A state-of-the-art review of population-based parallel meta-heuristics

Mathematical models of many real life optimization problems turn out to be so complex that traditional optimization techniques such as gradient based methods and other deterministic techniques etc are not applicable for them. A new class of optimization techniques called population-based meta-heuristics (PBM) is applied for the solution of these problems. Particle Swarm Optimization algorithm (PSO) and Genetic Algorithm (GA) are two of the most popular algorithms in this category and have been extensively used in recent years for the solution of this kind of optimization problems. But for these techniques, computational cost (measured by elapsed time) is too high. Fortunately, these techniques have inherent parallelism in them. This inspires many researchers to implement them on the latest parallel computers. This review paper tries to present a state-of-the-art in parallel genetic algorithms and parallel particle swarm optimization.