Anupam Trivedi and Dipti Srinivasan

Department of Electrical & Computer Engineering, National University of Singapore, Singapore

Email: eleatr@nus.edu.sg, dipti@nus.edu.sg

Abstract

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 multi-objective optimization until Zhang and Li proposed multi-objective 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 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 many-objective optimization. This tutorial will present a comprehensive survey of the decomposition-based MOEAs proposed in the last decade for multi-objective and many-objective optimization. Apart from decomposition-based MOEAs, we will also discuss other recent significant works in the field of evolutionary multi-objective and many-objective optimization.

Targeted audience

This tutorial should be of interest to both new beginners and experienced researchers in the area of multi-objective and many-objective optimization. The tutorial will provide a unique opportunity to showcase the latest development on this interesting research topic to the research community. We expect that the tutorial will be of around 110 minutes.

Speaker Bio

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 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.