Conventional community detection approaches in complex network are based on the optimization of a priori decision,i.e.,a single quality function designed *** paper proposes a posteriori decision approach for community...
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Conventional community detection approaches in complex network are based on the optimization of a priori decision,i.e.,a single quality function designed *** paper proposes a posteriori decision approach for community *** approach includes two phases：in the search phase,a special multi-objective evolutionary algorithm is designed to search for a set of tradeoff partitions that reveal the community structure at different scales in one run;in the decision phase,three model selection criteria and the Possibility Matrix method are proposed to aid decision makers to select the preferable solutions through differentiating the set of optimal solutions according to their *** experiments in five synthetic and real social networks illustrate that,in one run,our method is able to obtain many candidate solutions,which effectively avoids the resolution limit existing in priori decision *** addition,our method can discover more authentic and comprehensive community structures than those priori decision approaches.
Different constraint handling techniques have been used with multi-objective evolutionary algorithms (MOEA) to solve constrained multi-objective optimization problems. It is impossible for a single constraint handling...
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Different constraint handling techniques have been used with multi-objective evolutionary algorithms (MOEA) to solve constrained multi-objective optimization problems. It is impossible for a single constraint handling technique to outperform all other constraint handling techniques always on every problem irrespective of the exhaustiveness of the parameter tuning. To overcome this selection problem, an ensemble of constraint handling methods (ECHM) is used to tackle constrained multi-objective optimization problems. The ECHM is integrated with a multi-objective differential evolution (MODE) algorithm. The performance is compared between the ECHM and the same single constraint handling methods using the same MODE (using codes available from http://***/home/EPNSugan/***). The results show that ECHM overall outperforms the single constraint handling methods.
In the field of evolutionarymulti-criterion optimization, the hypervolume indicator is the only single set quality measure that is known to be strictly monotonic with regard to Pareto dominance: whenever a Pareto set...
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In the field of evolutionarymulti-criterion optimization, the hypervolume indicator is the only single set quality measure that is known to be strictly monotonic with regard to Pareto dominance: whenever a Pareto set approximation entirely dominates another one, then the indicator value of the dominant set will also be better. This property is of high interest and relevance for problems involving a large number of objective functions. However, the high computational effort required for hypervolume calculation has so far prevented the full exploitation of this indicator's potential;current hypervolume-based search algorithms are limited to problems with only a few objectives. This paper addresses this issue and proposes a fast search algorithm that uses Monte Carlo simulation to approximate the exact hypervolume values. The main idea is not that the actual indicator values are important, but rather that the rankings of solutions induced by the hypervolume indicator. In detail, we present HypE, a hypervolume estimation algorithm for multi-objective optimization, by which the accuracy of the estimates and the available computing resources can be traded off;thereby, not only do many-objective problems become feasible with hypervolume-based search, but also the runtime can be flexibly adapted. Moreover, we show how the same principle can be used to statistically compare the outcomes of different multi-objective optimizers with respect to the hypervolume so far, statistical testing has been restricted to scenarios with few objectives. The experimental results indicate that HypE is highly effective for many-objective problems in comparison to existing multi-objective evolutionary algorithms. HypE is available for download at http://***/sop/download/supplementary/hype/.
A methodology for achieving the best-fit set of parameters for a Mach-Zehnder interferometer with a semiconductor optical amplifier (MZI-SOA) static model is proposed. A multi-objective genetic algorithm is exploited ...
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A methodology for achieving the best-fit set of parameters for a Mach-Zehnder interferometer with a semiconductor optical amplifier (MZI-SOA) static model is proposed. A multi-objective genetic algorithm is exploited and the quality of the approach is validated by applying it in an existing sample. Optimisation of performance and determination of operational limits are enabled by the proposed methodology and good agreement was obtained between simulated and practical results.
The *** Task Parallel Library is the latest tool developed for multicore parallelism optimization using the .NET technology. It is a managed concurrency library that provides optimized managed code for multicore proce...
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The *** Task Parallel Library is the latest tool developed for multicore parallelism optimization using the .NET technology. It is a managed concurrency library that provides optimized managed code for multicore processors using a new thread pool that withstands cancellation, waiting and pool isolation, among many other features. The Task Parallel Library also uses dynamic work stealing techniques for superior scalability. This paper analyzes the performance improvement of using the Task Parallel Library of *** when applying a multi-objective evolutionary algorithm to solve a timetabling problem. For comparative purposes, this algorithm has also been parallelized using threads. The results obtained show that both alternatives allow a reduction in the runtime necessary to solve this problem. However, parallelizing the code using the Task Parallel Library of *** has the advantage of being easier and the code size is much smaller than directly programming threads. (C) 2010 Civil-Comp Ltd and Elsevier Ltd. All rights reserved.
Introduction: Drug discovery and development is a typical multi-objective problem and its successes or failures depend on the simultaneous control of numerous, often conflicting, molecular and pharmacological properti...
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Introduction: Drug discovery and development is a typical multi-objective problem and its successes or failures depend on the simultaneous control of numerous, often conflicting, molecular and pharmacological properties. multi-objective optimization strategies represent a new approach to capture the occurrence of varying optimal solutions based on trade-offs among the objectives taken into account. In view of this, multi-objective optimization aims to discover a set of satisfactory compromises that may in turn be used to find the global optimal solution by optimizing numerous dependent properties simultaneously. Areas covered: The authors review the potential of multi-objective strategies in a number of fields including: drug library design;substructure mining;the derivation of quantitative structure-activity relationship models;ranking of docking poses. The authors also discuss the potential of multi-objective strategies in controlling competing properties for absorption, distribution, metabolism and elimination/toxicity optimization. Expert opinion: It is very clear to those who work in drug discovery and development that the success of rational drug design is largely dependent on the control of a number of, often conflicting, objectives. Therefore, multi-objective optimization methods, which have recently been introduced to the field of molecular discovery, represent the ultimate frontier in chemoinformatics. The widespread use of these multi-objective techniques has provided new opportunities in medicinal chemistry as seen through its use in a number of applications for chemoinformatics both within academia and the pharmaceutical industry.
The global best (gbest) or local best (lbest) of every particle in state-of-the-art multi-objective particle swarm optimization (MOPSO) implementations is selected from the non-dominated solutions in the external arch...
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The global best (gbest) or local best (lbest) of every particle in state-of-the-art multi-objective particle swarm optimization (MOPSO) implementations is selected from the non-dominated solutions in the external archive. This approach emphasizes the elitism at the expense of diversity when the size of the current set of non-dominated solutions in the external archive is small. This article proposes that the gbests or lbests should be chosen from the top fronts in a non-domination sorted external archive of reasonably large size. In addition, a novel two local bests (lbest) based MOPSO (2LB-MOPSO) version is proposed to focus the search around small regions in the parameter space in the vicinity of the best existing fronts unlike the current MOPSO variants in which the pbest and gbest (or lbest) of a particle can be located far apart in the parameter space thereby potentially resulting in a chaotic search behaviour. Comparative evaluation using 19 multi-objectives test problems and 11 state-of-the-art multi-objective evolutionary algorithms ranks overall the 2LB-MOPSO as the best while two state-of-the-art MOPSO algorithms are ranked the worst with respect to other multi-objective evolutionary algorithms.
Optimising pump scheduling is a complex problem, which involves a large space search, continuous and discrete variables, physical and operational constraints and also multi-objectives. In this paper, multi-objective e...
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Optimising pump scheduling is a complex problem, which involves a large space search, continuous and discrete variables, physical and operational constraints and also multi-objectives. In this paper, multi-objective evolutionary algorithms (MOEAs) combined with a repair mechanism are used to solve the optimal operation problem within water supply system. In this work two objectives are minimised: operation cost (energy cost + treatment cost) and maintenance cost, while one objective is maximised: service level of hydraulic. Decision variables are the settings of the pumps and speed ratio of variable-speed pumps at a time step of the total operational time horizon. A mixed coding methodology and a new crossover operator are developed according to the characteristics of decision variables. Three well-known MOEAS (NSGA-II, epsilon-MOEA and SPEA2) are implemented and compared. Practical application of this method shows that it can make efficient decision to support the operators.
Software dependability modelling involves simultaneous consideration of several incompatible and often conflicting objectives, while hypervolume-based multi-objective evolutionary algorithm (MOEA) has been shown to pr...
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Software dependability modelling involves simultaneous consideration of several incompatible and often conflicting objectives, while hypervolume-based multi-objective evolutionary algorithm (MOEA) has been shown to produce better results for multi-objective problem in practice. A frame of reliability model optimization with hypervolume based MOEA is presented. Focusing on the key issue of hypervolume based MOEA, a new algorithm, set hypervolume contribution by slicing objective (SHSO), is proposed for calculating the exclusive hypervolume contribution of a subset to the whole nondominated set directly for small dimension. For the special case of SHSO, CHSO (the contribution of a point to hypervolume by slicing objective) is improved with heuristics. The feasibility and efficiency of developed algorithms are shown by experiments.
This paper presents an evolutionaryalgorithm for analyzing the best mix of distributed generations (DG) in a distribution network. The multi-objective optimization aims at minimizing the total cost of real power gene...
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ISBN:
(纸本)9781467325950
This paper presents an evolutionaryalgorithm for analyzing the best mix of distributed generations (DG) in a distribution network. The multi-objective optimization aims at minimizing the total cost of real power generation, line losses and CO_2 emissions, and maximizing the benefits from the DG over a 20 years planning horizon. The method assesses the fault current constraint imposed on the distribution network by the existing and new DG in order not to violate the short circuit capacity of existing switchgear. The analysis utilizes one of the highly regarded evolutionaryalgorithm, the Strength Pareto evolutionaryalgorithm 2 (SPEA2) for multi-objective optimization and MATPOWER for solving the optimal power flow problems.
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