Immune Systems in Multi-criterion Evolutionary Algorithm for Task Assignments in Distributed Computer System

Balicki, Jerzy

doi:10.1007/11495772_9

Cited by 7 publications

(8 citation statements)

References 6 publications

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“…The performances of MISA have been improved in a further work of the same authors [15]. Only in the last few years the AIS metaphor for multiobjective optimization has been investigated [16][17][18][19][20][21]. A good survey of these algorithms can be found in [22].…”

Section: Vector Immune Systemmentioning

confidence: 99%

Multiobjective design optimization and Pareto front analysis of a radial eddy current coupler

Canova

Freschi

2010

International Journal of Applied Electromagnetics and Mechanics

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The paper proposes a multiobjective optimization to design a radial eddy current coupler, where the torque is maximized while the momentum of inertia is minimized to improve the dynamic behavior of the device. An analytical solution of the electromagnetic problem is used to speed up the computation of objectives and an optimization algorithm based on the emulation of the immune system behavior, referred to as Vector Immune System, is adopted to find tradeoff solutions between objectives. The solutions found so far are compared with solutions obtained by a Genetic Algorithm with fuzzy scalarization of objectives. It shows a faster convergence with a better spread of trade-off solutions available for a higher level decision making. A deep analysis of these Pareto optimal configurations is also provided, trying to discover common properties of optimal solutions.

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Section: Vector Immune Systemmentioning

confidence: 99%

Multiobjective design optimization and Pareto front analysis of a radial eddy current coupler

Canova

Freschi

2010

International Journal of Applied Electromagnetics and Mechanics

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“…In this paper, we suggest introducing a ranking procedure to calculate fitness of antibodies and then to select the winner. In the previous works, a ranking idea for nondominated individuals has been applied to avoid the prejudice of the interior Pareto alternatives [1,3]. Now, we adjust this procedure to the negative selection algorithm (NSA*).…”

Section: Ranking Procedures For Negative Selectionmentioning

confidence: 99%

“…To test the ability of NSA* for handling constraints, we consider a multi-criterion optimisation problem for task assignment in a distributed computer system [1].…”

Section: Task Assignment In Distributed Computer Systemmentioning

confidence: 99%

“…Finding allocations of program modules may decrease the total time of a program execution by taking a benefit of the particular properties of some workstations or an advantage of the computer load. An adaptive evolutionary algorithm has been considered for solving multi-objective optimisation problems related to task assignment that minimize Z max -a workload of a bottleneck computer and F 2 -the cost of machines [1]. The total numerical performance of workstations is another criterion for assessment the quality of a task assignment and it has been involved to multi-criterion problem formulated in [1].…”

Section: Task Assignment In Distributed Computer Systemmentioning

confidence: 99%

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Negative Selection with Ranking Procedure in Tabu-Based Multi-criterion Evolutionary Algorithm for Task Assignment

Balicki

2006

Computational Science – ICCS 2006

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In this paper, an improved negative selection procedure to handle constraints in a multi-criterion evolutionary algorithm has been proposed. The problem that is of interest to us is the complex task assignment for a distributed computer system. Both a workload of a bottleneck computer and the cost of system are minimized; in contrast, a reliability of the system is maximized. Moreover, constraints related to memory limits and computer locations are imposed. Finally, an evolutionary algorithm with tabu search procedure and the improved negative selection is proposed to provide effective solutions.

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“…The performance of MISA has been improved in further work of the same authors (Coello Coello and Cruz Cortés 2005). Only in the last few years has the AIS metaphor been investigated for multi-objective optimization (Luh and Chueh 2004, Balicki 2005, Jiao et al 2005.…”

Section: Introductionmentioning

confidence: 97%

VIS: An artificial immune network for multi-objective optimization

Freschi¹,

Repetto²

2006

Engineering Optimization

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The aim of this work is to propose and validate a novel multi-objective optimization algorithm based on the emulation of the behaviour of the immune system. The rationale of this work is that the artificial immune system has, in its elementary structure, the main features required by other multi-objective evolutionary algorithms described in the literature, such as diversity preservation, memory, adaptivity, and elitism. The proposed approach is compared with three multi-objective evolutionary algorithms that are representative of the state of the art in multi-objective optimization. Algorithms are tested on six standard problems (both unconstrained and constrained) and comparisons are carried out using three different metrics. Results show that the proposed approach has very good performances and can become a valid alternative to standard algorithms for solving multi-objective optimization problems.

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Immune Systems in Multi-criterion Evolutionary Algorithm for Task Assignments in Distributed Computer System

Cited by 7 publications

References 6 publications

Multiobjective design optimization and Pareto front analysis of a radial eddy current coupler

Multiobjective design optimization and Pareto front analysis of a radial eddy current coupler

Negative Selection with Ranking Procedure in Tabu-Based Multi-criterion Evolutionary Algorithm for Task Assignment

VIS: An artificial immune network for multi-objective optimization

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