HMDC 2019: Recent Advances in Hybrid Metaheuristics for Data Clustering |
Website | http://hmdc.teamsb.net/ |
Submission link | https://easychair.org/conferences/?conf=hmdc2019 |
Proposal Submission: | October 31, 2018 |
Notification of Acceptance of Proposals: | November 10, 2018 |
Full Chapter Submission: | December 15, 2018 |
Chapter Review Notification: | January 15, 2019 |
Interim Version Due: | February 10, 2019 |
Final Notification: | February 28, 2019 |
Final Revised Chapter Due: | March 20, 2019 |
Final Acceptance: | April 10, 2019 |
Metaheuristic algorithms have been proven to be efficient way to handle and solve different types of data clustering problems. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. Basically, the solving procedure of a subordinate heuristic problem by an iterative generation procedure is known as metaheuristic. This is done by syndicating intelligently different concepts to explore and exploit the search space and the non-optimal solutions are derived efficiently by the learning strategies which are applied on the structure information of the problem. The main objective of metaheuristic is to derive a set of optimal solutions which is large enough to be completely sampled. Different types of real world problems can be handled by the metaheuristic techniques due to the fact that any conventional algorithm can’t manage many real world problems, in spite of the raising computational power, simply due to unrealistically large running times. To solve the optimization problems, these algorithms make few assumptions at the initial stages. It is not assured that the metaheuristic algorithms may generate globally optimal solutions to solve all types of problems since most of the implementations are some form of stochastic optimization and the resultant solutions may dependents on the set of generated random variables. To solve optimization algorithms, heuristics or iterative methods, metaheuristic algorithms are the better options as they often determine good solutions with lesser computational effort by exploring a large set of feasible solutions. Some well-known meta-heuristic algorithms are Genetic algorithm (GA), simulated annealing (SA), Tabu search (TS) and different types of swarm intelligence algorithms. Some recognized swarm intelligence algorithms are particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony optimization (ABC), differential optimization (DE) and cuckoo search algorithm, etc. In the recent advancement of the research arena, some modern swarm intelligence based optimization algorithms like, Egyptian Vulture Optimization Algorithm, Rats herds Algorithm (RATHA), Bat algorithm, Crow search algorithm, Glowworm Swarm Optimization (GSO), etc. are performing well to solve some real-life based problems. These algorithms are also working efficiently to cluster different types of real-life dataset. During clustering of the data, sometimes it has been observed that the methaheuristic algorithms suffer from time complexity though they can afford optimum solutions. To get rid of this types of problems and not depending on a particular type of metaheuristic algorithms to solve complex problems, researchers and scientists blended not only different meta-heuristic approaches but they also hybridized different metaheuristic algorithms with other soft computing tools and techniques, like, neural network, fuzzy set, rough set, etc. The hybrid metaheuristic algorithms, combination of metaheuristic algorithms and other techniques, are more effective to handle the real-life data clustering problems. Recently, quantum mechanical principles are also applied to cut down the time complexity of the meta-heuristic approaches to a great extent.
Submission Procedure
All papers must be original and not simultaneously submitted to another journal or conference.
- Prospective authors are invited to submit a 3- 4 pages Abstract of the paper along with title of the paper and author details. Abstract should highlight the novelty and contribution of the proposed article.
- Authors need to submit this abstract using the Easy Chair submission link Easy Chair link for Submission
List of Topics
- Introduction and background to the subject area
- Different Metaheuristic algorithms and their advancement (Genetic algorithm (GA), simulated annealing (SA), Tabu search (TS) and different types of swarm intelligence algorithms. Some recognized swarm intelligence algorithms are particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony optimization (ABC), differential optimization (DE) and cuckoo search algorithm, etc.)
- Marriage between Metaheuristic algorithms and Soft Computing Techniques
- Applications (medical domains, agriculture, object recognition, automatic speech recognition, Image recognition, portfolio management, Bioinformatics, Big data analytics, Social networks etc.)
- Contributors are required to submit coding examples, real life case studies and video demonstrations on their contributions
Contact
All questions about submissions should be emailed to
Prof. (Dr.) Siddhartha Bhattacharyya
RCC Institute of Information Technology
Canal South Road, Beliaghata, Kolkata – 700 015, India
Mobile: +919830354195
Email: dr.siddhartha.bhattacharyya@ieee.org