Document Type : Review Paper

Authors

• Sanaa Ali Jabber ¹
• Soukaena H. Hashem ²
• Shatha H Jafer ²

¹ Faculty of Administration and Economics, AL-Muthanna University, Iraq

² Department of Computer Science, University of Technology, Baghdad, Iraq

Abstract

Finding an optimal solution to some problem, like minimizing and
maximizing the objective function, is the goal of Single-Objective Optimization (SOP).
Real-world problems, on the other hand, are more complicated and involve a wider
range of objectives, several objectives should be maximized in such problems. No single
solution could be enhanced in all objectives without deteriorating at least one other
goal, which is the definition of Pareto-optimality. Understanding the idea of MultiObjective Optimization (MOP) is thus necessary to find the optimum solution. Multiobjective evolutionary algorithm (MOEA) are made to simultaneously assess many
objectives and find Pareto-optimal solutions, MOEA can resolve multi-objective and
single-objective optimization problems.
This paper aims to introduce a survey study for optimization problem solutions by
comparing techniques, advantages, and disadvantages of SOP and MOP with
metaheuristics and evolutionary algorithms. From this study, we conduct that the
efficiency of MOP lies in the present more than one SOP, but it takes a longer time to
process and train and is not suitable for all applications, While SOP is faster and more
useful in stock and profit maximization applications. And the posterior techniques are
considered the dominant approach to solving multi-objective problems by the use of the
field of metaheuristics. 

Keywords

• Multi-Objective Evolutionary Algorithm
• Multi-Objective Optimization
• Optimization problem
• Objective Function
• Single-Objective Optimization

Main Subjects

• Computer