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PLA-based permutation-scheduling

100%

Jędrzejowicz J. , Jędrzejowicz P.

Foundations of Computing and Decision Sciences

2003

tom Vol. 28, No. 3

159-177

The paper proposes the implementation of population learning algorithm (PLA) for solving three well-known NP-hard permutation-scheduling problems. PLA is a recently developed method belonging to the class of the population-based algorithms. One of possible applications of the PLA is solving difficult optimization problems. The first of the discussed problems involves scheduling tasks on a single machine against common due date with earliness and tardiness penalties. The second is known as the permutation flow shop problem. The third one involves scheduling tasks on a single machine with total weighted tardiness as a criterion. To evaluate the proposed implementations computational experiments have been carried. They involved solving available sets of benchmark problems and comparing the results with the optimum or best-known solutions. PLA has found better upper bounds on several benchmark instances. Experiments have also helped to identify some behavioral characteristics of the proposed algorithms.

An application of population-based metaheuristics to minimizing schedule makespan

88%

Forkiewicz M. , Jędrzejowicz P.

Prace Instytutu Podstaw Informatyki Polskiej Akademii Nauk

2001

tom Nr 928

21-24

The paper investigates application of the three population-based metaheuristics - evolution algorithm, population learning algorithm and ant colony optimization to minimizing schedule makespan. All three approaches are briefly revieved. Their implementation to schedule makespan minimization in case of independent, non-preemptable tasks and multiple processors is described. The approach is evaluated by means of computational experiment, results of which are presented and discussed.

Application of the population learning algorithm to training feed-forward ANN

63%

Czarnowski I. , Jędrzejowicz P.

Prace Naukowe Politechniki Warszawskiej. Elektronika

2002

tom z. 139

147--161

The chapter investigates the application of the new metaheuristic called the population learning algorithm (PLA) to training feed-forward artificial neural networks. This chapter introduces the population learning algorithm and proposes several implementations developed with a view to training several benchmark neural networks. The approach is compared with two alternative methods of training: quick propagation and genetic programming. Computer experiments show the high effectiveness and good quality of the suggested approach.