Pareto Multi Objective Optimization

Ngatchou, P.N.; Zarei, Anahita; El-Sharkawi, M.A.

doi:10.1109/isap.2005.1599245

Cited by 373 publications

(240 citation statements)

References 22 publications

Supporting

Mentioning

224

Contrasting

Unclassified

Order By: Relevance

“…Performance improvement of one objective may result in performance degradation of other objectives, so it is very difficult or impossible to optimize multiple objectives simultaneously. The feasible solutions of multi-objective optimization problem form a Pareto [18] set. Generally speaking, the multi-objective optimization problem with n decision variables and m objective functions can be expressed as follows.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective Virtual Machine Placement for Load Balancing

Feng

2017

ITM Web Conf.

View full text Add to dashboard Cite

Abstract. The virtual machine placement is closely related to the efficient and balanced utilization of physical resources. In this paper, the influence of two scenarios about resource utilization on load balancing is analyzed. A multi-objective ant colony optimization algorithm is proposed to solve the virtual machine placement problem, which balances the load among physical machines and the internal load of physical machine simultaneously. The proposed algorithm is compared with two single objective ant colony optimization algorithms, first fit algorithm and greedy algorithm under some instances. The results show that the proposed algorithm can search and find solutions that exhibit good balance among objectives while others cannot. This demonstrates the proposed algorithm can balance the load in the process of mapping virtual machines to physical machines.

show abstract

Section: Multi-objective Optimizationmentioning

confidence: 99%

“…g i ( x) ≤ 0(i = 1,2, , p) defines p inequality constraints, and q equality constraints are defined by h j ( x) = 0( j = 1,2, ,q) .The following concepts [18,19] …”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Multi-objective Virtual Machine Placement for Load Balancing

Feng

2017

ITM Web Conf.

View full text Add to dashboard Cite

show abstract

“…For example, First Order Stochastic Dominance specifies that the set S Υ contains all functions increasing in z. In contrast, the sets of utility functions traditionally considered in Pareto dominance, P Υ , specify different weightings over different objectives (Ngatchou, Zarei, and El-Sharkawi 2005). Often these objectives are discrete criteria, such as "cost", "safety", "reliability"; such problems are often referred to as Multi-Criteria Decision Making or MCDM.…”

Section: Ii1a Comparison With Other Dominance Conceptsmentioning

confidence: 99%

Finding Common Ground When Experts Disagree: Robust Portfolio Decision Analysis

2017

View full text Add to dashboard Cite

Within the IEB framework, the Chair of Energy Sustainability promotes research into the production, supply and use of the energy needed to maintain social welfare and development, placing special emphasis on economic, environmental and social aspects. There are three main research areas of interest within the program: energy sustainability, competition and consumers, and energy firms. The energy sustainability research area covers topics as energy efficiency, CO2 capture and storage, R+D in energy, green certificate markets, smart grids and meters, green energy and biofuels. The competition and consumers area is oriented to research on wholesale markets, retail markets, regulation, competition and consumers. The research area on energy firms is devoted to the analysis of business strategies, social and corporative responsibility, and industrial organization. Disseminating research outputs to a broad audience is an important objective of the program, whose results must be relevant both at national and international level.The Chair of Energy Sustainability of the University of Barcelona-IEB is funded by the following enterprises ACS, CEPSA, CLH, Enagas, Endesa, FCC Energia, HC Energia, Gas Natural Fenosa, and Repsol) through FUNSEAM (Foundation for Energy and Environmental Sustainability). introducing an approach we call Robust Portfolio Decision Analysis. We introduce the idea of Belief Dominance as a prescriptive operationalization of a concept that has appeared in the literature under a number of names. We use this concept to derive a set of non-dominated portfolios; and then identify robust individual alternatives from the non-dominated portfolios. The Belief Dominance concept allows us to synthesize multiple conflicting sources of information by uncovering the range of alternatives that are intelligent responses to the range of beliefs. This goes beyond solutions that are optimal for any specific set of beliefs to uncover defensible solutions that may not otherwise be revealed. We illustrate our approach using a problem in the climate change and energy policy context: choosing among clean energy technology R&D portfolios. We demonstrate how the Belief Dominance concept can uncover portfolios that would otherwise remain hidden and identify robust individual investments.

show abstract

“…The solution of equation 24 produces a single result that is as good as the selection of the weights [43]. A Pareto set can be generated by evaluating a series of single-objective optimisation problems at different values of the weighting factor to avoid having to, a priori, select a particular weighting between objectives.…”

Section: Multi-objective Optimisationmentioning

confidence: 99%

A multi-objective optimisation model for a general polymer electrolyte membrane fuel cell system

Ang

Brett

Fraga

2010

Journal of Power Sources

View full text Add to dashboard Cite

This paper presents an optimisation model for a general polymer electrolyte membrane (PEM) fuel cell system suitable for efficiency and size trade-offs investigation. Simulation of the model for a base case shows that for a given output power, a more efficient system is bigger and vice versa. Using the weighting method to perform a multi-objective optimisation, the Pareto sets were generated for different stack output powers. A Pareto set, presented as a plot of the optimal efficiency and area of the membrane electrode assembly (MEA), gives a quantitative description of the compromise between efficiency and size. Overall, our results indicate that, to make the most of the size-efficiency trade-off behaviour, the system must be operated at an efficiency of at least 40% but not more than 47%. Furthermore, the MEA area should be at least 3 cm 2 per Watt for the efficiency to be practically useful. Subject to the constraints imposed on the model, which are based on technical practicalities, a PEM fuel cell system such as the one presented in this work cannot operate at an efficiency above 54%. The results of this work, specifically the multi-objective model, will form a useful and practical basis for subsequent techno-economic studies for specific applications.

show abstract

Pareto Multi Objective Optimization

Cited by 373 publications

References 22 publications

Multi-objective Virtual Machine Placement for Load Balancing

Multi-objective Virtual Machine Placement for Load Balancing

Finding Common Ground When Experts Disagree: Robust Portfolio Decision Analysis

A multi-objective optimisation model for a general polymer electrolyte membrane fuel cell system

Contact Info

Product

Resources

About