Big Bang-Big Crunch Algorithm

Abstract

Inspired by the energy dissipation in the form of transformation from an ordered state to a disordered or chaos state, a novel and simple nature-based or physics-based metaheuristic has been developed by Erol and Eksin [1]. The algorithm named as Big Bang-Big Crunch (BB-BC) is taken from the prevailing evolutionary theory for the origin of universe: the Big Bang Theory. According to this theory, in the Big Bang phase, particles are drawn toward irregularity by losing energy, while in the Big Crunch phase, they converged toward a specific direction. Like other population-based metaheuristics, BB-BC starts with a set of random initial candidate solutions, as the initial Big Bang. In fact, each Big Bang phase is preceded with a Big Crunch phase except the first population which should be generated randomly within the search space. After each Big Bang phase, a Big Crunch phase should take place to determine a convergence operator by which particles will be drawn into an orderly fashion in the subsequent Big Bang phase. The convergence operator can be the weighted average of the positions of the candidate solutions or the position of the best candidate solution. These two contraction (Big Crunch) and dispersing (Big Bang) phases are repeated in the cyclic body of the algorithm in succession to satisfy a stopping criteria with the aim of steering the particles toward the global optimum.