Computer
sanaa ali jabber; Soukaena h. Hashiem; Shatha H Jafer
Articles in Press, Accepted Manuscript, Available Online from 28 May 2023
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 ...
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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 Multi-Objective 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.
