A survey of multicriteria optimization or the vector maximum problem, part I: 1776?1960

Stadler, W.

doi:10.1007/bf00932634

Cited by 211 publications

(83 citation statements)

References 26 publications

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“…Unlike single objective optimization, the solution to the MOP is not a single pointx, but a set of points known as Pareto set [4]. Such points are considered to be equally important solutions to the MOP in the sense that they have the property that moving from one point to another results in the improvement of one objective function while causing to degradation in the value of at least one of the remaining objective functions.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

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A Multi-objective Version of the Multiple Particle Collision Algorithm

Araújo¹,

Anochi²,

Velho³

2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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Abstract-A multi-objective version of a meta-heuristic, loosely inspired on the behaviour of particles inside a nuclear reactor, is presented. The Multi-objective Multiple Particle Collision Algorithm (MMPCA) uses the Pareto based fitness assignment, which uses the concept of dominance, to generate new solutions and build the Pareto set. The original population is duplicated, with purpose of classification and for applying the crowding distance approach. The latter procedures are also used in the NSGA-II.

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Section: Multi-objective Optimizationmentioning

confidence: 99%

“…An important concept in this context, used to define the Pareto set, is that of domination [4]. It is said that the solution x * dominates the solution x when x * is not worse than x for all the objective functions, and x * is better than x for at least one of the objective functions.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

A Multi-objective Version of the Multiple Particle Collision Algorithm

Araújo¹,

Anochi²,

Velho³

2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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“…Multi-objective optimization problems can be solved using the Pareto optimality technique, which was proposed by Edgeworth and Pareto [15], as defined below for the case of a minimization problem [16].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Multi-objective routing optimization using evolutionary algorithms

Yetgin

Cheung

Hanzo

2012

2012 IEEE Wireless Communications and Networking Conference (WCNC)

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Abstract-1 Wireless ad hoc networks suffer from several limitations, such as routing failures, potentially excessive bandwidth requirements, computational constraints and limited storage capability. Their routing strategy plays a significant role in determining the overall performance of the multi-hop network. However, in conventional network design only one of the desired routing-related objectives is optimized, while other objectives are typically assumed to be the constraints imposed on the problem. In this paper, we invoke the Non-dominated Sorting based Genetic Algorithm-II (NSGA-II) and the MultiObjective Differential Evolution (MODE) algorithm for finding optimal routes from a given source to a given destination in the face of conflicting design objectives, such as the dissipated energy and the end-to-end delay in a fullyconnected arbitrary multi-hop network. Our simulation results show that both the NSGA-II and MODE algorithms are efficient in solving these routing problems and are capable of finding the Pareto-optimal solutions at lower complexity than the 'brute-force' exhaustive search, when the number of nodes is higher than or equal to 10. Additionally, we demonstrate that at the same complexity, the MODE algorithm is capable of finding solutions closer to the Pareto front and typically, converges faster than the NSGA-II algorithm.

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“…Extensive literature reviews on multi-objective optimization are provided by White (1990) and Miettinen (1999). For a historical review of the origin and development of multi-criteria optimization, we refer to Stadler (1979). Surveys of concepts and methods of multi-objective optimization have also been done, e.g., by Chankong and Haimes (1983) and Steuer (1986).…”

Section: Introductionmentioning

confidence: 99%

Optimization and Analysis of Processes with Moving Materials Subjected to Fatigue Fracture and Instability

Баничук

Kurki

Neittaanmäki

et al. 2013

Mechanics Based Design of Structures and Machines

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We study safe conditions and process effectiveness of systems of moving materials from the viewpoint of failures including fracture and loss of stability. The web is modelled as a thin elastic plate made of brittle material, travelling between a system of supports at a constant velocity, and subjected to bending, in-plane tension and small initial cracks. We study crack growth under cyclic in-plane tension and transverse buckling of the web analytically. We seek optimal in-plane tension that maximizes a performance vector function consisting of the number of cycles before fracture, the critical velocity and process effectiveness. The present way of applying optimization in the studies of fracture and stability is new and affords an analytical tool for process analysis techniques.

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A survey of multicriteria optimization or the vector maximum problem, part I: 1776?1960

Cited by 211 publications

References 26 publications

A Multi-objective Version of the Multiple Particle Collision Algorithm

A Multi-objective Version of the Multiple Particle Collision Algorithm

Multi-objective routing optimization using evolutionary algorithms

Optimization and Analysis of Processes with Moving Materials Subjected to Fatigue Fracture and Instability

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