Parallel and Distributed Computing Approaches for Evolutionary Algorithms—A Review

The evolutionary computing (EC) domain provides variety of optimization algorithms to the computer science community. The increase of large-scale real-world optimization problems which consists of thousands of decision variable has given rise to different challenges EC algorithms (popularly known as evolutionary algorithms (EAs)). However, these challenges are addressed by the research community of EC domain integrating the parallel and distribute computing paradigms to design novel EA frameworks for solving complex large-scale optimization problems. The parallel and distributed EA frameworks have received global attention over the past two decades. This paper summarizes various commonly followed algorithm models for parallel and distributed EAs. This paper also comprehensively reviews the research attempts in the literature relating these algorithmic models with different EAs and different optimization problems.