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Handbook of Optimization 2023 : Handbook of Formal Optimization Methods


When N/A
Where N/A
Submission Deadline Sep 30, 2022
Notification Due Oct 30, 2022
Final Version Due Nov 30, 2022
Categories    optimization   swarm intelligence   evolutionary computation   operation research

Call For Papers

Call For Chapters

‘Handbook of Formal Optimization Methods’ - SPRINGER

This is an open invitation to submit a chapter to one of the sections of the handbook. The chapter is expected to include background/literature review, method description including mathematical formulation, illustrations, problem(s) and application(s) solved, results and discussions, flowcharts/pseudocodes, etc.

Aim and Scope

Optimization carries great significance in both human affairs and the laws of nature. It refers to a positive and intrinsically human concept of minimization or maximization to achieve the best or most favorable outcome from a given situation. Besides, as the resources are becoming scarce, there is a need to develop new methods and techniques and modify the existing ones which will make the systems extract maximum from minimum use of these resources, i.e., maximum utilization of available resources with minimum investment or cost of any kind. The resources could be any, such as land, materials, machines, personnel, skills, time, etc. The handbook aims at discussing background/literature review, optimization method description including mathematical formulation, illustrations, problems and application(s), results and critical discussions, flowcharts/pseudocodes, etc. The handbook is expected to serve as a complete reference discussing a wide aspect of optimization methods. The handbook sections are given below.

Section I: Mathematical Optimization/Programming
Section II: Bayesian Optimization
Section III: Evolutionary Optimization-based Methods
Section IV: Bio-inspired Optimization
Section V: Swarm-based Optimization
Section VI: Physics-based Optimization
Section VII: Socio-inspired based Optimization
Section VIII: Machine Learning
Section IX: Neural Networks and Deep Learning
Section X: Multi/Many-objective Optimization
Section XI: Hybrid Optimization Methods
Section XII: Heuristics in Optimization
Section XIII: Goal Programming Problems and Methods
Section XIV: Combinatorial Optimization
Section XV: Genetic Algorithms and Applications
Section XVI: Engineering Optimization
Section XVII: Optimization in Management
Section XVIII: Optimization in Manufacturing Processes
Section XIX: Constraint Handling in Optimization Methods

Important Dates:
Chapter Proposal Jul 31, 2022
Full Chapter Submission: Sep 30, 2022
Chapter Review Notification: Oct 30, 2022
Revised Chapter Due: Nov 30, 2022
Final Acceptance: Dec 15, 2022
Expected Publication Date: Jan 30, 2023

Prof. Anand J Kulkarni, MIT World Peace University
Prof. Amir H Gandomi, University of Technology Sydney

If interested, please email a tentative title, author names, and affiliations to by July 31

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