IEEE Computational Intelligence Magazine - November 2021 - 13

methods can at best find one solution in
a single run, thereby making them inefficient
to
solve MOPs. In contrast,
evolutionary algorithms (EAs) can
approximate multiple optimal solutions.
There have been increasing research
interests in developing EAs or improving
their performance for MOPs. However,
EAs depend largely on the
properties of the MOPs in question, e.g.,
static/dynamic optimization environments,
simple/complex Pareto front
characteristics, and low/high dimensionality.
Therefore, it is not trivial to further
study the generalization of EAs for
wider use.
This thesis explores EAs' ability to
solve a variety of MOPs with different
problem characteristics, attempting to
widen EAs' applicability with greater
performance. First, decomposition-based
EAs are generalized to handle complex
Pareto fronts with a two-phase search and
niche-guided selection strategy. Second,
new scalarizing functions are proposed,
and an efficient decompositionbased
EA is introduced to deal with a
class of hard MOPs. Third, a diversityfirst-and-convergence-second
sorting
method is suggested to handle possible
drawbacks of convergence-first based
sorting methods and shows great promise
for MOPs with an increase of objective
dimensionality. After that, EAs are investigated
for dynamic multiobjective optimization
where objective functions and
constraints can change over time. A new
set of test problems consisting of a wide
range of dynamic characteristics is introduced
to standardize test environments
for dynamic multiobjective optimization,
thereby aiding fair algorithm comparison
and sound performance analysis. Finally, a
dynamic EA is developed to tackle
dynamic MOPs by exploiting the advantages
of both generational and steadystate
algorithms. All
the proposed
approaches have been extensively examined
against existing state-of-the-art
methods, showing robust performance in
a variety of test scenarios.
Recognizing volunteers and eminent colleagues is a
key element to keep our Society alive and to promote
research excellence in computational intelligence.
The research work presented in the
thesis is the output of initiative and
novel attempts to tackle some challenging
issues in evolutionary multiobjective
optimization. This research has not only
increased the applicability of some of
the existing approaches, such as decomposition-based
or Pareto-based algorithms,
for complex or hard MOPs, but
also moved forward dynamic multiobjective
optimization with novel ideas
including new test suites and novel algorithm
design.
Outstanding Early Career Award
Zhi-Hui Zhan,
South China University of Technology,
CHINA
For contributions to the efficiency of swarm
optimizers.
Zhi-Hui Zhan re -
ceived the Bachelor's
degree and the Ph.
D. degree in Computer
Science from
the Sun Yat-Sen University,
Guangzhou,
China, in 2007 and 2013, respectively.
He is currently the Changjiang
Scholar Young Professor with the School
of Computer Science and Engineering,
South China University of Technology,
Guangzhou, China. His current research
interests include evolutionary computation,
swarm intelligence, and their applications
in real-world problems and in
environments of cloud computing and
big data.
Evolutionary computation and
swarm intelligence optimizers are popular
tools for solving optimization problems
in many fields. However, the
performance and applicability of swarm
optimizers are related to the optimization
efficiency that whether they have
strong global search ability to avoid premature
in local optima, and whether
they can converge fast and use acceptable
running time to obtain good
enough solutions for real-world optimization
problems. To these aims, Dr.
Zhan has put his heart and soul in the
field of evolutionary computation and
swarm optimizers and has made significant
outstanding achievements.
Specially, his contributions to the efficiency
of swarm optimizers mainly
include to enhance the optimization
efficiency of swarm optimizers in
terms of " Stronger Search Ability, "
" Faster Convergence Speed, " and
" Shorter Running Time " , so as to
make the algorithms more efficient in
" Harder Problem Solving " and successful
in " Broader Application Fields " .
For his contributions in the related
works, Dr. Zhan received the Outstanding
Youth Science Foundation from
National Natural Science Foundations
of China and the Wu Wen-Jun Artificial
Intelligence Excellent Youth from the
Chinese Association for Artificial Intelligence.
He is appointed as the Changjiang
Scholar Young Professor from
Ministry of Education, China. His doctoral
dissertation was awarded the IEEE
Computational
Intelligence Society
Outstanding Ph. D. Dissertation and the
China Computer Federation Outstanding
Ph. D. Dissertation. He is listed as
one of the Highly Cited Chinese
Researchers in Computer Science. He is
currently an Associate Editor of the
IEEE Transactions on Evolutionary Computation,
the Memetic Computing, and
the Neurocomputing.
NOVEMBER 2021 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 13
IEEE Computational Intelligence Magazine - November 2021

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