Evolutionary Computation in Scheduling

1Evolutionary Computation in Scheduling: A Scientometric Analysis

Amir H. Gandomi¹, Ali Emrouznejad², and Iman Rahimi³

¹ Faculty of Engineering and IT, University of Technology Sydney, Ultimo, Australia

² Aston Business School, Aston University, Birmingham, UK

³ Young Researchers and Elite Club, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

1.1 Introduction

Evolutionary computation (EC) is known as a powerful tool for global optimization‐inspired nature. Technically, EC is also known as a family of population‐based algorithms which could be addressed as metaheuristic or stochastic optimization approaches. The term “stochastic” is used because of the nature of these algorithms, such that a primary set of potential solutions (initial population) is produced and updated, iteratively. Another generation is made by eliminating the less desired solutions stochastically. Increasing the fitness function of the algorithm resulted from evolving the population. A metaheuristic term refers to the fact that these algorithms are defined as higher‐level procedures or heuristics considered to discover, produce, or choose a heuristic which is an adequately good solution for an optimization problem [1, 2]. Applications of metaheuristics can be found in the literature, largely [3–9]. Swarm intelligence algorithms are also a family of EC, based on a population of simple agents which are interacting with each other in an environment. The inspiration for these algorithms often ...