Special Session on
Evolutionary Computation for Combinatorial Optimization Problems
Aim and Scope
Combinatorial optimization problems (COPs) are ubiquitous in science, engineering, and industry, such as scheduling, routing, packing, assignment, and design. These problems involve finding an optimal or near-optimal arrangement or selection of a finite number of discrete elements, subject to some constraints and objectives. COPs are often NP-hard, meaning that there is no known polynomial-time algorithm to solve them exactly. Therefore, heuristic and metaheuristic methods are widely used to find good solutions in a reasonable amount of time.
Evolutionary computation (EC) is a family of metaheuristic methods inspired by natural evolution, such as genetic algorithms, genetic programming, evolutionary strategies, particle swarm optimization, ant colony optimization, differential evolution, and multi-objective optimization. EC methods have been successfully applied to many COPs, showing their effectiveness, flexibility, and robustness. However, there are still many challenges and open issues in applying EC to COPs, such as scalability, diversity, constraint handling, hybridization, parallelization, and dynamic adaptation.
The aim of this special session is to provide a forum for researchers and practitioners to exchange the latest advances and challenges in applying EC to COPs, covering both theoretical and practical aspects. The session will also foster collaboration and discussion among the EC and COP communities and identify new directions and opportunities for future research.
Related topics:
The topics of interest include but are not limited to the following:
- EC methods for solving COPs
- Novel representations, operators, and algorithms for EC applied to COPs
- Theoretical analysis and performance evaluation of EC methods for COPs
- Benchmarking and comparison of EC methods and other metaheuristics for COPs
- Hybridization of EC methods with other techniques, such as local search, machine learning, and mathematical programming, for COPs
- Parallel and distributed EC methods for COPs
- Adaptive and self-tuning EC methods for COPs
- EC methods for dynamic, uncertain, and noisy COPs
- EC methods for large-scale and complex COPs
- EC methods for multi-objective and many-objective COPs
- EC methods for constrained and multi-constrained COPs
- EC methods for real-world applications of COPs, such as scheduling, routing, packing, assignment, and design
Paper Submission
Potential authors may submit their manuscripts for presentation consideration through ICETCI 2024 submission system electronically at https://edas.info/N31894 , following the conference guidelines. All submissions will go through peer review process. To submit your paper to this special session, you have to choose our special session title on the submission page.
Important Dates
| Paper submission deadline: | Apr 15, 2024 |
| Paper acceptance notification date: | Jun 27, 2024 |
| Final paper submission and early registration deadline: | Jul 10, 2024 |
Organizers
Prof. Pramod Kumar Singh
ABV – Indian Institute of Information Technology and Management Gwalior, Madhya Pradesh, India - 474015
E-mail:pksingh@iiitm.ac.in
He has more than 33 years of experience in academics, research, and academic administration. He completed his B. Tech. in Computer Science and Engineering from Kamla Nehru Institute of Technology Sultanpur in 1989 and his M. Tech. in Computer and Information Technology and Ph.D. from Indian Institute of Technology Kharagpur in 1998 and 2008, respectively. His Ph.D. thesis work was focused on enhancing the quality of solutions to hard multi-objective combinatorial optimization problems using multi-objective evolutionary algorithms. Presently, his areas of interest/research are Nature-Inspired Computing, Evolutionary Multi-Objective Optimization, Data Mining, Machine/Deep Learning, Natural Language Processing, and Health Informatics.
He is a recipient of the Merit scholarship of the Government of Uttar Pradesh for two years (1983 - 1985). He has approximately ninety publications in journals and conferences published by IEEE, Elsevier, Springer, and ACM; five edited books, and four book chapters published in Springer. He has been an Associate Editor of the journal - International Journal of Swarm Intelligence - published by Inderscience and is a reviewer for various reputed journals, including IEEE Transactions. Besides being a member of the Technical Program Committees (TPCs) and chairing sessions in various international conferences in India and abroad, he has been General Chair, Technical Session Chair, Publication Chair, and Member advisory board of many international conferences. Additionally, he has been a reviewer of many books published by McGraw Hill related to his research/work interest and contributor to the book entitled “Introduction to Data Mining,” authored by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar and published by Pearson.
He has served his institute in many administrative responsibilities also, e.g., Faculty In-charge, Academics; Chairman, Library Committee; Faculty Coordinator, M. Tech. (Advanced Networking); faculty In-charge, Masters' Thesis Project; Warden (Boys' Hostels). He has been Dean of Academic Affairs and Dean of Faculty Affairs at ABV-IIITM Gwalior.
Dr. Avadh Kishor
ABV – Indian Institute of Information Technology and Management Gwalior, Madhya Pradesh, India - 474015
E-mail:akishor@iiitm.ac.in
Moving forward, Dr. Kishor pursued a Ph.D. at IIT Roorkee, specializing in Algorithmic Game Theory. His research focused on the intersection of computer science, game theory, and optimization, particularly in multi-agent systems (MAS). MAS, a sub-field of artificial intelligence, explores the computational aspects of distributed, rational decision-making. Game theory studies the behaviour of self-interested agents in situations of strategic interdependence. At the same time, optimization aims to develop computational methodologies for maximizing objectives subject to constraints.
Dr. Kishor's work aims to derive optimal system-wide decisions in a distributed environment with multiple agents, each possessing local information, capabilities, and individual goals. His Ph.D. dissertation concentrated on the load balancing problem in a distributed environment, emphasizing objectives such as minimizing user response time, reducing costs, ensuring fair resource utilization, and decreasing energy consumption. Dr. Kishor formulated these load-balancing problems as game-theoretic models, utilizing cooperative and non-cooperative games. He characterized the Nash equilibrium for each game. He proposed four distinct distributed algorithms, resulting in seven publications during his Ph.D., including four Q1 Journals and two IEEE Transactions papers.
Over the past decade, Dr. Kishor has dedicated himself to developing optimization and game theoretic solutions for global challenges. His impressive track record includes 23 research articles published in 12 journals, 10 international conferences, and 1 workshop, accumulating 333 citations (according to Google Scholar). Dr. Kishor guides four Ph.D. students, two focusing on developing electric vehicle charging management strategies using game theory. His research impact extends beyond academia, with one research scholar working on charge scheduling for electric vehicles and an ongoing project titled "A Techno-Economic System Development for Smart Electric Vehicle Charging Solution" progressing for approximately one year.
He is a reviewer of IEEE Transactions on Evolutionary Computation, IEEE Transactions on cloud computing, IEEE IoT Journal, IEEE Transactions on Parallel and distributed systems, IEEE Transactions on Systems, Man and Cybernetics; Elsevier’s Applied Soft Computing, MIT Press Evolutionary Computation. He is also a Reviewer & Program Committee Member for IEEE CEC, GECCO, EMO, and other top conferences
Publication details: https://scholar.google.com/citations?user=hUcQKQUAAAAJ&hl=en&oi=ao