A Cooperative Coevolution UMDA for the Machine-Part Cell Formation.
ABSTRACT The machine-part cell formation is a NP- complete combinationaloptimization problem in cellular manufacturing system. Past
research has shown that although the genetic algorithm (GA) can get high quality solutions, special selection strategy, crossover
and mutation operators as well as the parameters must be defined previously to solve the problem efficiently and flexibly.
The Estimation of Distribution Algorithms (EDAs) can get the same or better solutions with less operators and parameters,
but the EDAs need more function evaluations than that of the GA. In this paper, a Cooperation Coevolution UMDA is proposed
to solve the machine-part cell formation problem. Simulation results on six well known problems show that the Cooperation
Coevolution UMDA can solve the machine-part cell formation problem more effectively and efficiently.
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ABSTRACT: A general model for the coevolution of cooperating species is presented. This model is instantiated and tested in the domain of function optimization, and compared with a traditional GA-based function optimizer. The results are encouraging in two respects. They suggest ways in which the performance of GA and other EA-based optimizers can be improved, and they suggest a new approach to evolving complex structures such as neural networks and rule sets. 1 Introduction Genetic algorithms (GAs), originally conceived by Holland , represent a fairly abstract model of Darwinian evolution and biological genetics. They evolve a population of competing individuals using fitness-biased selection, random mating, and a gene-level representation of individuals together with simple genetic operators (typically, crossover and mutation) for modeling inheritance of traits. These GAs have been successfully applied to a wide variety of problems including multimodal function optimization, machine learn...
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ABSTRACT: The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range of problems, including neural network training. This paper presents a variation on the traditional PSO algorithm, called the cooperative particle swarm optimizer, or CPSO, employing cooperative behavior to significantly improve the performance of the original algorithm. This is achieved by using multiple swarms to optimize different components of the solution vector cooperatively. Application of the new PSO algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional PSO.IEEE Transactions on Evolutionary Computation 07/2004; 8(3-8):225 - 239. DOI:10.1109/TEVC.2004.826069 · 5.55 Impact Factor
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ABSTRACT: The design of a cellular manufacturing system requires that a part population, at least minimally described by its use of process technology (part/machine incidence matrix), be partitioned into part families and that the associated plant equipment be partitioned into machine cells. At the highest level, the objective is to form a set of completely autonomous units such that inter-cell movement of parts is minimized. We present an integer program that is solved using a genetic algorithm (GA) to assist in the design of cellular manufacturing systems. The formulation uses a unique representation scheme for individuals (part/machine partitions) that reduces the size of the cell formation problem and increases the scale of problems that can be solved. This approach offers improved design flexibility by allowing a variety of evaluation functions to be employed and by incorporating design constraints during cell formation. The effectiveness of the GA approach is demonstrated on several proble...IIE Transactions 03/1996; 28(1). DOI:10.1080/07408179608966253 · 1.06 Impact Factor