Generating Epsilon-Efficient Solutions in Multiobjective Optimization by Genetic Algorithm

Abstract

We develop a new evolutionary method of generating epsilon-efficient solutions of a continuous multiobjective programming problem. This is achieved by discretizing the problem and then using a genetic algorithm with some derived probabilistic stopping criteria to obtain all minimal solutions for the discretized problem. We prove that these minimal solutions are the epsilonoptimal solutions to the original problem. We also present some computational examples illustrating the efficiency of our method.

A Soft Sensor Approach Based on an Echo State Network Optimized by Improved Genetic Algorithm

“…In the later stages of the iteration of the standard genetic algorithm, the population is obliged to move closer to the optimal individual. The resulting consequence is necessary to reduce the diversity of the population and increase the risk of the algorithm falling into a locally optimal solution [ 46 ]. As a result, the immigration strategy has been applied, that is, the introduction of new individuals into the evolution process will increase the diversity of the population and thus improve the searchability of the algorithm.…”