IEEE Computational Intelligence Magazine - November 2021 - 17
Liang Feng
Guest
Chongqing University, CHINA
Handing Wang
Xidian University, CHINA
Yew-Soon Ong
Nanyang Technological University,
SINGAPORE
Kay Chen Tan
Hong Kong Polytechnic University,
HONG KONG
Yaochu Jin
University of Surrey,
UK
Editorial
E
volutionary Algorithms (EAs) are
nature-inspired population-based
search methods which work on
Darwinian principles of natural selection.
Due to their strong search capability
and simplicity of implementation,
EAs have been successfully applied to
solve many complex optimization problems,
which cannot be easily solved by
traditional mathematical programming
approaches, such as linear programming,
quadratic programming, and convex
optimization. Despite the great success
enjoyed by EAs, it is worth noting that
existing EA solvers usually conduct the
search process from scratch, regardless of
how similar the new problem encountered
is to those already solved in the
past. Therefore, conventional EAs do not
learn from previous problems and the
search capabilities of the EA solvers do
not automatically grow with problemsolving
experiences. However, in reality,
since problems seldom exist in isolation,
solving one problem may thus yield useful
information for solving other related
problems. In the literature, there is a
growing interest in conducting research
on evolutionary transfer optimization
(ETO) in recent years: a paradigm that
integrates EA solvers with knowledge
learning and transfer across related
domains to achieve better optimization
efficiency and performance.
Digital Object Identifier 10.1109/MCI.2021.3108301
Date of current version: 13 October 2021
Evolutionary transfer optimization (ETO) integrates EA solvers
with knowledge learning and transfer across related domains
to achieve better optimization efficiency and performance.
This special issue has brought together
researchers to report state-of-the-art
contributions on the latest research and
development, up-to-date issues, challenges,
and applications in the field of
ETO. Following a rigorous peer review
process, three papers have been accepted
for publication in this special issue.
The first paper included in the
special issue is entitled " Towards Generalized
Resource Allocation on Evolutionary
Multitasking for Multi-Objective Optimization "
authored by T. Wei et al.,
which aims to accelerate the convergence
of multitasking multi-objective
optimization. This article proposes a
generalized resource allocation framework
by concerning both theoretical
grounds of conventional resource allocation
and characteristics of multi-objective
optimization. In the proposed
framework, a normalized attainment
function is designed for better quantifying
convergence status, multi-step nonlinear
regression is proposed to serve as a
stable performance estimator, and the
algorithm procedure of conventional
resource allocation is refined for flexibly
adjusting resource allocation intensity
and involving knowledge transfer information.
The experimental results demonstrated
that the proposed framework
can enhance the overall performance of
multi-objective evolutionary multitasking
optimization algorithms.
The second paper, titled " Improving
Evolutionary Multitasking Optimization
by Leveraging Inter-task Gene Similarity
and Mirror Transform " by X. Ma
et al., proposes integrating an inter-task
gene-similarity-based knowledge transfer
and an intra-task mirror transform into
Multifactorial evolutionary algorithm
(MFEA) to address the negative knowledge
transfer. In the proposed intertask
gene-similarity-based knowledge transfer,
a probabilistic model is used to feature
each gene and the Kullback-Leibler
divergence is employed to measure the
inter-task dimension similarity. A selective
crossover is guided by the inter-task
gene similarity for reproduction. The
proposed inter-task knowledge transfer is
based on online gene similarity evaluation,
instead of individual similarity, to
overcome the imprecise estimation issue
of population distribution in a highdimensional
space with a small number
of samples. The proposed intra-task mirror
transform is an extension of opposition-based
learning to avoid premature
convergence and explore more promising
search areas. Experimental results on
both single-objective and multiobjective
NOVEMBER 2021 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 17
IEEE Computational Intelligence Magazine - November 2021
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