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Heuristics for Optimization and Learning de Farouk Yalaoui

Heuristics for Optimization and Learning: Studies in Computational Intelligence, cartea 906

Editat de Farouk Yalaoui, Lionel Amodeo, El-Ghazali Talbi
en Limba Engleză Paperback – 17 dec 2021
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. 
The number of researches dealing with decision maker tool and optimization method grows very quickly these last years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields.
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Specificații

ISBN-13: 9783030589325
ISBN-10: 3030589323
Ilustrații: XV, 442 p. 168 illus., 97 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.64 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Process Plan Generation for Reconfigurable Manufacturing Systems: Exact vs Evolutionary-Based Multi-Objective Approaches.- On VNS-GRASP and Iterated Greedy Metaheuristics for Solving Hybrid Flow Shop Scheduling Problem with Uniform Parallel Machines and Sequence Independent Setup Time.- A Variable Block Insertion Heuristic for the Energy-Efficient Permutation Flowshop Scheduling with Makespan Criterion.- Solving 0-1 Bi-Objective Multi-Dimensional Knapsack Problems using Binary Genetic Algorithm.- An asynchronous parallel evolutionary algorithm for solving large .instances of the multi-objective QAP.- Learning from Prior Designs for Facility Layout Optimization.- Single-objective Real-parameter Optimization: Enhanced LSHADE-SPACMA Algorithm.- Operations Research at Bulk Terminal: A Parallel Column Generation Approach.- Heuristic solutions for the (α,β)-k feature set problem.- Generic Support for Precomputation-Based Global Routing Constraints in Local Search Optimization.- Dynamic Simulated Annealing with Adaptive Neighborhood using Hidden Markov Model.- Hybridization of the differential evolution algorithm for continuous multi-objective optimization.- A Steganographic Embedding Scheme Using Improved-PSO Approach.- Algorithms towards the Automated Customer Inquiry Classification.-  An heuristic scheme for a reaction advection diffusion equation.- Stock Market Speculation System Development based on Technico Temporal indicators and Data Mining Tools.- A New Hidden Markov Model Approach for Pheromone Level Exponent Adaptation in Ant Colony System.- A new cut-based genetic algorithm for graph partitioning applied to cell formation.-  Memetic algorithm and evolutionary operators for multi-objective matrix tri-factorization problem.- Quaternion simulated annealing.- A Cooperative Multi-Swarm Particle Swarm Optimizer Based Hidden Markov Model.- Experimental Sensitivity Analysis of Grid-Based Parameter Adaptation Method.-  Auto-Scaling System in Apache Spark Cluster using Model-Based Deep Reinforcement Learning.- Innovation Networks from Inter-Organizational Research Collaborations.- Assessing Film Coefficients of Microchannel Heat Sinks via Cuckoo Search Algorithm.- One-Class Subject Authentication using Feature Extraction by Grammatical Evolution on Accelerometer Data.- Semantic composition of word-embeddings with genetic programming.- New Approach for Continuous and Discrete Optimization: Optimization by Morphological Filters.

Textul de pe ultima copertă

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. 
The number of researches dealing with decision maker tool and optimization method grows very quickly these last years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields.

Caracteristici

Presents recent research on Heuristics for Optimization and Learning Gathers contributions in optimization technique for production decision, general development for optimization and computing methods, and wider spread applications Edited results of the 7th International Conference on Metaheuristics and Nature Inspired Computing META’18, held at Marrakech, Morocco October 27–31, 2018 Written by experts in the field