Evolutionary Computation in Scheduling

7Application of Sub‐Population Scheduling Algorithm in Multi‐Population Evolutionary Dynamic Optimization

Javidan Kazemi Kordestani¹ and Mohammad Reza Meybodi²

¹ Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

² Soft Computing Laboratory, Computer Engineering and Information Technology Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

7.1 Introduction

Many problems in real‐world applications involve optimizing a set of parameters, in which the objectives of the optimization, some constraints, or other elements of the problems may vary over time. If so, the optimal solution(s) to the problems may change as well. Generally speaking, various forms of dynamic behavior are observed in a substantial part of real‐world optimization problems in different domains. Examples of such problems include the dynamic resource allocation in shared hosting platforms [1], dynamic traveling salesman problem that changes traffic over time [2], dynamic shortest path routing in MANETs [3], aerospace design [4], pollution control [5], ship navigation at sea [5], dynamic vehicle routing in transportation logistics [6], autonomous robot path planning [7], optimal power flow problem [8], dynamic load balancing [9], and groundwater contaminant source identification [10].

Evolutionary computation (EC) techniques have attracted a great deal of attention due to their potential for solving complex optimization problems. ...