Computational Intelligence - May 2016 - 11

Hagras, and G. Aldabbagh, IEEE
Transactions on Fuzzy Systems, Vol. 23,
No. 6, Dec. 2015, pp. 1984-1997.
Digital Object Identifier: 10.1109/
TFUZZ.2015.2394781
"This paper presents a fuzzy logicbased system, which can be cheaply retrofitted in existing electric cookers to
convert them to semiautonomous,
energy efficient, and safe smart electric
cookers. The proposed system can control the cooker heating plate to allow
the semiautonomous safe operation of
the most common cooking techniques
including boiling, stir/shallow-frying,
deep-frying, and warming. The developed system can identify when human
intervention is necessary and when
dangerous situations happen or are
imminent. In addition, the realized
smart cooker has shown unique safety
features not present in the existing
commercial cookers."
Generalizations of OWA Operators, by
R. Mesiar, A. Stupnanova, and R.R.
Yager, IEEE Transactions on Fuzzy
Systems, Vol. 23, No. 6, Dec. 2015,
pp. 2154-2162.
Digital Object Identifier: 10.1109/
TFUZZ.2015.2406888
"OWA operators can be seen as symmetrized weighted arithmetic means, as
Choquet integrals with respect to symmetric measures, or as comonotone additive functionals.The authors discuss several
already known generalizations of OWA
operators, including GOWA, IOWA,
OMA operators, as well as they propose
new types of such generalizations."
IEEE Transactions on
Evolutionary Computation

A Knee Point-Driven Evolutionary
Algorithm for Many-Objective Optimization, by X. Zhang, Y. Tian, and Y.
Jin, IEEE Transactions on Evolutionary
Computation, Vol. 19, No. 6, December 2015, pp. 761-776.
Digital Object Identifier: 10.1109/
TEVC.2014.2378512

"Evolutionary algorithms (EAs) have
shown to be promising in solving manyobjective optimization problems
(MaOPs), where the performance of
these algorithms heavily depends on
whether solutions that can accelerate
convergence toward the Pareto front and
maintaining a high degree of diversity
will be selected from a set of nondominated solutions. In this paper, a knee
point-driven EA is proposed to solve
MaOPs. The basic idea is that knee
points are naturally most preferred
among nondominated solutions if no
explicit user preferences are given. A bias
toward the knee points in the nondominated solutions in the current population
is shown to be an approximation of a
bias toward a large hypervolume, thereby
enhancing the convergence performance
in many-objective optimization. In addition, as at most one solution will be
identified as a knee point inside the
neighborhood of each solution in the
nondominated front, no additional diversity maintenance mechanisms need to be
introduced in the proposed algorithm,
considerably reducing the computational
complexity compared to many existing
multiobjective EAs for many-objective
optimization. Experimental results on 16
test problems demonstrate the competitiveness of the proposed algorithm in
terms of both solution quality and computational efficiency."
A Hybrid Swarm-Based Approach to
University Timetabling, by C. W. Fong,
H. Asmuni, and B. McCollum, IEEE
Transactions on Evolutionary Computation, Vol. 19, No. 6, December 2015,
pp. 870-884.
Digital Object Identifier: 10.1109/
TEVC.2015.2411741
"This paper is concerned with the
application of an automated hybrid
approach in addressing the university
timetabling problem. The approach
described is based on the natureinspired artificial bee colony (ABC)
algorithm. An ABC algorithm is a biologically-inspired optimization approach,
which has been widely implemented in
solving a range of optimization prob-

lems in recent years such as job shop
scheduling and machine timetabling
problems. Although the approach has
proven to be robust across a range of
problems, it is acknowledged within the
literature that there currently exist a
number of inefficiencies regarding the
exploration and exploitation abilities.
These inefficiencies can often lead to a
slow convergence speed within the
search process. Hence, this paper introduces a variant of the algorithm which
utilizes a global best model inspired
from particle swarm optimization to
enhance the global exploration ability
while hybridizing with the great deluge
(GD) algorithm in order to improve the
local exploitation ability. Using this
approach, an effective balance between
exploration and exploitation is attained.
In addition, a traditional local search
approach is incorporated within the GD
algorithm with the aim of further
enhancing the performance of the overall hybrid method. To evaluate the performance of the proposed approach, two
diverse university timetabling datasets
are investigated, i.e., Carter's examination timetabling and Socha course timetabling datasets. It should be noted that
both problems have differing complexity and different solution landscapes.
Experimental results demonstrate that
the proposed method is capable of producing high quality solutions across
both these benchmark problems, showing a good degree of generality in the
approach. Moreover, the proposed
method produces the best results on
some instances as compared with other
approaches presented in the literature."
IEEE Transactions on
Computational Intelligence
and AI in Games

A Panorama of Artificial and Computational Intelligence in Games, by G. N.
Yannakakis and J. Togelius, IEEE
Transactions on Computational Intelligence and AI in Games, Vol. 7, No. 4,
December 2015, pp. 317-335.
Digital Object Identifier: 10.1109/
TCIAIG.2014.2339221

may 2016 | IEEE ComputatIonal IntEllIgEnCE magazInE

Table of Contents for the Digital Edition of Computational Intelligence - May 2016