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Int. J. Simul. Multidisci. Des. Optim. 1, 1-8 (2007)
DOI: 10.1051/ijsmdo:2007001
Current trends in evolutionary multi-objective optimization
Kalyanmoy DebDepartment of Mechanical Engineering, Indian Institute of Technology Kanpur, PIN 208016, India
(Received 20 August 2007; accepted 25 September 2007; published online12 December 2007)
Abstract
In a short span of about 14 years,
evolutionary multi-objective optimization (EMO) has established itself
as a mature field
of research and application with an extensive literature,
many commercial softwares, numerous freely downloadable codes,
a dedicated biannual conference running successfully four times so far since
2001, special sessions and workshops held at all major evolutionary
computing conferences, and full-time researchers from universities and
industries from all around the globe. In this paper, we make a
brief outline of EMO principles, some EMO algorithms, and focus on current research and
application potential of EMO. Besides, simply finding a set of
Pareto-optimal solutions, EMO research has now diversified in
hybridizing its search with multi-criterion decision-making tools to
arrive at a single preferred solution, in utilizing EMO principle in
solving different kinds of single-objective optimization problems
efficiently, and in various interesting application domains which were
not possible to be solved adequately due to the lack of a suitable
solution technique.
© ASMDO, EDP Sciences 2007