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BIG BANG - BIG CRUNCH |
A New Optimization
Method
Osman K. EROL, Ibrahim EKSIN |
Abstract:
Nature is the principal source
for proposing new optimization methods such as genetic algorithms (GA) and
simulated annealing (SA) methods. All traditional evolutionary algorithms
are heuristic population-based search procedures that incorporate random
variation and selection. The main contribution of this study is that it
proposes a novel optimization method that relies on one of the theories of
the evolution of the universe; namely, the Big Bang and Big Crunch Theory.
In the Big Bang phase, energy dissipation produces disorder and randomness
is the main feature of this phase; whereas, in the Big Crunch phase,
randomly distributed particles are drawn into an order. Inspired by this
theory, an optimization algorithm is constructed, which will be called the
Big Bang–Big Crunch (BB–BC) method that generates random points in the Big
Bang phase and shrinks those points to a single representative point via a
center of mass or minimal cost approach in the Big Crunch phase. It is shown
that the performance of the new (BB–BC) method demonstrates superiority over
an improved and enhanced genetic search algorithm also developed by the
authors of this study, and outperforms the classical genetic algorithm (GA)
for many benchmark test functions. |
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