Opposition Based ElectromagnetismLike for Global Optimization
  Electromagnetismlike Optimization (EMO) is a global optimization algorithm,
particularly well suited to solve problems featuring nonlinear and multimodal
cost functions. EMO employs searcher agents that emulate a population of
charged particles which interact to each other according to electromagnetisms
laws of attraction and repulsion. However, EMO usually requires a large number
of iterations for a local search procedure; any reduction or cancelling over
such number, critically perturb other issues such as convergence, exploration,
population diversity and accuracy. This paper presents an enhanced EMO
algorithm called OBEMO, which employs the Opposition-Based Learning (OBL)
approach to accelerate the global convergence speed. OBL is a machine
intelligence strategy which considers the current candidate solution and its
opposite value at the same time, achieving a faster exploration of the search
space. The proposed OBEMO method significantly reduces the required
computational effort yet avoiding any detriment to the good search capabilities
of the original EMO algorithm. Experiments are conducted over a comprehensive
set of benchmark functions, showing that OBEMO obtains promising performance
for most of the discussed test problems.
