Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Foundations of Computing and Decision Sciences

2 2000

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: multiple objective metaheuristics

Sortuj według:

Ogranicz wyniki do:

Adaptive evolutionary algorithms for multiobjective task assignments in distributed computer system

Balicki J.

Foundations of Computing and Decision Sciences

2000

Vol. 25, No. 4

231-248

A task assignment in a distributed computer system may reduce the total cost of a program execution and the workload of a bottleneck computer. It can decrease the cost of computers because of the computer sort selection, too. A total amount of system performance is another measure that can be minimized by task scattering. A problem of task allocation is formulated as a multiobjective combinatorial optimization question, which is solved by three evolutionary approaches: a tuned genetic algorithm with ranking procedure, an adaptive evolutionary algorithm, and an evolution strategy. They are applied for finding the subset of Pareto-optimal solutions. Finally, two evolutionary approaches are recommended for finding efficient task assignments.

A systematic comparison of performance of various multiple objective metaheuristics using a common set of analytical test functions

Collette Y., Siarry P., Wong H.-I.

Foundations of Computing and Decision Sciences

2000

Vol. 25, No. 4

249-271

Many multiple objective optimization algorithms have been described in the literature. Some of them use a "metaheuristic" (genetic algorithm, simulated annealing, tabu search and so on) that allow, in principle, to avoid getting trapped into a local minimum of an objective function. We feel that this approach can be advantageously extended to a large set of multiple objective optimization methods. Moreover, it is interesting to perform a systematic comparison between performances of various multiple objective metaheuristics. Such a comparison needs, on the one hand, to adopt a common set of test functions and, on the other hand, to use a common set of performance criteria. In this study, we propose to compare various metaheuristics associated with various multiple objective optimization methods (such as weighted sum of objective functions, goal programming, distance method and so on). These different couples are evaluated using a set of classical test functions. The set of test functions is chosen so as to represent most of the difficulties (multifrontality, discontinuity, non-convexity and so on) that can be met in engineering when handling real multiple objective optimization problems.