Название: Heuristics for Optimization and Learning: 906 (Studies in Computational Intelligence) Автор: Farouk Yalaoui, Lionel Amodeo, El-Ghazali Talbi
Издательство: Springer
Год: 2021
Страниц: 444
Язык: английский
Формат: pdf (true)
Размер: 12.9 MB
This book is a new contribution aiming to give some last research findings in the field of optimization and computing. This work is in the same field target than our two previous books published: “Recent Developments in Metaheuristics” and “Metaheuristics for Production Systems”, books in Springer Series in Operations Research/Computer Science Interfaces.
The challenge with this work is to gather the main contribution in three fields, optimization technique for production decision, general development for optimization and computing method and wider spread applications.
Chapter 18 is entitled “A New Cut-Based Genetic Algorithm for Graph Partitioning Applied to Cell Formation”. Cell formation is a critical step in the design of cellular manufacturing systems. M Boulif claims that the problem was tackled by using a cut-based-graph-partitioning model. This model meets real-life production systems requirements as it uses the actual amount of product flows, it looks for the suitable number of cells, and it takes into account the natural constraints such as operation sequences, maximum cell size, cohabitation and non-cohabitation constraints. The author proposes an original encoding representation to solve the problem by using a genetic algorithm.
Chapter 21 is entitled “A Cooperative Multi-Swarm Particle Swarm Optimizer Based Hidden Markov Model”. Particle swarm optimization (PSO) is a population-based stochastic metaheuristic algorithm; it has been successful in dealing with a multitude of optimization problems.
Chapter 23 is entitled “Auto-Scaling System in Apache Spark Cluster using Model-Based Deep Reinforcement Learning”. Real-time processing is a fast and prompt processing technology that needs to complete the execution within a limited time constraint almost equal to the input time. Executing such real-time processing needs an efficient auto-scaling system, which provides sufficient resources to compute the process within the time constraint. We use Apache Spark framework to build a cluster which supports real-time processing.
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