Computer
Sanaa Ali Jabber; Soukaena H. Hashem; Shatha H Jafer
Abstract
Finding an optimal solution to some problem, like minimizing andmaximizing the objective function, is the goal of Single-Objective Optimization (SOP).Real-world problems, on the other hand, are more complicated and involve a widerrange of objectives, several objectives should be maximized in such problems. ...
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Finding an optimal solution to some problem, like minimizing andmaximizing the objective function, is the goal of Single-Objective Optimization (SOP).Real-world problems, on the other hand, are more complicated and involve a widerrange of objectives, several objectives should be maximized in such problems. No singlesolution could be enhanced in all objectives without deteriorating at least one othergoal, 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 manyobjectives and find Pareto-optimal solutions, MOEA can resolve multi-objective andsingle-objective optimization problems.This paper aims to introduce a survey study for optimization problem solutions bycomparing techniques, advantages, and disadvantages of SOP and MOP withmetaheuristics and evolutionary algorithms. From this study, we conduct that theefficiency of MOP lies in the present more than one SOP, but it takes a longer time toprocess and train and is not suitable for all applications, While SOP is faster and moreuseful in stock and profit maximization applications. And the posterior techniques areconsidered the dominant approach to solving multi-objective problems by the use of thefield of metaheuristics.
