algorithm for dynamic multi-objective optimization. The immigration scheme can improve the diversity of the population in the evolutionary process, so the proposed algorithm can work well in the dynamic multi-objective optimization environment. The proposed algorithm is tested against a variety of benchmark function types and its performance is compared to the NSGAII-B algorithm.

关键词： dynamic multi-objective optimization adaptive immigration scheme differential evolution

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