Development of a multi-objective optimization evolutionary algorithm based on educational systems

Abstract

Multi-objective optimization is an inseparable area of optimization and plays a crucial role in terms of practicality. Almost all multi-objective optimization problems in the real world are suitable as opposed to goals with several ideal models around. Rather than one optimal solution, these issues have a set of optimal solutions known as the Pareto optimal solution. Owing to the lack of proper optimal methodology for finding effective optimal solutions, classical solutions to these problems were changed from multi-objective ones to a single-objective solution. They usually need to perform repetitive applications of an algorithm to find the Pareto optimal solutions. In some cases, such programs cannot even guarantee the Pareto optimal solution. In contrast, the population-oriented approach of Evolutionary Algorithms (EAs) is an effective way to find multiple Pareto optimal solutions in a single program simultaneously. In this research, a multi-objective optimal evolutionary algorithm is represented based on the educational system, which is compared with other multi-objective optimal algorithms.