The Power of Oppositeness in Optimization

We are witness of the rapidly growing
application of the Evolutionary Algorithms to solve
real-world nonlinear complex problems. The
computational time of these algorithms directly
depends on the dimensionality and complexity of the
problem. Although, compared to traditional methods,
these algorithms are more applicable to real-world
problems, they suffer from low convergence speed. So
far, many attempts have been conducted to make them
as fast as possible. In the current study, a new
direction has been disclosed for this purpose,
namely, employing the power of oppositeness to
accelerate evolutionary algorithms. For the first
time, the concept of opposition has been utilized in
optimization. The opposition-based optimization is
simple to implement and apply in many different ways
for various purposes. This study is a
starting point in this direction to confirm the
potentials, and to motivate other researchers in
optimization and machine learning fields to engage
with the opposition concepts. This book is especially
useful for researchers and professionals in soft
computing, machine learning, or optimization.

Autorentext

PhD: University of Waterloo, Canada, 2007. Research Interests: Evolutionary Computation, Machine Intelligence, and Image Processing. Assistant Professor at the faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), Canada.

Klappentext

We are witness of the rapidly growing application of the Evolutionary Algorithms to solve real-world nonlinear complex problems. The computational time of these algorithms directly depends on the dimensionality and complexity of the problem. Although, compared to traditional methods, these algorithms are more applicable to real-world problems, they suffer from low convergence speed. So far, many attempts have been conducted to make them as fast as possible. In the current study, a new direction has been disclosed for this purpose,namely, employing the power of oppositeness toaccelerate evolutionary algorithms. For the firsttime, the concept of opposition has been utilized in optimization. The opposition-based optimization is simple to implement and apply in many different waysfor various purposes. This study is astarting point in this direction to confirm thepotentials, and to motivate other researchers inoptimization and machine learning fields to engagewith the opposition concepts. This book is especiallyuseful for researchers and professionals in softcomputing, machine learning, or optimization.