A Population Learning Algorithm for Solving the Generalized Segregated Storage Problem
The paper presents a new population-based method, called population learning algorithm (PLA) for solving the Generalized Segregated Storage Problem (GSSP). PLA is an extension of population-based methods and adaptive memory programming techniques. It has been inspired by analogies to a social phenomenon rather than to a natural process. The paper introduces the GSSP, describes the concept of PLA and presents the application of PLA for solving GSSP. Computational experiment results are discussed in the final part of the paper.
KeywordsComputational Experiment Operational Research Society Discrete Uniform Distribution Adaptive Memory Storage Problem
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