Abstract
The concept of opposition can contribute to improve the performance of population-based algorithms. This chapter presents an overview of a novel opposition-based scheme to accelerate an evolutionary algorithm, differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based computation (OBC) for population initialization and also for generation jumping. Opposite numbers, representing anti-chromosomes, have been utilized to improve the convergence rate of the classical DE. A test suite with 15 well-known benchmark functions is employed for experimental verification. Descriptions for the DE and ODE algorithms, and a comparison strategy are provided. Results are promising and confirm that the ODE outperforms its parent algorithm DE. This work can be regarded as an initial study to exploit oppositional concepts to expedite the optimization process for any population-based approach.
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