Handbook of AI-based Metaheuristics

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advanced metaheuristic applications
Age Group_Uncategorized
Age Group_Uncategorized
Algorithm
automatic-update
B01=Anand J. Kulkarni
B01=Patrick Siarry
BCO Algorithm
Benchmark Functions
Benchmark Instances
Bio-inspired Methods
Category1=Non-Fiction
Category=UGC
computational intelligence
Computational Results
constraint handling techniques
COP=United Kingdom
Cultural Algorithm
Delivery_Pre-order
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
evolutionary computation
Firefly Algorithm
GA
Glowworm Swarm Optimization
graduate level resource
Gravitational Search Algorithm
GWO
Honey Bees
HS
Imperialist Competitive Algorithm
Knapsack Problem
Language_English
Metaheuristic Algorithms
Multi-objective Optimization
Multi-objective Optimization Problems
Optimization
Optimization Problem
PA=Not yet available
Pareto Front
Physics and Chemistry-based Methods
Pressure Vessel Design Problem
Price_€50 to €100
PS=Forthcoming
Si Algorithm
Socio-inspired Methods
softlaunch
stochastic optimisation
structural design algorithms
Swarm-based Methods
Teaching Learning Based Optimization
Variable Neighborhood Search Algorithm
VRP
Welded Beam Design Problem

Product details

  • ISBN 9780367755355
  • Weight: 820g
  • Dimensions: 174 x 246mm
  • Publication Date: 04 Oct 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
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At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to artificial intelligence (AI)-based, nature-inspired solution methodologies or algorithms.

The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural, and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications; and newly devised metaheuristic algorithms.

This will be a valuable reference for researchers in industry and academia, as well as for all Master’s and PhD students working in the metaheuristics and applications domains.

Patrick Siarry is a Professor of Automatics and Informatics at the University of Paris-Est Créteil, where he leads the Image and Signal Processing team in the Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi).

Anand J Kulkarni is Associate Professor at the Symbiosis Center for Research and Innovation, Symbiosis International (Deemed University).