AUSTRALASIAN CONFERENCE ON ARTIFICIAL LIFE AND COMPUTATIONAL INTELLIGENCE (ACALCI 2017)
31 January-2 February 2017, Melbourne, Australia
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Tutorial: Advances in Multi-objective Evolutionary Algorithms based on Decomposition

In the last decade, the framework which has attracted the most attention of researchers in the evolutionary multi-objective optimization community is the decomposition-based framework. Decomposition is a well-known strategy in traditional multi-objective optimization. However, the decomposition strategy was not widely employed in evolutionary multiobjective optimization until Zhang and Li proposed multiobjective evolutionary algorithm based on decomposition (MOEA/D) in 2007. MOEA/D proposed by Zhang and Li decomposes a multi-objective optimization problem into a number of scalar optimization subproblems, and optimizes them in a collaborative manner using an evolutionary algorithm. Each subproblem is optimized by utilizing the information mainly from its several neighbouring subproblems. Since the proposition of MOEA/D in 2007, several studies have been conducted in the literature to: a) overcome the limitations in the design components of the original MOEA/D, b) improve the performance of MOEA/D, c) present novel decomposition-based MOEAs, and d) adapt decomposition-based MOEAs for different type of problems.

Investigations on the decomposition-based framework have been undertaken in several directions, including development of novel weight vector generation methods, use of new decomposition approaches, efficient allocation of computational resources, modifications in the reproduction operators, mating selection and replacement mechanism, hybridizing decomposition- and dominance-based approaches, etc. Furthermore, several attempts have been made at extending the decomposition-based framework to constrained multi-objective optimization, many-objective optimization, and incorporate the preference of decision makers. This tutorial will present a comprehensive survey of the decomposition-based MOEAs proposed in the last decade. We will highlight the strengths and weakness of the different decomposition-based MOEAs presented in the literature and present interesting directions for future research. Finally, we will present our newly designed MOEA/D variant to incorporate the a priori preferences of the decision maker, namely pMOEA/D. We will present experimental results on benchmark functions from different test suites such as ZDT, DTLZ, and UF, to demonstrate the effectiveness of the proposed algorithm.


Targeted audience

This tutorial should be of interest to both new beginners and experienced researchers in the area of multiobjective optimization. The tutorial will provide a unique opportunity to showcase the latest development on this hot research topic to the EC research community. We expect the tutorial will last about 110 minutes.


Speakers

Anupam Trivedi, National University of Singapore
Anupam Trivedi received his received the Dual degree (integrated Bachelor’s and Master’s) in Civil Engineering from the Indian Institute of Technology (IIT) Bombay, Mumbai, India, in 2009, and the Ph.D. degree in Electrical & Computer engineering from the National University of Singapore, Singapore, in 2015. Currently, he is a Research Fellow at the Department of Electrical & Computer Engineering, National University of Singapore, Singapore. His research interests include evolutionary computation, multiobjective optimization, and power systems.
Dipti Srinivasan, National University of Singapore
Dipti Srinivasan received the Ph.D. degree in engineering from the National University of Singapore, Singapore, in 1994. She worked as a Postdoctoral Researcher with the University of California, Berkeley, CA, USA, from 1994 to 1995, before joining the National University of Singapore, where she is currently an Associate Professor with the Department of Electrical and Computer Engineering. Her research interests include evolutionary computation, neural networks, multiobjective optimization, and power systems. She is currently serving as an Associate Editor of IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Networks and Learning Systems, IEEE Computational Intelligence magazine, IEEE Transactions on Intelligent Transportation Systems, and IEEE Transactions on Sustainable Energy.


Important Dates
Paper submission deadline: 10 September 2016
Decision notification: 17 October 2016
Camera ready submission: 7 November 2016
Conference dates: 31 January-2 February 2017
Tutorials: 3 February 2017 (at RMIT University)
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