posted by user: imanrahimi || 4006 views || tracked by 4 users: [display]

Elsevier 2020 : Call for Elsevier book chapter proposal: Multi-Objective Combinatorial Optimization Problems and Solution Methods

FacebookTwitterLinkedInGoogle

Link: https://cutt.ly/jtgsDNj
 
When Mar 14, 2020 - Feb 16, 2020
Where Elsevier
Submission Deadline May 15, 2020
Notification Due May 30, 2020
Final Version Due Jul 15, 2020
Categories    optimization   combinatorial problems   multi-objective   solution
 

Call For Papers

Call for book chapter proposal (Elsevier):
Editors:
-Professor Mehdi Toloo,
Technical University of Ostrava, Ostrava, Czech Republic (mehdi.toloo@vsb.cz)
University of Torino, Torino, Italy (mehdi.toloo@unitio.it)
Sultan Qaboos University, Muscat, Oman (m.toloo@squ.edu.om)

-Professor Siamak Talatahari,
University of Tabriz, Tabriz, Iran and Near East University, North Cyprus, Turkey (Talatahari@tabrizu.ac.ir, Siamak.Talat@gmail.com).

-Dr. Iman Rahimi,
Universiti Putra Malaysia, Malaysia, and Young Researchers and Elite Club, Iran (iman83@gmail.com).

Project summary:
Combinatorial optimization problems appear in a wide range of applications in operations research, engineering, biological sciences and computer science. Many combinatorial optimization approaches have been developed that link the discrete universe to the continuous universe through geometric, analytic, and algebraic techniques.
Optimization problems with multi-objective arise in a natural fashion in most disciplines and their solution has been a challenge to researchers for a long time. Despite the considerable variety of techniques developed in Operations Research (OR) and other disciplines to tackle these problems, the complexities of their solution calls for alternative approaches.
In this book, we will discuss the results of a recent multi-objective combinatorial optimization achievement considering metaheuristic, mathematical programming, heuristic, hyper heuristic, and hybrid approaches. In other words, this book intends to show a diversity of various multi-objective combinatorial optimization issues that may benefit from different methods in theory and practice.
-A non-exhaustive list of topics we invite to be considered for inclusion in this book are as follows:
1. Basic concepts of combinatorial optimization
Chapter 1 presents and motivates MOP terminology and the nomenclature used in successive chapters including a lengthy discussion on theimpact of computational limitations on finding the Pareto front along with insight to MOP concepts.
2. Random methods for combinatorial optimization problems
2.1. Metaheuristic
2.1.1. Population-based methods
2.1.1.1. Multi-objective Evolutionary Algorithm (MOEA) Approaches
MOEA developmental history has proceeded in a number of ways from aggregated forms of single-objective Evolutionary Algorithms (EAs) to true multiobjective approaches and their extensions. Each MOEA is presented with historical and algorithmic insight. Being aware of the many facets of historical multiobjective problem solving provides a foundational understanding of the discipline.
2.1.1.2. Multi-objective swarm intelligence algorithms
Multi-objective particle swarm optimization, multi-objective ant colony optimization,…
2.1.2. Trajectory methods
Simulated annealing, Tabu search,…
2.1.3. Coevolution and hybrid of MOEA Local Search
Both coevolutionary MOEAs and hybridizations of MOEAs with local search procedures are covered.

2.2. Heuristic algorithms
2.2.1. Improvement heuristics
2.2.2. Constructive heuristics
2.3. Relaxation algorithms
E.g. Lagrangian relaxation
2.4. Decomposition algorithms
Benders decomposition algorithm
2.5. Column generation
3. Enumerate methods for combinatorial optimization problems
E.g. Dynamic programming
4. Deterministic methods for combinatorial optimization problems
4.1. Linear programming methods
Goal programming,…
4.2. Branching algorithms
This chapter presents well-known branching algorithms such as branch&cut, branch&price, branch& bound.
5. Many-objective combinatorial optimization problems
This chapter presents multi-objective combinatorial optimization in the case of more than three objectives along with solution approaches.
Schedule

Chapter proposals  May 15, 2020
Decisions from editors  May 30, 2020
Full submission of chapters  July 15, 2020
Feedback of reviews  October 31, 2020
Revised chapter submission  November 30, 2020
Final acceptance notifications  December 30, 2020

Please submit your proposal here:https://cutt.ly/jtgsDNj

Related Resources

FCST 2021   International Conference on Foundations of Computer Science & Technology
COVID19_Book 2020   Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 (Elsevier book)
IEEE DSLW 2021   IEEE Data Science and Learning Workshop
MIF-SHAI (INFFUS) 2020   Special Issue on Multi-Source Information Fusion for Smart Health with AI, Information Fusion, Elsevier
SCOPUS-CSMO 2021   5th International Conference on Simulation, Modeling and Optimization (CSMO 2021)
Call for Book Chapter 2020   Implementing Data Analytics and Architectures for Next Generation Wireless Communications
EuroGP 2021   The 24th European Conference on Genetic Programming
JISA-CYBER-SEC 2020   Special Issue on Trends in Cybersecurity, Journal of Information Security and Applications, Elsevier
WCN 2022   Wireless Communications and Networking: Theory and Practice
JAAMAS MODeM SI 2021   Special Issue of JAAMAS on Multi-Objective Decision Making (MODeM)