OPPOSITION-BASED ELECTROMAGNETISM-LIKE FOR GLOBAL OPTIMIZATION
Erik Cuevas, Diego A. Oliva, Daniel Zaldívar, Marco Pérez‐Cisneros, Gonzalo Pájares · 2012
Electromagnetism -like Optimization (EMO) is a global optimization algo- rithm, particularly well-suited to solve problems featuring non-linear and multimodal cost functions. EMO employs searcher agents that emulate a population of charged particles which interact with each other according to electromagnetism 's 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 Learn- ing (OBL) approach to accelerate the global convergence speed. OBL is a machine intel- ligence 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 signicantly 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 promis- ing performance for most of the discussed test problems.